From b8b5082cde393dfa11ff3765973e086cc4d31cf6 Mon Sep 17 00:00:00 2001 From: artiz Date: Sun, 28 Jun 2026 11:44:08 +0200 Subject: [PATCH 01/42] fix(pdf): de-hyphenate wrapped words and normalize text in PDF assembly Multi-column PDFs came out with mid-word splits and a stray control char. pdfium emits the soft line-wrap hyphen as the U+0002 control char, and region_text joined line cells with a space and left that char in place, so a 2-column paragraph rendered as "a concise and com[U+0002] pact repre[U+0002] sentation" instead of "a concise and compact representation". This was the dominant readability gap vs docling. region_text (via the new clean_text helper) now: - undoes soft-hyphen line wraps, dropping the U+0002/U+00AD control chars and merging the word -- matching docling ("compact", "end-toend"); - maps curly quotes and the ellipsis to ASCII, as docling does. The PDF title is rendered as "##" (docling never emits a top-level "#" for PDFs: 0 level-1 vs 119 level-2 headings across the groundtruth corpus). Token spacing is otherwise left as a plain single-space join. docling's per-glyph spacing comes from the PDF text stream (it keeps "{ ahn }", "Name 1 .", "[ 9 ]"); a geometric gap heuristic and punctuation-tightening were prototyped but diverged from docling more than the plain join, and a centre-band sort that fixed citation ordering reordered prose elsewhere (amt_handbook, RTL) -- so all were dropped after measuring. Regenerated the PDF snapshot baseline (12 text fixtures: cleaner words, ASCII quotes, "##" titles; pdf_conformance still 76/76 exact). 2203.01017v2.pdf vs docling drops from 436 to 366 diff-lines -- the abstract and most prose now match docling exactly. The residual gap is the geometric tables (no TableFormer) and citation/footnote spacing that needs the PDF text stream (documented in MIGRATION.md). Co-Authored-By: Claude Opus 4.8 (1M context) --- MIGRATION.md | 7 + crates/fleischwolf-pdf/src/assemble.rs | 65 +++++- .../latex/sources/2305.03393/llncsdoc.pdf.md | 24 +- .../pdf/sources/2203.01017v2.pdf.md | 220 +++++++++--------- .../pdf/sources/2206.01062.pdf.md | 114 ++++----- .../pdf/sources/2305.03393v1-pg9.pdf.md | 2 +- .../pdf/sources/2305.03393v1.pdf.md | 76 +++--- .../pdf/sources/code_and_formula.pdf.md | 4 +- .../pdf/sources/multi_page.pdf.md | 6 +- .../pdf/sources/normal_4pages.pdf.md | 36 +-- .../pdf/sources/picture_classification.pdf.md | 2 +- .../pdf/sources/redp5110_sampled.pdf.md | 52 ++--- .../table_mislabeled_as_picture.pdf.md | 14 +- .../tiff/sources/2206.01062.tif.md | 2 +- 14 files changed, 338 insertions(+), 286 deletions(-) diff --git a/MIGRATION.md b/MIGRATION.md index e5e27f85..83c959bc 100644 --- a/MIGRATION.md +++ b/MIGRATION.md @@ -150,6 +150,13 @@ These are deliberate or unavoidable divergences, not bugs. - **Tables** — *geometric* grid reconstruction (cluster cells into rows/cols), **not TableFormer** (docling's autoregressive table-structure model). Table structure is approximate; complex spans are not recovered. + - **Text assembly** — line cells are joined in reading order; soft-hyphen line + wraps (pdfium emits the wrap hyphen as the U+0002 control char) are undone so + `com-`/`pact` rejoins as `compact`, and curly quotes/ellipsis are mapped to + ASCII — both matching docling. Token spacing is a plain single-space join: + docling's per-glyph spacing comes from the PDF *text stream*, which the + segment-based path can't reproduce, so citation/footnote spacing (e.g. + `[ 37 , 36 ]` vs `[37, 36]`) can still differ. - Output is therefore a **snapshot baseline**, never byte-for-byte with docling. 6. **Extracted image bytes are real but not byte-identical.** Cropped/embedded diff --git a/crates/fleischwolf-pdf/src/assemble.rs b/crates/fleischwolf-pdf/src/assemble.rs index 516f9482..3bc974c5 100644 --- a/crates/fleischwolf-pdf/src/assemble.rs +++ b/crates/fleischwolf-pdf/src/assemble.rs @@ -82,6 +82,29 @@ fn order_regions(regions: &mut [Region], page_w: f32) { } } +/// Clean a region's assembled text: undo soft-hyphen line wraps, map curly +/// quotes and the ellipsis to ASCII (matching docling), and collapse runs of +/// whitespace. pdfium emits the line-wrap hyphen as U+0002 in this corpus +/// (U+00AD elsewhere), so `word\u{2} continuation` is one hyphenated word — +/// drop the hyphen + the joining space and merge (`com\u{2} pact` → `compact`, +/// `end-to\u{2} end` → `end-toend`), exactly as docling does. +/// +/// Token spacing is otherwise left as the geometric join produced it. We do not +/// tighten punctuation spacing: docling preserves the PDF's own spaces (it keeps +/// `{ ahn }`, `Name 1 .`, `[ 9 ]`), and a geometric gap heuristic diverges from +/// it more than a plain single-space join does. +fn clean_text(text: &str) -> String { + text.replace("\u{2} ", "") + .replace("\u{ad} ", "") + .replace(['\u{2}', '\u{ad}'], "") // any stray wrap hyphens not at a join + .replace(['\u{2018}', '\u{2019}'], "'") // ‘ ’ → ' + .replace(['\u{201c}', '\u{201d}'], "\"") // “ ” → " + .replace('\u{2026}', "...") // … → ... + .split_whitespace() + .collect::>() + .join(" ") +} + /// Cells assigned to a region (best container), in reading order, joined. fn region_text(region: &Region, cells: &[TextCell]) -> String { let mut inside: Vec<&TextCell> = cells @@ -100,14 +123,12 @@ fn region_text(region: &Region, cells: &[TextCell]) -> String { .fold(0.0f32, f32::max) .max(1.0); inside.sort_by_key(|c| ((c.t / band).round() as i64, (c.l * 10.0) as i64)); - inside + let joined = inside .iter() .map(|c| c.text.trim()) .collect::>() - .join(" ") - .split_whitespace() - .collect::>() - .join(" ") + .join(" "); + clean_text(&joined) } /// Reconstruct a table's grid geometrically from the text cells inside its @@ -171,12 +192,13 @@ fn reconstruct_table(region: &Region, cells: &[TextCell]) -> Vec> { let mut cols = vec![String::new(); ncols]; for c in row { let ci = col_of(c.l); - let t = c.text.trim(); + // Strip the wrap-hyphen control char so it never lands in a cell. + let t = c.text.trim().replace(['\u{2}', '\u{ad}'], ""); if cols[ci].is_empty() { - cols[ci] = t.to_string(); + cols[ci] = t; } else { cols[ci].push(' '); - cols[ci].push_str(t); + cols[ci].push_str(&t); } } grid.push(cols); @@ -234,8 +256,9 @@ pub fn assemble_page(page: &PdfPage, mut regions: Vec, doc: &mut Docling continue; } match region.label { - "title" => doc.push(Node::Heading { level: 1, text }), - "section_header" => doc.push(Node::Heading { level: 2, text }), + // docling renders both the document title and section headers as + // `##` (it never emits a top-level `#` for PDFs), so match that. + "title" | "section_header" => doc.push(Node::Heading { level: 2, text }), "list_item" => doc.push(Node::ListItem { ordered: false, number: 0, @@ -259,3 +282,25 @@ pub fn assemble_page(page: &PdfPage, mut regions: Vec, doc: &mut Docling } } } + +#[cfg(test)] +mod tests { + use super::clean_text; + + #[test] + fn clean_text_dehyphenates_and_normalizes_typography() { + // U+0002 line-wrap hyphen + the join space → merged word (like docling). + assert_eq!(clean_text("com\u{2} pact"), "compact"); + assert_eq!(clean_text("end-to\u{2} end deep"), "end-toend deep"); + // A stray wrap hyphen (no following join) is dropped. + assert_eq!(clean_text("word\u{2}"), "word"); + // Typographic punctuation → ASCII. + assert_eq!( + clean_text("Graph\u{2019}s \u{201c}x\u{201d}"), + "Graph's \"x\"" + ); + assert_eq!(clean_text("a\u{2026}"), "a..."); + // Whitespace collapses; a normal space-join is preserved. + assert_eq!(clean_text("a b\nc"), "a b c"); + } +} diff --git a/tests/pdf_snapshots/latex/sources/2305.03393/llncsdoc.pdf.md b/tests/pdf_snapshots/latex/sources/2305.03393/llncsdoc.pdf.md index a05dbe5c..d74a7158 100644 --- a/tests/pdf_snapshots/latex/sources/2305.03393/llncsdoc.pdf.md +++ b/tests/pdf_snapshots/latex/sources/2305.03393/llncsdoc.pdf.md @@ -1,4 +1,4 @@ -## Instructions for Using Springer’s llncs Class for Computer Science Proceedings Papers +## Instructions for Using Springer's llncs Class for Computer Science Proceedings Papers , Version 2.22, Sep 05, 2022 llncs @@ -14,7 +14,7 @@ The llncs class is an extension of the standard LA LAT ATE TEX EX article class. If you are already familiar with LA LAT ATE TEX EX, the llncs class should not give you any major difficulties. It basically adjusts the layout to the required standard, defining styles and spacing of headings and captions and setting the printing area to 122mm horizontally by 193mm vertically. To keep the layout consistent, we kindly ask you to refrain from using any LA LAT ATE TEX EX or TE TEX EX command that modifies these settings (i.e. \textheight , \vspace , baselinestretch ,etc.). Such manual layout adjustments should be lim ited to very exceptional cases. -In addition to defining the general layout, the llncs document class pro vides some special commands for typesetting the contribution header, i.e. title, authors, affiliations, abstract, and additional metadata. These special commands are described in Sect. 3 . +In addition to defining the general layout, the llncs document class provides some special commands for typesetting the contribution header, i.e. title, authors, affiliations, abstract, and additional metadata. These special commands are described in Sect. 3 . For a more detailed description of how to prepare your text, illustrations, and references, see the Springer Guidelines for Authors of Proceedings . @@ -86,9 +86,9 @@ If you have done this correctly, the author line now reads, for example: \author{First Author\inst{1}\orcidID{0000-1111-2222-3333} \and Second Author\inst{2,3}\orcidID{1111-2222-3333-4444}} -The given name(s) should always be followed by the family name(s). Au thors who have more than one family name should indicate which part of their name represents the family name(s), for example by non-breaking spaces Jos\’{e} Martinez~Perez or curly braces Jos\’{e} {Martinez Perez} . +The given name(s) should always be followed by the family name(s). Authors who have more than one family name should indicate which part of their name represents the family name(s), for example by non-breaking spaces Jos\'{e} Martinez~Perez or curly braces Jos\'{e} {Martinez Perez} . -As given name(s) are to be shortened to initials in the running heads, speci fying an abbreviated author list with the optional command: +As given name(s) are to be shortened to initials in the running heads, specifying an abbreviated author list with the optional command: \authorrunning @@ -100,7 +100,7 @@ might add some clarity about the correct representation of author names, in the \institute Addresses of institutes, companies, etc. should be given in \institute . -Multiple affiliations are separated by \and , which automatically assures cor rect numbering: +Multiple affiliations are separated by \and , which automatically assures correct numbering: \and @@ -116,7 +116,7 @@ Multiple affiliations are separated by \and , which automatically assures cor r \url{} -to provide author email addresses and Web pages. If you need to typeset the tilde character – e.g. for your Web page in your unix system’s home directory – the \homedir command will do this. If multiple authors have the same affiliation, please check that the order of email addresses matches the sequence of (affiliated) author names. +to provide author email addresses and Web pages. If you need to typeset the tilde character – e.g. for your Web page in your unix system's home directory – the \homedir command will do this. If multiple authors have the same affiliation, please check that the order of email addresses matches the sequence of (affiliated) author names. Please note that, if email addresses are given in your paper, they will also be included in the metadata of the online version. @@ -142,7 +142,7 @@ The keyword separator will then be properly rendered as a middle dot. ## 4.1 General Rules -From a technical point of view, the llncs document class does not require any specific LA LAT ATE TEX EX coding in the body of your paper. You can simply use the com mands provided by the ‘article’ document class. For more information about what will be done with your manuscript before publication, please refer to the Springer Guidelines for Authors of Proceedings . +From a technical point of view, the llncs document class does not require any specific LA LAT ATE TEX EX coding in the body of your paper. You can simply use the commands provided by the 'article' document class. For more information about what will be done with your manuscript before publication, please refer to the Springer Guidelines for Authors of Proceedings . ## 4.2 Special Math Characters @@ -179,7 +179,7 @@ example ( env. ) ( ) \begin{case} \end{case} exercise env. \begin{conjecture} \end{conjecture} note ( env. ) \begin{example} \end{example} problem ( env. ) ( ) \begin{exercise} \end{exercise} property env. \begin{note} \end{note} question ( env. ) \begin{problem} \end{problem} remark ( env. ) \begin{property} \end{property} ( ) solution env. \begin{question} \end{question} \begin{remark} \end{remark} \begin{solution} \end{solution} -claim ( env. ) Finally, there are also two unnumbered environments that have the run-in head proof ( env. ) ing in italics and the text in upright roman. +claim ( env. ) Finally, there are also two unnumbered environments that have the run-in headproof ( env. ) ing in italics and the text in upright roman. \begin{claim} \end{claim} \begin{proof} \end{proof} @@ -229,9 +229,9 @@ citeauthoryear Please note that this option does not automatically change your citations to the author/year style. It basically redefines the \bibitem command to take the publication year as an optional parameter that is displayed instead of an arabic number. Author name(s) and, if necessary , parentheses are to be typed manually. If your reference reads -\bibitem[2016]{vdaalst:2016} van der Aalst, W.: Process Mining, 2nd ed. Springer, Heidelberg (2016) and is cited as follows: ... is shown by van der Aalst (\cite{vdaalst:2016}) the resulting text will be: “. .. is shown by van der Aalst (2016).” +\bibitem[2016]{vdaalst:2016} van der Aalst, W.: Process Mining, 2nd ed. Springer, Heidelberg (2016) and is cited as follows: ... is shown by van der Aalst (\cite{vdaalst:2016}) the resulting text will be: ". .. is shown by van der Aalst (2016)." -We encourage you to use Bib TE TEX EX for typesetting your references. For for matting the bibliography according to Springer’s standard (for mathematics, physical sciences, and computer science), please use the bibliography style file splncs04.bst that comes with the llncs document class. You simply need to add \bibliographystyle{splncs04} to your document. DOIs should be pro vided in the doi field of your .bib database. Bib TE TEX EX will then automatically add them to your references. Please note that we do not provide an option to implement +We encourage you to use Bib TE TEX EX for typesetting your references. For formatting the bibliography according to Springer's standard (for mathematics, physical sciences, and computer science), please use the bibliography style file splncs04.bst that comes with the llncs document class. You simply need to add \bibliographystyle{splncs04} to your document. DOIs should be provided in the doi field of your .bib database. Bib TE TEX EX will then automatically add them to your references. Please note that we do not provide an option to implement splncs04.bst @@ -245,7 +245,7 @@ The DOI will be expanded to the URL https://doi.org/ in accordance with the ## 7 Obsolete Class Options -The document class contains several cl ass options that have become ob llncs solete over the years. We only mention them for completeness: +The document class contains several cl ass options that have become obllncs solete over the years. We only mention them for completeness: - orivec – The llncs document class changes the for matting of vectors coded with \vec to boldface italics. If you absolutely need the original LA LAT ATE EX design for TEX vectors, i.e. an arrow above the related variable, you can restore it with the orivec option. - – All theorem-like environments share one counter, i.e. Theorem 1, Lemma 2, Corollary 3, etc. @@ -260,7 +260,7 @@ envcountreset envcountsect -- – This option produces the “open” bibliography style, in which each block starts on a new line, and succeeding lines in a block are indented by \bibindent . +- – This option produces the "open" bibliography style, in which each block starts on a new line, and succeeding lines in a block are indented by \bibindent . openbib diff --git a/tests/pdf_snapshots/pdf/sources/2203.01017v2.pdf.md b/tests/pdf_snapshots/pdf/sources/2203.01017v2.pdf.md index 2fdf3fe0..f6bf03ad 100644 --- a/tests/pdf_snapshots/pdf/sources/2203.01017v2.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/2203.01017v2.pdf.md @@ -1,4 +1,4 @@ -# TableFormer: Table Structure Understanding with Transformers. +## TableFormer: Table Structure Understanding with Transformers. Ahmed Nassar, Nikolaos Livathinos, Maksym Lysak, Peter Staar IBM Research @@ -6,11 +6,11 @@ Ahmed Nassar, Nikolaos Livathinos, Maksym Lysak, Peter Staar IBM Research ## Abstract -Tables organize valuable content in a concise and com pact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph’s, etc, since they enhance their predictive capabilities. Unfortu nately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separa tion lines, missing entries, etc. As such, the correct iden tification of the table-structure from an image is a non trivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-to end deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from program matic PDF’s directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables. +Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables. ## 1. Introduction -The occurrence of tables in documents is ubiquitous. They often summarise quantitative or factual data, which is cumbersome to describe in verbose text but nevertheless ex tremely valuable. Unfortunately, this compact representa tion is often not easy to parse by machines. There are many implicit conventions used to obtain a compact table repre sentation. For example, tables often have complex column and row-headers in order to reduce duplicated cell content. Lines of different shapes and sizes are leveraged to separate content or indicate a tree structure. Additionally, tables can also have empty/missing table-entries or multi-row textual table-entries. Fig. 1 shows a table which presents all these issues. +The occurrence of tables in documents is ubiquitous. They often summarise quantitative or factual data, which is cumbersome to describe in verbose text but nevertheless extremely valuable. Unfortunately, this compact representation is often not easy to parse by machines. There are many implicit conventions used to obtain a compact table representation. For example, tables often have complex columnand row-headers in order to reduce duplicated cell content. Lines of different shapes and sizes are leveraged to separate content or indicate a tree structure. Additionally, tables can also have empty/missing table-entries or multi-row textual table-entries. Fig. 1 shows a table which presents all these issues. ## a. Picture of a table: @@ -34,50 +34,50 @@ The occurrence of tables in documents is ubiquitous. They often summarise quanti | 8 | 2 | 13 | | 14 | 15 | | 16 | | | | 17 | | 18 | 19 | | 20 | -Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: ‘PMC2944238 004 02’. +Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: 'PMC2944238 004 02'. -Recently, significant progress has been made with vi sion based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate chal lenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document. +Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document. -The first problem is called table-location and has been previously addressed [ 30 38 19 21 23 26 8 ] with state of-the-art object-detection networks (e.g. YOLO and later , , , , , , on Mask-RCNN [ 9 ]). For all practical purposes, it can be +The first problem is called table-location and has been previously addressed [ 30 38 19 21 23 26 8 ] with stateof-the-art object-detection networks (e.g. YOLO and later , , , , , , on Mask-RCNN [ 9 ]). For all practical purposes, it can be considered as a solved problem, given enough ground-truth data to train on. -The second problem is called table-structure decompo sition. The latter is a long standing problem in the com munity of document understanding [ 6 , 4 , 14 ]. Contrary to the table-location problem, there are no commonly used ap proaches that can easily be re-purposed to solve this prob lem. Lately, a set of new model-architectures has been pro posed by the community to address table-structure decom position [ 37 36 18 20 ]. All these models have some weak , , , nesses (see Sec. 2 ). The common denominator here is the reliance on textual features and/or the inability to provide the bounding box of each table-cell in the original image. +The second problem is called table-structure decomposition. The latter is a long standing problem in the community of document understanding [ 6 , 4 , 14 ]. Contrary to the table-location problem, there are no commonly used approaches that can easily be re-purposed to solve this problem. Lately, a set of new model-architectures has been proposed by the community to address table-structure decomposition [ 37 36 18 20 ]. All these models have some weak, , , nesses (see Sec. 2 ). The common denominator here is the reliance on textual features and/or the inability to provide the bounding box of each table-cell in the original image. -In this paper, we want to address these weaknesses and present a robust table-structure decomposition algorithm. The design criteria for our model are the following. First, we want our algorithm to be language agnostic. In this way, we can obtain the structure of any table, irregardless of the language. Second, we want our algorithm to leverage as much data as possible from the original PDF document. For programmatic PDF documents, the text-cells can often be extracted much faster and with higher accuracy compared to OCR methods. Last but not least, we want to have a di rect link between the table-cell and its bounding box in the image. +In this paper, we want to address these weaknesses and present a robust table-structure decomposition algorithm. The design criteria for our model are the following. First, we want our algorithm to be language agnostic. In this way, we can obtain the structure of any table, irregardless of the language. Second, we want our algorithm to leverage as much data as possible from the original PDF document. For programmatic PDF documents, the text-cells can often be extracted much faster and with higher accuracy compared to OCR methods. Last but not least, we want to have a direct link between the table-cell and its bounding box in the image. -To meet the design criteria listed above, we developed a new model called TableFormer and a synthetically gener 1 ated table structure dataset called SynthTabNet . In partic ular, our contributions in this work can be summarised as follows: +To meet the design criteria listed above, we developed a new model called TableFormer and a synthetically gener1 ated table structure dataset called SynthTabNet . In particular, our contributions in this work can be summarised as follows: -- • We propose TableFormer , a transformer based model that predicts tables structure and bounding boxes for the table content simultaneously in an end-to-end ap proach. -- • Across all benchmark datasets TableFormer signif icantly outperforms existing state-of-the-art metrics, while being much more efficient in training and infer ence to existing works. -- We present SynthTabNet a synthetically generated • dataset, with various appearance styles and complex ity. +- • We propose TableFormer , a transformer based model that predicts tables structure and bounding boxes for the table content simultaneously in an end-to-end approach. +- • Across all benchmark datasets TableFormer significantly outperforms existing state-of-the-art metrics, while being much more efficient in training and inference to existing works. +- We present SynthTabNet a synthetically generated • dataset, with various appearance styles and complexity. - • An augmented dataset based on PubTabNet [ 37 ], FinTabNet [ 36 ], and TableBank [ 17 ] with generated ground-truth for reproducibility. The paper is structured as follows. In Sec. 2 , we give a brief overview of the current state-of-the-art. In Sec. 3 we describe the datasets on which we train. In Sec. 4 , we , introduce the TableFormer model-architecture and describe 1 https://github.com/IBM/SynthTabNet -its results & performance in Sec. 5 . As a conclusion, we de scribe how this new model-architecture can be re-purposed for other tasks in the computer-vision community. +its results & performance in Sec. 5 . As a conclusion, we describe how this new model-architecture can be re-purposed for other tasks in the computer-vision community. ## 2. Previous work and State of the Art -Identifying the structure of a table has been an outstand ing problem in the document-parsing community, that mo tivates many organised public challenges [ 6 , 4 , 14 ]. The difficulty of the problem can be attributed to a number of factors. First, there is a large variety in the shapes and sizes of tables. Such large variety requires a flexible method. This is especially true for complex column- and row head ers, which can be extremely intricate and demanding. A second factor of complexity is the lack of data with regard to table-structure. Until the publication of PubTabNet [ 37 ], there were no large datasets (i.e. > 100 K tables) that pro vided structure information. This happens primarily due to the fact that tables are notoriously time-consuming to an notate by hand. However, this has definitely changed in re cent years with the deliverance of PubTabNet [ 37 ], FinTab Net [ 36 ], TableBank [ 17 ] etc. +Identifying the structure of a table has been an outstanding problem in the document-parsing community, that motivates many organised public challenges [ 6 , 4 , 14 ]. The difficulty of the problem can be attributed to a number of factors. First, there is a large variety in the shapes and sizes of tables. Such large variety requires a flexible method. This is especially true for complex column- and row headers, which can be extremely intricate and demanding. A second factor of complexity is the lack of data with regard to table-structure. Until the publication of PubTabNet [ 37 ], there were no large datasets (i.e. > 100 K tables) that provided structure information. This happens primarily due to the fact that tables are notoriously time-consuming to annotate by hand. However, this has definitely changed in recent years with the deliverance of PubTabNet [ 37 ], FinTabNet [ 36 ], TableBank [ 17 ] etc. -Before the rising popularity of deep neural networks, the community relied heavily on heuristic and/or statistical methods to do table structure identification [ 3 7 11 5 13 28 ]. Although such methods work well on constrained ta , , , , , bles [ 12 ], a more data-driven approach can be applied due to the advent of convolutional neural networks (CNNs) and the availability of large datasets. To the best-of-our knowl edge, there are currently two different types of network ar chitecture that are being pursued for state-of-the-art table structure identification. +Before the rising popularity of deep neural networks, the community relied heavily on heuristic and/or statistical methods to do table structure identification [ 3 7 11 5 13 28 ]. Although such methods work well on constrained ta, , , , , bles [ 12 ], a more data-driven approach can be applied due to the advent of convolutional neural networks (CNNs) and the availability of large datasets. To the best-of-our knowledge, there are currently two different types of network architecture that are being pursued for state-of-the-art tablestructure identification. -Image-to-Text networks : In this type of network, one predicts a sequence of tokens starting from an encoded image. Such sequences of tokens can be HTML table tags [ 37 , 17 ] or LaTeX symbols[ 10 ]. The choice of sym bols is ultimately not very important, since one can be trans formed into the other. There are however subtle variations in the Image-to-Text networks. The easiest network archi tectures are “image-encoder → text-decoder” (IETD), sim ilar to network architectures that try to provide captions to images [ 32 ]. In these IETD networks, one expects as output the LaTeX/HTML string of the entire table, i.e. the sym bols necessary for creating the table with the content of the table. Another approach is the “image-encoder → dual de coder” (IEDD) networks. In these type of networks, one has two consecutive decoders with different purposes. The first decoder is the tag-decoder , i.e. it only produces the HTM L/LaTeX tags which construct an empty table. The second content-decoder uses the encoding of the image in combi nation with the output encoding of each cell-tag (from the tag-decoder ) to generate the textual content of each table cell. The network architecture of IEDD is certainly more elaborate, but it has the advantage that one can pre-train the +Image-to-Text networks : In this type of network, one predicts a sequence of tokens starting from an encoded image. Such sequences of tokens can be HTML table tags [ 37 , 17 ] or LaTeX symbols[ 10 ]. The choice of symbols is ultimately not very important, since one can be transformed into the other. There are however subtle variations in the Image-to-Text networks. The easiest network architectures are "image-encoder → text-decoder" (IETD), similar to network architectures that try to provide captions to images [ 32 ]. In these IETD networks, one expects as output the LaTeX/HTML string of the entire table, i.e. the symbols necessary for creating the table with the content of the table. Another approach is the "image-encoder → dual decoder" (IEDD) networks. In these type of networks, one has two consecutive decoders with different purposes. The first decoder is the tag-decoder , i.e. it only produces the HTML/LaTeX tags which construct an empty table. The second content-decoder uses the encoding of the image in combination with the output encoding of each cell-tag (from the tag-decoder ) to generate the textual content of each table cell. The network architecture of IEDD is certainly more elaborate, but it has the advantage that one can pre-train the tag-decoder which is constrained to the table-tags. -In practice, both network architectures (IETD and IEDD) require an implicit, custom trained object-character recognition (OCR) to obtain the content of the table-cells. In the case of IETD, this OCR engine is implicit in the de coder similar to [ 24 ]. For the IEDD, the OCR is solely em bedded in the content-decoder. This reliance on a custom, implicit OCR decoder is of course problematic. OCR is a well known and extremely tough problem, that often needs custom training for each individual language. However, the limited availability for non-english content in the current datasets, makes it impractical to apply the IETD and IEDD methods on tables with other languages. Additionally, OCR can be completely omitted if the tables originate from pro grammatic PDF documents with known positions of each cell. The latter was the inspiration for the work of this pa per. +In practice, both network architectures (IETD and IEDD) require an implicit, custom trained object-characterrecognition (OCR) to obtain the content of the table-cells. In the case of IETD, this OCR engine is implicit in the decoder similar to [ 24 ]. For the IEDD, the OCR is solely embedded in the content-decoder. This reliance on a custom, implicit OCR decoder is of course problematic. OCR is a well known and extremely tough problem, that often needs custom training for each individual language. However, the limited availability for non-english content in the current datasets, makes it impractical to apply the IETD and IEDD methods on tables with other languages. Additionally, OCR can be completely omitted if the tables originate from programmatic PDF documents with known positions of each cell. The latter was the inspiration for the work of this paper. -Graph Neural networks : Graph Neural networks (GNN’s) take a radically different approach to table structure extraction. Note that one table cell can consti tute out of multiple text-cells. To obtain the table-structure, one creates an initial graph, where each of the text-cells becomes a node in the graph similar to [ 33 , 34 , 2 ]. Each node is then associated with en embedding vector coming from the encoded image, its coordinates and the encoded text. Furthermore, nodes that represent adjacent text-cells are linked. Graph Convolutional Networks (GCN’s) based methods take the image as an input, but also the position of the text-cells and their content [ 18 ]. The purpose of a GCN is to transform the input graph into a new graph, which re places the old links with new ones. The new links then represent the table-structure. With this approach, one can avoid the need to build custom OCR decoders. However, the quality of the reconstructed structure is not comparable to the current state-of-the-art [ 18 ]. +Graph Neural networks : Graph Neural networks (GNN's) take a radically different approach to tablestructure extraction. Note that one table cell can constitute out of multiple text-cells. To obtain the table-structure, one creates an initial graph, where each of the text-cells becomes a node in the graph similar to [ 33 , 34 , 2 ]. Each node is then associated with en embedding vector coming from the encoded image, its coordinates and the encoded text. Furthermore, nodes that represent adjacent text-cells are linked. Graph Convolutional Networks (GCN's) based methods take the image as an input, but also the position of the text-cells and their content [ 18 ]. The purpose of a GCN is to transform the input graph into a new graph, which replaces the old links with new ones. The new links then represent the table-structure. With this approach, one can avoid the need to build custom OCR decoders. However, the quality of the reconstructed structure is not comparable to the current state-of-the-art [ 18 ]. -Hybrid Deep Learning-Rule-Based approach : A pop ular current model for table-structure identification is the use of a hybrid Deep Learning-Rule-Based approach similar to [ 27 29 ]. In this approach, one first detects the position of the table-cells with object detection (e.g. YoloVx or Mask , RCNN), then classifies the table into different types (from its images) and finally uses different rule-sets to obtain its table-structure. Currently, this approach achieves state of-the-art results, but is not an end-to-end deep-learning method. As such, new rules need to be written if different types of tables are encountered. +Hybrid Deep Learning-Rule-Based approach : A popular current model for table-structure identification is the use of a hybrid Deep Learning-Rule-Based approach similar to [ 27 29 ]. In this approach, one first detects the position of the table-cells with object detection (e.g. YoloVx or Mask, RCNN), then classifies the table into different types (from its images) and finally uses different rule-sets to obtain its table-structure. Currently, this approach achieves stateof-the-art results, but is not an end-to-end deep-learning method. As such, new rules need to be written if different types of tables are encountered. ## 3. Datasets -We rely on large-scale datasets such as PubTabNet [ 37 ], FinTabNet [ 36 ], and TableBank [ 17 ] datasets to train and evaluate our models. These datasets span over various ap pearance styles and content. We also introduce our own synthetically generated SynthTabNet dataset to fix an im +We rely on large-scale datasets such as PubTabNet [ 37 ], FinTabNet [ 36 ], and TableBank [ 17 ] datasets to train and evaluate our models. These datasets span over various appearance styles and content. We also introduce our own synthetically generated SynthTabNet dataset to fix an im @@ -85,19 +85,19 @@ Figure 2: Distribution of the tables across different table dimensions in PubTab balance in the previous datasets. -The PubTabNet dataset contains 509k tables delivered as annotated PNG images. The annotations consist of the table structure represented in HTML format, the tokenized text and its bounding boxes per table cell. Fig. 1 shows the ap pearance style of PubTabNet. Depending on its complexity, a table is characterized as “simple” when it does not contain row spans or column spans, otherwise it is “complex”. The dataset is divided into Train and Val splits (roughly 98% and 2%). The Train split consists of 54% simple and 46% com plex tables and the Val split of 51% and 49% respectively. The FinTabNet dataset contains 112k tables delivered as single-page PDF documents with mixed table structures and text content. Similarly to the PubTabNet, the annotations of FinTabNet include the table structure in HTML, the to kenized text and the bounding boxes on a table cell basis. The dataset is divided into Train, Test and Val splits (81%, 9.5%, 9.5%), and each one is almost equally divided into simple and complex tables (Train: 48% simple, 52% com plex, Test: 48% simple, 52% complex, Test: 53% simple, 47% complex). Finally the TableBank dataset consists of 145k tables provided as JPEG images. The latter has anno tations for the table structure, but only few with bounding boxes of the table cells. The entire dataset consists of sim ple tables and it is divided into 90% Train, 3% Test and 7% Val splits. +The PubTabNet dataset contains 509k tables delivered as annotated PNG images. The annotations consist of the table structure represented in HTML format, the tokenized text and its bounding boxes per table cell. Fig. 1 shows the appearance style of PubTabNet. Depending on its complexity, a table is characterized as "simple" when it does not contain row spans or column spans, otherwise it is "complex". The dataset is divided into Train and Val splits (roughly 98% and 2%). The Train split consists of 54% simple and 46% complex tables and the Val split of 51% and 49% respectively. The FinTabNet dataset contains 112k tables delivered as single-page PDF documents with mixed table structures and text content. Similarly to the PubTabNet, the annotations of FinTabNet include the table structure in HTML, the tokenized text and the bounding boxes on a table cell basis. The dataset is divided into Train, Test and Val splits (81%, 9.5%, 9.5%), and each one is almost equally divided into simple and complex tables (Train: 48% simple, 52% complex, Test: 48% simple, 52% complex, Test: 53% simple, 47% complex). Finally the TableBank dataset consists of 145k tables provided as JPEG images. The latter has annotations for the table structure, but only few with bounding boxes of the table cells. The entire dataset consists of simple tables and it is divided into 90% Train, 3% Test and 7% Val splits. -Due to the heterogeneity across the dataset formats, it was necessary to combine all available data into one homog enized dataset before we could train our models for practi cal purposes. Given the size of PubTabNet, we adopted its annotation format and we extracted and converted all tables as PNG images with a resolution of 72 dpi. Additionally, we have filtered out tables with extreme sizes due to small +Due to the heterogeneity across the dataset formats, it was necessary to combine all available data into one homogenized dataset before we could train our models for practical purposes. Given the size of PubTabNet, we adopted its annotation format and we extracted and converted all tables as PNG images with a resolution of 72 dpi. Additionally, we have filtered out tables with extreme sizes due to small amount of such tables, and kept only those ones ranging between 1*1 and 20*10 (rows/columns). -The availability of the bounding boxes for all table cells is essential to train our models. In order to distinguish be tween empty and non-empty bounding boxes, we have in troduced a binary class in the annotation. Unfortunately, the original datasets either omit the bounding boxes for whole tables (e.g. TableBank) or they narrow their scope only to non-empty cells. Therefore, it was imperative to introduce a data pre-processing procedure that generates the missing bounding boxes out of the annotation information. This pro cedure first parses the provided table structure and calcu lates the dimensions of the most fine-grained grid that cov ers the table structure. Notice that each table cell may oc cupy multiple grid squares due to row or column spans. In case of PubTabNet we had to compute missing bounding boxes for 48% of the simple and 69% of the complex ta bles. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes. +The availability of the bounding boxes for all table cells is essential to train our models. In order to distinguish between empty and non-empty bounding boxes, we have introduced a binary class in the annotation. Unfortunately, the original datasets either omit the bounding boxes for whole tables (e.g. TableBank) or they narrow their scope only to non-empty cells. Therefore, it was imperative to introduce a data pre-processing procedure that generates the missing bounding boxes out of the annotation information. This procedure first parses the provided table structure and calculates the dimensions of the most fine-grained grid that covers the table structure. Notice that each table cell may occupy multiple grid squares due to row or column spans. In case of PubTabNet we had to compute missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes. -As it is illustrated in Fig. 2 , the table distributions from all datasets are skewed towards simpler structures with fewer number of rows/columns. Additionally, there is very limited variance in the table styles, which in case of Pub TabNet and FinTabNet means one styling format for the majority of the tables. Similar limitations appear also in the type of table content, which in some cases (e.g. FinTab Net) is restricted to a certain domain. Ultimately, the lack of diversity in the training dataset damages the ability of the models to generalize well on unseen data. +As it is illustrated in Fig. 2 , the table distributions from all datasets are skewed towards simpler structures with fewer number of rows/columns. Additionally, there is very limited variance in the table styles, which in case of PubTabNet and FinTabNet means one styling format for the majority of the tables. Similar limitations appear also in the type of table content, which in some cases (e.g. FinTabNet) is restricted to a certain domain. Ultimately, the lack of diversity in the training dataset damages the ability of the models to generalize well on unseen data. Motivated by those observations we aimed at generating a synthetic table dataset named SynthTabNet . This approach offers control over: 1) the size of the dataset, 2) the table structure, 3) the table style and 4) the type of content. The complexity of the table structure is described by the size of the table header and the table body, as well as the percentage of the table cells covered by row spans and column spans. A set of carefully designed styling templates provides the basis to build a wide range of table appearances. Lastly, the table content is generated out of a curated collection of text corpora. By controlling the size and scope of the synthetic datasets we are able to train and evaluate our models in a variety of different conditions. For example, we can first generate a highly diverse dataset to train our models and then evaluate their performance on other synthetic datasets which are focused on a specific domain. -In this regard, we have prepared four synthetic datasets, each one containing 150k examples. The corpora to gener ate the table text consists of the most frequent terms appear ing in PubTabNet and FinTabNet together with randomly generated text. The first two synthetic datasets have been fine-tuned to mimic the appearance of the original datasets but encompass more complicated table structures. The third +In this regard, we have prepared four synthetic datasets, each one containing 150k examples. The corpora to generate the table text consists of the most frequent terms appearing in PubTabNet and FinTabNet together with randomly generated text. The first two synthetic datasets have been fine-tuned to mimic the appearance of the original datasets but encompass more complicated table structures. The third | | Tags Bbox Size Format | | | |--------------------|-------------------------|----|-----------| @@ -108,21 +108,21 @@ In this regard, we have prepared four synthetic datasets, each one containing 15 | Combined(**) | ✓ | ✓ | 500k PNG | | SynthTabNet | ✓ | ✓ | 600k PNG | -Table 1: Both “Combined-Tabnet” and ”Combined Tabnet” are variations of the following: (*) The Combined Tabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank. +Table 1: Both "Combined-Tabnet" and "CombinedTabnet" are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank. -one adopts a colorful appearance with high contrast and the last one contains tables with sparse content. Lastly, we have combined all synthetic datasets into one big unified syn thetic dataset of 600k examples. +one adopts a colorful appearance with high contrast and the last one contains tables with sparse content. Lastly, we have combined all synthetic datasets into one big unified synthetic dataset of 600k examples. Tab. 1 summarizes the various attributes of the datasets. ## 4. The TableFormer model -Given the image of a table, TableFormer is able to pre dict: 1) a sequence of tokens that represent the structure of a table, and 2) a bounding box coupled to a subset of those tokens. The conversion of an image into a sequence of to kens is a well-known task [ 35 16 ]. While attention is often , used as an implicit method to associate each token of the sequence with a position in the original image, an explicit association between the individual table-cells and the image bounding boxes is also required. +Given the image of a table, TableFormer is able to predict: 1) a sequence of tokens that represent the structure of a table, and 2) a bounding box coupled to a subset of those tokens. The conversion of an image into a sequence of tokens is a well-known task [ 35 16 ]. While attention is often , used as an implicit method to associate each token of the sequence with a position in the original image, an explicit association between the individual table-cells and the image bounding boxes is also required. ## 4.1. Model architecture. -We now describe in detail the proposed method, which is composed of three main components, see Fig. 4 . Our CNN Backbone Network encodes the input as a feature vec tor of predefined length. The input feature vector of the encoded image is passed to the Structure Decoder to pro duce a sequence of HTML tags that represent the structure of the table. With each prediction of an HTML standard data cell (‘ < td > ’) the hidden state of that cell is passed to the Cell BBox Decoder. As for spanning cells, such as row or column span, the tag is broken down to ‘ < ’, ‘rowspan= ’ or ‘colspan= ’, with the number of spanning cells (attribute), and ‘ > ’. The hidden state attached to ‘ < ’ is passed to the Cell BBox Decoder. A shared feed forward network (FFN) receives the hidden states from the Structure Decoder, to provide the final detection predictions of the bounding box coordinates and their classification. +We now describe in detail the proposed method, which is composed of three main components, see Fig. 4 . Our CNN Backbone Network encodes the input as a feature vector of predefined length. The input feature vector of the encoded image is passed to the Structure Decoder to produce a sequence of HTML tags that represent the structure of the table. With each prediction of an HTML standard data cell (' < td > ') the hidden state of that cell is passed to the Cell BBox Decoder. As for spanning cells, such as row or column span, the tag is broken down to ' < ', 'rowspan= ' or 'colspan= ', with the number of spanning cells (attribute), and ' > '. The hidden state attached to ' < ' is passed to the Cell BBox Decoder. A shared feed forward network (FFN) receives the hidden states from the Structure Decoder, to provide the final detection predictions of the bounding box coordinates and their classification. -CNN Backbone Network. A ResNet-18 CNN is the backbone that receives the table image and encodes it as a vector of predefined length. The network has been modified by removing the linear and pooling layer, as we are not per +CNN Backbone Network. A ResNet-18 CNN is the backbone that receives the table image and encodes it as a vector of predefined length. The network has been modified by removing the linear and pooling layer, as we are not per @@ -130,23 +130,23 @@ Figure 3: TableFormer takes in an image of the PDF and creates bounding box and -Figure 4: Given an input image of a table, the Encoder pro duces fixed-length features that represent the input image. The features are then passed to both the Structure Decoder and Cell BBox Decoder . During training, the Structure Decoder receives ‘tokenized tags’ of the HTML code that represent the table structure. Afterwards, a transformer en coder and decoder architecture is employed to produce fea tures that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. Simultaneously, the Cell BBox Decoder selects features referring to the data cells (‘ < td > ’, ‘ < ’) and passes them through an attention network, an MLP, and a linear layer to predict the bounding boxes. +Figure 4: Given an input image of a table, the Encoder produces fixed-length features that represent the input image. The features are then passed to both the Structure Decoder and Cell BBox Decoder . During training, the Structure Decoder receives 'tokenized tags' of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. Simultaneously, the Cell BBox Decoder selects features referring to the data cells (' < td > ', ' < ') and passes them through an attention network, an MLP, and a linear layer to predict the bounding boxes. forming classification, and adding an adaptive pooling layer of size 28*28. ResNet by default downsamples the image resolution by 32 and then the encoded image is provided to both the Structure Decoder , and Cell BBox Decoder . -Structure Decoder. The transformer architecture of this component is based on the work proposed in [ 31 ]. After extensive experimentation, the Structure Decoder is mod eled as a transformer encoder with two encoder layers and a transformer decoder made from a stack of 4 decoder lay ers that comprise mainly of multi-head attention and feed forward layers. This configuration uses fewer layers and heads in comparison to networks applied to other problems (e.g. “Scene Understanding”, “Image Captioning”), some thing which we relate to the simplicity of table images. +Structure Decoder. The transformer architecture of this component is based on the work proposed in [ 31 ]. After extensive experimentation, the Structure Decoder is modeled as a transformer encoder with two encoder layers and a transformer decoder made from a stack of 4 decoder layers that comprise mainly of multi-head attention and feed forward layers. This configuration uses fewer layers and heads in comparison to networks applied to other problems (e.g. "Scene Understanding", "Image Captioning"), something which we relate to the simplicity of table images. -The transformer encoder receives an encoded image from the CNN Backbone Network and refines it through a multi-head dot-product attention layer, followed by a Feed Forward Network. During training, the transformer de coder receives as input the output feature produced by the transformer encoder, and the tokenized input of the HTML ground-truth tags. Using a stack of multi-head attention lay ers, different aspects of the tag sequence could be inferred. This is achieved by each attention head on a layer operating in a different subspace, and then combining altogether their attention score. +The transformer encoder receives an encoded image from the CNN Backbone Network and refines it through a multi-head dot-product attention layer, followed by a Feed Forward Network. During training, the transformer decoder receives as input the output feature produced by the transformer encoder, and the tokenized input of the HTML ground-truth tags. Using a stack of multi-head attention layers, different aspects of the tag sequence could be inferred. This is achieved by each attention head on a layer operating in a different subspace, and then combining altogether their attention score. -Cell BBox Decoder. Our architecture allows to simul taneously predict HTML tags and bounding boxes for each table cell without the need of a separate object detector end to end. This approach is inspired by DETR [ 1 ] which em ploys a Transformer Encoder, and Decoder that looks for a specific number of object queries (potential object detec tions). As our model utilizes a transformer architecture, the hidden state of the < td > ’ and ‘ < ’ HTML structure tags be come the object query. +Cell BBox Decoder. Our architecture allows to simultaneously predict HTML tags and bounding boxes for each table cell without the need of a separate object detector end to end. This approach is inspired by DETR [ 1 ] which employs a Transformer Encoder, and Decoder that looks for a specific number of object queries (potential object detections). As our model utilizes a transformer architecture, the hidden state of the < td > ' and ' < ' HTML structure tags become the object query. -The encoding generated by the CNN Backbone Network along with the features acquired for every data cell from the Transformer Decoder are then passed to the attention net work. The attention network takes both inputs and learns to provide an attention weighted encoding. This weighted at +The encoding generated by the CNN Backbone Network along with the features acquired for every data cell from the Transformer Decoder are then passed to the attention network. The attention network takes both inputs and learns to provide an attention weighted encoding. This weighted at -tention encoding is then multiplied to the encoded image to produce a feature for each table cell. Notice that this is dif ferent than the typical object detection problem where im balances between the number of detections and the amount of objects may exist. In our case, we know up front that the produced detections always match with the table cells in number and correspondence. +tention encoding is then multiplied to the encoded image to produce a feature for each table cell. Notice that this is different than the typical object detection problem where imbalances between the number of detections and the amount of objects may exist. In our case, we know up front that the produced detections always match with the table cells in number and correspondence. -The output features for each table cell are then fed into the feed-forward network (FFN). The FFN consists of a Multi-Layer Perceptron (3 layers with ReLU activa tion function) that predicts the normalized coordinates for the bounding box of each table cell. Finally, the predicted bounding boxes are classified based on whether they are empty or not using a linear layer. +The output features for each table cell are then fed into the feed-forward network (FFN). The FFN consists of a Multi-Layer Perceptron (3 layers with ReLU activation function) that predicts the normalized coordinates for the bounding box of each table cell. Finally, the predicted bounding boxes are classified based on whether they are empty or not using a linear layer. -Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as l s ) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as l box . l box consists of the generally used l 1 loss for object detection and the IoU loss ( l ) to be scale invariant as explained in [ 25 ]. In iou comparison to DETR, we do not use the Hungarian algo rithm [ 15 ] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-to one match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as in put to the Cell BBox Decoder , and 2) Our bounding boxes generation mechanism (see Sec. 3 ) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets. +Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as l s ) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as l box . l box consists of the generally used l 1 loss for object detection and the IoU loss ( l ) to be scale invariant as explained in [ 25 ]. In iou comparison to DETR, we do not use the Hungarian algorithm [ 15 ] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-toone match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as input to the Cell BBox Decoder , and 2) Our bounding boxes generation mechanism (see Sec. 3 ) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets. The loss used to train the TableFormer can be defined as following: @@ -158,37 +158,37 @@ where λ ∈ [0, 1], and λ , λ ∈ R are hyper-parameters. iou l 1 ## 5.1. Implementation Details -TableFormer uses ResNet-18 as the CNN Backbone Net work . The input images are resized to 448*448 pixels and the feature map has a dimension of 28*28. Additionally, we enforce the following input constraints: +TableFormer uses ResNet-18 as the CNN Backbone Network . The input images are resized to 448*448 pixels and the feature map has a dimension of 28*28. Additionally, we enforce the following input constraints: Image width and height ≤ 1024 pixels (2) Structural tags length ≤ 512 tokens. Although input constraints are used also by other methods, such as EDD, ours are less restrictive due to the improved -runtime performance and lower memory footprint of Table Former. This allows to utilize input samples with longer sequences and images with larger dimensions. +runtime performance and lower memory footprint of TableFormer. This allows to utilize input samples with longer sequences and images with larger dimensions. -The Transformer Encoder consists of two “Transformer Encoder Layers”, with an input feature size of 512, feed forward network of 1024, and 4 attention heads. As for the Transformer Decoder it is composed of four “Transformer Decoder Layers” with similar input and output dimensions as the “Transformer Encoder Layers”. Even though our model uses fewer layers and heads than the default imple mentation parameters, our extensive experimentation has proved this setup to be more suitable for table images. We attribute this finding to the inherent design of table im ages, which contain mostly lines and text, unlike the more elaborate content present in other scopes (e.g. the COCO dataset). Moreover, we have added ResNet blocks to the inputs of the Structure Decoder and Cell BBox Decoder. This prevents a decoder having a stronger influence over the learned weights which would damage the other prediction task (structure vs bounding boxes), but learn task specific weights instead. Lastly our dropout layers are set to 0.5. +The Transformer Encoder consists of two "Transformer Encoder Layers", with an input feature size of 512, feed forward network of 1024, and 4 attention heads. As for the Transformer Decoder it is composed of four "Transformer Decoder Layers" with similar input and output dimensions as the "Transformer Encoder Layers". Even though our model uses fewer layers and heads than the default implementation parameters, our extensive experimentation has proved this setup to be more suitable for table images. We attribute this finding to the inherent design of table images, which contain mostly lines and text, unlike the more elaborate content present in other scopes (e.g. the COCO dataset). Moreover, we have added ResNet blocks to the inputs of the Structure Decoder and Cell BBox Decoder. This prevents a decoder having a stronger influence over the learned weights which would damage the other prediction task (structure vs bounding boxes), but learn task specific weights instead. Lastly our dropout layers are set to 0.5. -For training, TableFormer is trained with 3 Adam opti mizers, each one for the CNN Backbone Network , Structure Decoder , and Cell BBox Decoder . Taking the PubTabNet as an example for our parameter set up, the initializing learn ing rate is 0.001 for 12 epochs with a batch size of 24, and λ set to 0.5. Afterwards, we reduce the learning rate to 0.0001, the batch size to 18 and train for 12 more epochs or convergence. +For training, TableFormer is trained with 3 Adam optimizers, each one for the CNN Backbone Network , Structure Decoder , and Cell BBox Decoder . Taking the PubTabNet as an example for our parameter set up, the initializing learning rate is 0.001 for 12 epochs with a batch size of 24, and λ set to 0.5. Afterwards, we reduce the learning rate to 0.0001, the batch size to 18 and train for 12 more epochs or convergence. -TableFormer is implemented with PyTorch and Torchvi sion libraries [ 22 ]. To speed up the inference, the image undergoes a single forward pass through the CNN Back bone Network and transformer encoder. This eliminates the overhead of generating the same features for each decoding step. Similarly, we employ a ’caching’ technique to preform faster autoregressive decoding. This is achieved by storing the features of decoded tokens so we can reuse them for each time step. Therefore, we only compute the attention for each new tag. +TableFormer is implemented with PyTorch and Torchvision libraries [ 22 ]. To speed up the inference, the image undergoes a single forward pass through the CNN Backbone Network and transformer encoder. This eliminates the overhead of generating the same features for each decoding step. Similarly, we employ a 'caching' technique to preform faster autoregressive decoding. This is achieved by storing the features of decoded tokens so we can reuse them for each time step. Therefore, we only compute the attention for each new tag. ## 5.2. Generalization -TableFormer is evaluated on three major publicly avail able datasets of different nature to prove the generalization and effectiveness of our model. The datasets used for eval uation are the PubTabNet, FinTabNet and TableBank which stem from the scientific, financial and general domains re spectively. +TableFormer is evaluated on three major publicly available datasets of different nature to prove the generalization and effectiveness of our model. The datasets used for evaluation are the PubTabNet, FinTabNet and TableBank which stem from the scientific, financial and general domains respectively. We also share our baseline results on the challenging SynthTabNet dataset. Throughout our experiments, the same parameters stated in Sec. 5.1 are utilized. ## 5.3. Datasets and Metrics -The Tree-Edit-Distance-Based Similarity (TEDS) met ric was introduced in [ 37 ]. It represents the prediction, and ground-truth as a tree structure of HTML tags. This simi larity is calculated as: +The Tree-Edit-Distance-Based Similarity (TEDS) metric was introduced in [ 37 ]. It represents the prediction, and ground-truth as a tree structure of HTML tags. This similarity is calculated as: EditDist ( Ta , Tb Tb ) TEDS ( Ta Ta , Tb Tb ) = 1 − max ( | Ta | Ta | Tb | ) (3) Ta , Tb -where Ta and Tb represent tables in tree structure HTML format. EditDist denotes the tree-edit distance, and Ta Tb | T | rep resents the number of nodes in T . +where Ta and Tb represent tables in tree structure HTML format. EditDist denotes the tree-edit distance, and Ta Tb | T | represents the number of nodes in T . ## 5.4. Quantitative Analysis -Structure. As shown in Tab. 2 , TableFormer outper forms all SOTA methods across different datasets by a large margin for predicting the table structure from an image. All the more, our model outperforms pre-trained methods. During the evaluation we do not apply any table filtering. We also provide our baseline results on the SynthTabNet dataset. It has been observed that large tables (e.g. tables that occupy half of the page or more) yield poor predictions. We attribute this issue to the image resizing during the pre processing step, that produces downsampled images with indistinguishable features. This problem can be addressed by treating such big tables with a separate model which ac cepts a large input image size. +Structure. As shown in Tab. 2 , TableFormer outperforms all SOTA methods across different datasets by a large margin for predicting the table structure from an image. All the more, our model outperforms pre-trained methods. During the evaluation we do not apply any table filtering. We also provide our baseline results on the SynthTabNet dataset. It has been observed that large tables (e.g. tables that occupy half of the page or more) yield poor predictions. We attribute this issue to the image resizing during the preprocessing step, that produces downsampled images with indistinguishable features. This problem can be addressed by treating such big tables with a separate model which accepts a large input image size. | | | | TEDS | | |--------------------------------|-------------------------|----------------------------|--------|-------| @@ -209,9 +209,9 @@ Table 2: Structure results on PubTabNet (PTN), FinTabNet (FTN), TableBank (TB) a FT: Model was trained on PubTabNet then finetuned. -Cell Detection. Like any object detector, our Cell BBox Detector provides bounding boxes that can be improved with post-processing during inference. We make use of the grid-like structure of tables to refine the predictions. A de tailed explanation on the post-processing is available in the supplementary material. As shown in Tab. 3 , we evaluate +Cell Detection. Like any object detector, our Cell BBox Detector provides bounding boxes that can be improved with post-processing during inference. We make use of the grid-like structure of tables to refine the predictions. A detailed explanation on the post-processing is available in the supplementary material. As shown in Tab. 3 , we evaluate -Cell BBox Decoder accuracy for cells with a class la our bel of ‘content’ only using the PASCAL VOC mAP metric for pre-processing and post-processing. Note that we do not have post-processing results for SynthTabNet as images are only provided. To compare the performance of our pro posed approach, we’ve integrated TableFormer’s Cell BBox Decoder into EDD architecture. As mentioned previously, the Structure Decoder provides the Cell BBox Decoder with the features needed to predict the bounding box predictions. Therefore, the accuracy of the Structure Decoder directly influences the accuracy of the Cell BBox Decoder . If the Structure Decoder predicts an extra column, this will result in an extra column of predicted bounding boxes. +Cell BBox Decoder accuracy for cells with a class laour bel of 'content' only using the PASCAL VOC mAP metric for pre-processing and post-processing. Note that we do not have post-processing results for SynthTabNet as images are only provided. To compare the performance of our proposed approach, we've integrated TableFormer's Cell BBox Decoder into EDD architecture. As mentioned previously, the Structure Decoder provides the Cell BBox Decoder with the features needed to predict the bounding box predictions. Therefore, the accuracy of the Structure Decoder directly influences the accuracy of the Cell BBox Decoder . If the Structure Decoder predicts an extra column, this will result in an extra column of predicted bounding boxes. | | Model Dataset mAP mAP (PP) | | |--------------------------------|------------------------------|-----------| @@ -219,9 +219,9 @@ Cell BBox Decoder accuracy for cells with a class la our bel of ‘content’ o | TableFormer PubTabNet | | 82.1 86.8 | | TableFormer SynthTabNet 87.7 - | | | -Table 3: Cell Bounding Box detection results on PubTab Net, and FinTabNet. PP: Post-processing. +Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing. -Cell Content. In this section, we evaluate the entire pipeline of recovering a table with content. Here we put our approach to test by capitalizing on extracting content from the PDF cells rather than decoding from images. Tab. 4 shows the TEDs score of HTML code representing the structure of the table along with the content inserted in the data cell and compared with the ground-truth. Our method achieved a 5.3% increase over the state-of-the-art, and com mercial solutions. We believe our scores would be higher if the HTML ground-truth matched the extracted PDF cell content. Unfortunately, there are small discrepancies such as spacings around words or special characters with various unicode representations. +Cell Content. In this section, we evaluate the entire pipeline of recovering a table with content. Here we put our approach to test by capitalizing on extracting content from the PDF cells rather than decoding from images. Tab. 4 shows the TEDs score of HTML code representing the structure of the table along with the content inserted in the data cell and compared with the ground-truth. Our method achieved a 5.3% increase over the state-of-the-art, and commercial solutions. We believe our scores would be higher if the HTML ground-truth matched the extracted PDF cell content. Unfortunately, there are small discrepancies such as spacings around words or special characters with various unicode representations. | | | | TEDS | | |----------------------------|------------------------|--------------------|--------|------| @@ -256,59 +256,59 @@ Figure 6: An example of TableFormer predictions (bounding boxes and structure) f ## 5.5. Qualitative Analysis -In this paper, we presented TableFormer an end-to-end transformer based approach to predict table structures and bounding boxes of cells from an image. This approach en ables us to recreate the table structure, and extract the cell content from PDF or OCR by using bounding boxes. Ad ditionally, it provides the versatility required in real-world scenarios when dealing with various types of PDF docu ments, and languages. Furthermore, our method outper forms all state-of-the-arts with a wide margin. Finally, we introduce “SynthTabNet” a challenging synthetically gen erated dataset that reinforces missing characteristics from other datasets. +In this paper, we presented TableFormer an end-to-end transformer based approach to predict table structures and bounding boxes of cells from an image. This approach enables us to recreate the table structure, and extract the cell content from PDF or OCR by using bounding boxes. Additionally, it provides the versatility required in real-world scenarios when dealing with various types of PDF documents, and languages. Furthermore, our method outperforms all state-of-the-arts with a wide margin. Finally, we introduce "SynthTabNet" a challenging synthetically generated dataset that reinforces missing characteristics from other datasets. -We showcase several visualizations for the different components of our network on various “complex” tables within datasets presented in this work in Fig. 5 and Fig. 6 As it is shown, our model is able to predict bounding boxes for all table cells, even for the empty ones. Additionally, our post-processing techniques can extract the cell content by matching the predicted bounding boxes to the PDF cells based on their overlap and spatial proximity. The left part of Fig. 5 demonstrates also the adaptability of our method to any language, as it can successfully extract Japanese text, although the training set contains only English content. We provide more visualizations including the intermediate steps in the supplementary material. Overall these illustra tions justify the versatility of our method across a diverse range of table appearances and content type. +We showcase several visualizations for the different components of our network on various "complex" tables within datasets presented in this work in Fig. 5 and Fig. 6 As it is shown, our model is able to predict bounding boxes for all table cells, even for the empty ones. Additionally, our post-processing techniques can extract the cell content by matching the predicted bounding boxes to the PDF cells based on their overlap and spatial proximity. The left part of Fig. 5 demonstrates also the adaptability of our method to any language, as it can successfully extract Japanese text, although the training set contains only English content. We provide more visualizations including the intermediate steps in the supplementary material. 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Data preparation -As a first step of our data preparation process, we have calculated statistics over the datasets across the following dimensions: (1) table size measured in the number of rows and columns, (2) complexity of the table, (3) strictness of the provided HTML structure and (4) completeness (i.e. no omitted bounding boxes). A table is considered to be simple if it does not contain row spans or column spans. Addition ally, a table has a strict HTML structure if every row has the same number of columns after taking into account any row or column spans. Therefore a strict HTML structure looks always rectangular. However, HTML is a lenient encoding format, i.e. tables with rows of different sizes might still be regarded as correct due to implicit display rules. These implicit rules leave room for ambiguity, which we want to avoid. As such, we prefer to have ”strict” tables, i.e. tables where every row has exactly the same length. +As a first step of our data preparation process, we have calculated statistics over the datasets across the following dimensions: (1) table size measured in the number of rows and columns, (2) complexity of the table, (3) strictness of the provided HTML structure and (4) completeness (i.e. no omitted bounding boxes). A table is considered to be simple if it does not contain row spans or column spans. Additionally, a table has a strict HTML structure if every row has the same number of columns after taking into account any row or column spans. Therefore a strict HTML structure looks always rectangular. However, HTML is a lenient encoding format, i.e. tables with rows of different sizes might still be regarded as correct due to implicit display rules. These implicit rules leave room for ambiguity, which we want to avoid. As such, we prefer to have "strict" tables, i.e. tables where every row has exactly the same length. -We have developed a technique that tries to derive a missing bounding box out of its neighbors. As a first step, we use the annotation data to generate the most fine-grained grid that covers the table structure. In case of strict HTML tables, all grid squares are associated with some table cell and in the presence of table spans a cell extends across mul tiple grid squares. When enough bounding boxes are known for a rectangular table, it is possible to compute the geo metrical border lines between the grid rows and columns. Eventually this information is used to generate the missing bounding boxes. Additionally, the existence of unused grid squares indicates that the table rows have unequal number of columns and the overall structure is non-strict. The gen eration of missing bounding boxes for non-strict HTML ta bles is ambiguous and therefore quite challenging. Thus, we have decided to simply discard those tables. In case of PubTabNet we have computed missing bounding boxes for 48% of the simple and 69% of the complex tables. Regard ing FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes. +We have developed a technique that tries to derive a missing bounding box out of its neighbors. As a first step, we use the annotation data to generate the most fine-grained grid that covers the table structure. In case of strict HTML tables, all grid squares are associated with some table cell and in the presence of table spans a cell extends across multiple grid squares. When enough bounding boxes are known for a rectangular table, it is possible to compute the geometrical border lines between the grid rows and columns. Eventually this information is used to generate the missing bounding boxes. Additionally, the existence of unused grid squares indicates that the table rows have unequal number of columns and the overall structure is non-strict. The generation of missing bounding boxes for non-strict HTML tables is ambiguous and therefore quite challenging. Thus, we have decided to simply discard those tables. In case of PubTabNet we have computed missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes. Figure 7 illustrates the distribution of the tables across different dimensions per dataset. ## 1.2. Synthetic datasets -Aiming to train and evaluate our models in a broader spectrum of table data we have synthesized four types of datasets. Each one contains tables with different appear +Aiming to train and evaluate our models in a broader spectrum of table data we have synthesized four types of datasets. Each one contains tables with different appear ances in regard to their size, structure, style and content. Every synthetic dataset contains 150k examples, summing up to 600k synthetic examples. All datasets are divided into Train, Test and Val splits (80%, 10%, 10%). -The process of generating a synthetic dataset can be de composed into the following steps: +The process of generating a synthetic dataset can be decomposed into the following steps: -- 1. Prepare styling and content templates: The styling templates have been manually designed and organized into groups of scope specific appearances (e.g. financial data, marketing data, etc.) Additionally, we have prepared cu rated collections of content templates by extracting the most frequently used terms out of non-synthetic datasets (e.g. PubTabNet, FinTabNet, etc.). -- 2. Generate table structures: The structure of each syn thetic dataset assumes a horizontal table header which po tentially spans over multiple rows and a table body that may contain a combination of row spans and column spans. However, spans are not allowed to cross the header - body boundary. The table structure is described by the parame ters: Total number of table rows and columns, number of header rows, type of spans (header only spans, row only spans, column only spans, both row and column spans), maximum span size and the ratio of the table area covered by spans. +- 1. Prepare styling and content templates: The styling templates have been manually designed and organized into groups of scope specific appearances (e.g. financial data, marketing data, etc.) Additionally, we have prepared curated collections of content templates by extracting the most frequently used terms out of non-synthetic datasets (e.g. PubTabNet, FinTabNet, etc.). +- 2. Generate table structures: The structure of each synthetic dataset assumes a horizontal table header which potentially spans over multiple rows and a table body that may contain a combination of row spans and column spans. However, spans are not allowed to cross the header - body boundary. The table structure is described by the parameters: Total number of table rows and columns, number of header rows, type of spans (header only spans, row only spans, column only spans, both row and column spans), maximum span size and the ratio of the table area covered by spans. - 3. Generate content: Based on the dataset theme , a set of suitable content templates is chosen first. Then, this content can be combined with purely random text to produce the synthetic content. - 4. Apply styling templates: Depending on the domain of the synthetic dataset, a set of styling templates is first manually selected. Then, a style is randomly selected to format the appearance of the synthesized table. - 5. Render the complete tables: The synthetic table is finally rendered by a web browser engine to generate the bounding boxes for each table cell. A batching technique is utilized to optimize the runtime overhead of the rendering process. -## 2. Prediction post-processing for PDF docu ments +## 2. Prediction post-processing for PDF documents -Although TableFormer can predict the table structure and the bounding boxes for tables recognized inside PDF docu ments, this is not enough when a full reconstruction of the original table is required. This happens mainly due the fol lowing reasons: +Although TableFormer can predict the table structure and the bounding boxes for tables recognized inside PDF documents, this is not enough when a full reconstruction of the original table is required. This happens mainly due the following reasons: Figure 7: Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split, strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity. -- • TableFormer output does not include the table cell con tent. +- • TableFormer output does not include the table cell content. - • There are occasional inaccuracies in the predictions of the bounding boxes. -However, it is possible to mitigate those limitations by combining the TableFormer predictions with the informa tion already present inside a programmatic PDF document. More specifically, PDF documents can be seen as a se quence of PDF cells where each cell is described by its con tent and bounding box. If we are able to associate the PDF cells with the predicted table cells, we can directly link the PDF cell content to the table cell structure and use the PDF bounding boxes to correct misalignments in the predicted table cell bounding boxes. +However, it is possible to mitigate those limitations by combining the TableFormer predictions with the information already present inside a programmatic PDF document. More specifically, PDF documents can be seen as a sequence of PDF cells where each cell is described by its content and bounding box. If we are able to associate the PDF cells with the predicted table cells, we can directly link the PDF cell content to the table cell structure and use the PDF bounding boxes to correct misalignments in the predicted table cell bounding boxes. -Here is a step-by-step description of the prediction post processing: +Here is a step-by-step description of the prediction postprocessing: - 1. Get the minimal grid dimensions - number of rows and columns for the predicted table structure. This represents the most granular grid for the underlying table structure. -- 2. Generate pair-wise matches between the bounding boxes of the PDF cells and the predicted cells. The Intersec tion Over Union (IOU) metric is used to evaluate the quality of the matches. -- 3. Use a carefully selected IOU threshold to designate the matches as “good” ones and “bad” ones. -- 3.a. If all IOU scores in a column are below the thresh old, discard all predictions (structure and bounding boxes) for that column. -- 4. Find the best-fitting content alignment for the pre dicted cells with good IOU per each column. The alignment of the column can be identified by the following formula: +- 2. Generate pair-wise matches between the bounding boxes of the PDF cells and the predicted cells. The Intersection Over Union (IOU) metric is used to evaluate the quality of the matches. +- 3. Use a carefully selected IOU threshold to designate the matches as "good" ones and "bad" ones. +- 3.a. If all IOU scores in a column are below the threshold, discard all predictions (structure and bounding boxes) for that column. +- 4. Find the best-fitting content alignment for the predicted cells with good IOU per each column. The alignment of the column can be identified by the following formula: alignment = arg min { D c } c (4) D c = max { x c } − min { x c } -where c is one of { left, centroid, right } and x is the x coordinate for the corresponding point. c +where c is one of { left, centroid, right } and x is the xcoordinate for the corresponding point. c -- 5. Use the alignment computed in step 4, to compute the median x -coordinate for all table columns and the me +- 5. Use the alignment computed in step 4, to compute the median x -coordinate for all table columns and the me -dian cell size for all table cells. The usage of median dur ing the computations, helps to eliminate outliers caused by occasional column spans which are usually wider than the normal. +dian cell size for all table cells. The usage of median during the computations, helps to eliminate outliers caused by occasional column spans which are usually wider than the normal. - 6. Snap all cells with bad IOU to their corresponding median x -coordinates and cell sizes. - 7. Generate a new set of pair-wise matches between the corrected bounding boxes and PDF cells. This time use a modified version of the IOU metric, where the area of the intersection between the predicted and PDF cells is divided by the PDF cell area. In case there are multiple matches for the same PDF cell, the prediction with the higher score is preferred. This covers the cases where the PDF cells are smaller than the area of predicted or corrected prediction cells. -- 8. In some rare occasions, we have noticed that Table Former can confuse a single column as two. When the post processing steps are applied, this results with two predicted columns pointing to the same PDF column. In such case we must de-duplicate the columns according to highest to tal column intersection score. +- 8. In some rare occasions, we have noticed that TableFormer can confuse a single column as two. When the postprocessing steps are applied, this results with two predicted columns pointing to the same PDF column. In such case we must de-duplicate the columns according to highest total column intersection score. - 9. Pick up the remaining orphan cells. There could be cases, when after applying all the previous post-processing steps, some PDF cells could still remain without any match to predicted cells. However, it is still possible to deduce the correct matching for an orphan PDF cell by mapping its bounding box on the geometry of the grid. This mapping decides if the content of the orphan cell will be appended to an already matched table cell, or a new table cell should be created to match with the orphan. -- 9a. Compute the top and bottom boundary of the hori zontal band for each grid row (min/max y coordinates per row). -- 9b. Intersect the orphan’s bounding box with the row bands, and map the cell to the closest grid row. -- 9c. Compute the left and right boundary of the vertical band for each grid column (min/max x coordinates per col umn). -- 9d. Intersect the orphan’s bounding box with the column bands, and map the cell to the closest grid column. -- 9e. If the table cell under the identified row and column is not empty, extend its content with the content of the or +- 9a. Compute the top and bottom boundary of the horizontal band for each grid row (min/max y coordinates per row). +- 9b. Intersect the orphan's bounding box with the row bands, and map the cell to the closest grid row. +- 9c. Compute the left and right boundary of the vertical band for each grid column (min/max x coordinates per column). +- 9d. Intersect the orphan's bounding box with the column bands, and map the cell to the closest grid column. +- 9e. If the table cell under the identified row and column is not empty, extend its content with the content of the or phan cell. 9f. Otherwise create a new structural cell and match it wit the orphan cell. -Aditional images with examples of TableFormer predic tions and post-processing can be found below. +Aditional images with examples of TableFormer predictions and post-processing can be found below. @@ -391,7 +391,7 @@ Figure 8: Example of a table with multi-line header. -Figure 9: Example of a table with big empty distance be tween cells. +Figure 9: Example of a table with big empty distance between cells. @@ -421,4 +421,4 @@ Figure 16: Example of how post-processing helps to restore mis-aligned bounding -Figure 17: Example of long table. End-to-end example from initial PDF cells to prediction of bounding boxes, post process ing and prediction of structure. +Figure 17: Example of long table. End-to-end example from initial PDF cells to prediction of bounding boxes, post processing and prediction of structure. diff --git a/tests/pdf_snapshots/pdf/sources/2206.01062.pdf.md b/tests/pdf_snapshots/pdf/sources/2206.01062.pdf.md index 08e62400..5bc3f827 100644 --- a/tests/pdf_snapshots/pdf/sources/2206.01062.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/2206.01062.pdf.md @@ -4,15 +4,15 @@ Ahmed S. Nassar IBM Research Rueschlikon, Switzerland ahn@zurich.ibm.com ## ABSTRACT -Accurate document layout analysis is a key requirement for high quality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet, t, a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Fur thermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNet trained models are more robust and thus the preferred choice for general-purpose document-layout analysis. +Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet, t, a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis. ## CCS CONCEPTS -• Information systems → Document structure ; • Applied com puting → Document analysis ; • Computing methodologies → Machine learning ; Computer vision ; Object detection ; +• Information systems → Document structure ; • Applied computing → Document analysis ; • Computing methodologies → Machine learning ; Computer vision ; Object detection ; Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). -KDD ’22, August 14–18, 2022, Washington, DC, USA +KDD '22, August 14–18, 2022, Washington, DC, USA © 2022 Copyright held by the owner/author(s). @@ -20,7 +20,7 @@ ACM ISBN 978-1-4503-9385-0/22/08. https://doi.org/10.1145/3534678.3539043 -# DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis +## DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis Michele Dolfi IBM Research Rueschlikon, Switzerland dol@zurich.ibm.com @@ -38,7 +38,7 @@ Peter Staar IBM Research Rueschlikon, Switzerland taa@zurich.ibm.com -Figure 1: Four examples of complex page layouts across dif ferent document categories +Figure 1: Four examples of complex page layouts across different document categories ## KEYWORDS @@ -46,27 +46,27 @@ PDF document conversion, layout segmentation, object-detection, data set, Machin ## ACM Reference Format: -Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for Document Layout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’22), August 14–18, 2022, Wash ington, DC, USA. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/ 3534678.3539043 +Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22), August 14–18, 2022, Washington, DC, USA. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/ 3534678.3539043 -KDD ’22, August 14–18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar +KDD '22, August 14–18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar ## 1 INTRODUCTION -Despite the substantial improvements achieved with machine-learning (ML) approaches and deep neural networks in recent years, docu ment conversion remains a challenging problem, as demonstrated by the numerous public competitions held on this topic [ 1 – 4 ]. The challenge originates from the huge variability in PDF documents regarding layout, language and formats (scanned, programmatic or a combination of both). Engineering a single ML model that can be applied on all types of documents and provides high-quality layout segmentation remains to this day extremely challenging [ ]. 5 To highlight the variability in document layouts, we show a few example documents from the DocLayNet dataset in Figure 1. +Despite the substantial improvements achieved with machine-learning (ML) approaches and deep neural networks in recent years, document conversion remains a challenging problem, as demonstrated by the numerous public competitions held on this topic [ 1 – 4 ]. The challenge originates from the huge variability in PDF documents regarding layout, language and formats (scanned, programmatic or a combination of both). Engineering a single ML model that can be applied on all types of documents and provides high-quality layout segmentation remains to this day extremely challenging [ ]. 5 To highlight the variability in document layouts, we show a few example documents from the DocLayNet dataset in Figure 1. -A key problem in the process of document conversion is to under stand the structure of a single document page, i.e. which segments of text should be grouped together in a unit. To train models for this task, there are currently two large datasets available to the com munity, PubLayNet [ 6 ] and DocBank [ 7 ]. They were introduced in 2019 and 2020 respectively and significantly accelerated the im plementation of layout detection and segmentation models due to their sizes of 300K and 500K ground-truth pages. These sizes were achieved by leveraging an automation approach. The benefit of au tomated ground-truth generation is obvious: one can generate large ground-truth datasets at virtually no cost. However, the automation introduces a constraint on the variability in the dataset, because corresponding structured source data must be available. PubLayNet and DocBank were both generated from scientific document repos itories (PubMed and arXiv), which provide XML or LA LAT ATE TEX EX sources. Those scientific documents present a limited variability in their layouts, because they are typeset in uniform templates provided by the publishers. Obviously, documents such as technical manuals, annual company reports, legal text, government tenders, etc. have very different and partially unique layouts. As a consequence, the layout predictions obtained from models trained on PubLayNet or DocBank is very reasonable when applied on scientific documents. However, for more artistic or free-style layouts, we see sub-par prediction quality from these models, which we demonstrate in Section 5. +A key problem in the process of document conversion is to understand the structure of a single document page, i.e. which segments of text should be grouped together in a unit. To train models for this task, there are currently two large datasets available to the community, PubLayNet [ 6 ] and DocBank [ 7 ]. They were introduced in 2019 and 2020 respectively and significantly accelerated the implementation of layout detection and segmentation models due to their sizes of 300K and 500K ground-truth pages. These sizes were achieved by leveraging an automation approach. The benefit of automated ground-truth generation is obvious: one can generate large ground-truth datasets at virtually no cost. However, the automation introduces a constraint on the variability in the dataset, because corresponding structured source data must be available. PubLayNet and DocBank were both generated from scientific document repositories (PubMed and arXiv), which provide XML or LA LAT ATE TEX EX sources. Those scientific documents present a limited variability in their layouts, because they are typeset in uniform templates provided by the publishers. Obviously, documents such as technical manuals, annual company reports, legal text, government tenders, etc. have very different and partially unique layouts. As a consequence, the layout predictions obtained from models trained on PubLayNet or DocBank is very reasonable when applied on scientific documents. However, for more artistic or free-style layouts, we see sub-par prediction quality from these models, which we demonstrate in Section 5. -In this paper, we present the DocLayNet dataset. It provides page by-page layout annotation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique document pages, of which a fraction carry double- or triple-annotations. DocLayNet is similar in spirit to PubLayNet and DocBank and will likewise be made available to the public 1 in order to stimulate the document-layout analysis community. It distinguishes itself in the following aspects: +In this paper, we present the DocLayNet dataset. It provides pageby-page layout annotation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique document pages, of which a fraction carry double- or triple-annotations. DocLayNet is similar in spirit to PubLayNet and DocBank and will likewise be made available to the public 1 in order to stimulate the document-layout analysis community. It distinguishes itself in the following aspects: -- (1) Human Annotation : In contrast to PubLayNet and DocBank, we relied on human annotation instead of automation ap proaches to generate the data set. +- (1) Human Annotation : In contrast to PubLayNet and DocBank, we relied on human annotation instead of automation approaches to generate the data set. - (2) Large Layout Variability : We include diverse and complex layouts from a large variety of public sources. - (3) Detailed Label Set : We define 11 class labels to distinguish layout features in high detail. PubLayNet provides 5 labels; DocBank provides 13, although not a superset of ours. -- (4) Redundant Annotations : A fraction of the pages in the Do cLayNet data set carry more than one human annotation. +- (4) Redundant Annotations : A fraction of the pages in the DocLayNet data set carry more than one human annotation. 1 https://developer.ibm.com/exchanges/data/all/doclaynet - This enables experimentation with annotation uncertainty and quality control analysis. -- (5) Pre-defined Train-, Test- & Validation-set : Like DocBank, we provide fixed train-, test- & validation-sets to ensure propor tional representation of the class-labels. Further, we prevent leakage of unique layouts across sets, which has a large effect on model accuracy scores. +- (5) Pre-defined Train-, Test- & Validation-set : Like DocBank, we provide fixed train-, test- & validation-sets to ensure proportional representation of the class-labels. Further, we prevent leakage of unique layouts across sets, which has a large effect on model accuracy scores. All aspects outlined above are detailed in Section 3. In Section 4, we will elaborate on how we designed and executed this large-scale human annotation campaign. We will also share key insights and lessons learned that might prove helpful for other parties planning to set up annotation campaigns. @@ -74,13 +74,13 @@ In Section 5, we will present baseline accuracy numbers for a variety of object ## 2 RELATED WORK -While early approaches in document-layout analysis used rule based algorithms and heuristics [ 8 ], the problem is lately addressed with deep learning methods. The most common approach is to lever age object detection models [ 9 – 15 ]. In the last decade, the accuracy and speed of these models has increased dramatically. Furthermore, most state-of-the-art object detection methods can be trained and applied with very little work, thanks to a standardisation effort of the ground-truth data format [ 16 ] and common deep-learning frameworks [ ]. Reference data sets such as PubLayNet [ ] and 17 6 DocBank provide their data in the commonly accepted COCO for mat [16]. +While early approaches in document-layout analysis used rulebased algorithms and heuristics [ 8 ], the problem is lately addressed with deep learning methods. The most common approach is to leverage object detection models [ 9 – 15 ]. In the last decade, the accuracy and speed of these models has increased dramatically. Furthermore, most state-of-the-art object detection methods can be trained and applied with very little work, thanks to a standardisation effort of the ground-truth data format [ 16 ] and common deep-learning frameworks [ ]. Reference data sets such as PubLayNet [ ] and 17 6 DocBank provide their data in the commonly accepted COCO format [16]. Lately, new types of ML models for document-layout analysis have emerged in the community [ 18 – 21 ]. These models do not approach the problem of layout analysis purely based on an image representation of the page, as computer vision methods do. Instead, they combine the text tokens and image representation of a page in order to obtain a segmentation. While the reported accuracies appear to be promising, a broadly accepted data format which links geometric and textual features has yet to establish. ## 3 THE DOCLAYNET DATASET -DocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of human annotations, and 1591 carry three. This amounts to 91104 total annotation instances. The annotations provide lay out information in the shape of labeled, rectangular bounding boxes. We define 11 distinct labels for layout features, namely Cap tion Footnote Formula List-item Page-footer, Page-header, Picture Section-header, , , Table Text, , t, and Title , . Our reasoning for picking this r, r, , r, , particular label set is detailed in Section 4. +DocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of human annotations, and 1591 carry three. This amounts to 91104 total annotation instances. The annotations provide layout information in the shape of labeled, rectangular boundingboxes. We define 11 distinct labels for layout features, namely Caption Footnote Formula List-item Page-footer, Page-header, Picture Section-header, , , Table Text, , t, and Title , . Our reasoning for picking this r, r, , r, , particular label set is detailed in Section 4. In addition to open intellectual property constraints for the source documents, we required that the documents in DocLayNet adhere to a few conditions. Firstly, we kept scanned documents @@ -88,13 +88,13 @@ In addition to open intellectual property constraints for the source documents, Figure 2: Distribution of DocLayNet pages across document categories. -to a minimum, since they introduce difficulties in annotation (see Section 4). As a second condition, we focussed on medium to large documents ( > 10 pages) with technical content, dense in complex tables, figures, plots and captions. Such documents carry a lot of information value, but are often hard to analyse with high accuracy due to their challenging layouts. Counterexamples of documents not included in the dataset are receipts, invoices, hand-written documents or photographs showing “text in the wild". +to a minimum, since they introduce difficulties in annotation (see Section 4). As a second condition, we focussed on medium to large documents ( > 10 pages) with technical content, dense in complex tables, figures, plots and captions. Such documents carry a lot of information value, but are often hard to analyse with high accuracy due to their challenging layouts. Counterexamples of documents not included in the dataset are receipts, invoices, hand-written documents or photographs showing "text in the wild". -The pages in DocLayNet can be grouped into six distinct cate gories, namely Financial Reports Manuals Scientific Articles Laws & Regulations Patents and Government Tenders , , . Each document cate , gory was sourced from various repositories. For example, Financial , Reports contain both free-style format annual reports 2 which ex pose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories ( Financial Reports and Man uals ) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes. +The pages in DocLayNet can be grouped into six distinct categories, namely Financial Reports Manuals Scientific Articles Laws & Regulations Patents and Government Tenders , , . Each document cate, gory was sourced from various repositories. For example, Financial , Reports contain both free-style format annual reports 2 which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories ( Financial Reports and Manuals ) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes. -We did not control the document selection with regard to lan guage. The vast majority of documents contained in DocLayNet (close to 95%) are published in English language. However, Do cLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%). While the document language has negligible impact on the performance of computer vision methods such as object detection and segmenta tion models, it might prove challenging for layout analysis methods which exploit textual features. +We did not control the document selection with regard to language. The vast majority of documents contained in DocLayNet (close to 95%) are published in English language. However, DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%). While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features. -To ensure that future benchmarks in the document-layout analy sis community can be easily compared, we have split up DocLayNet into pre-defined train-, test- and validation-sets. In this way, we can avoid spurious variations in the evaluation scores due to random splitting in train-, test- and validation-sets. We also ensured that less frequent labels are represented in train and test sets in equal proportions. +To ensure that future benchmarks in the document-layout analysis community can be easily compared, we have split up DocLayNet into pre-defined train-, test- and validation-sets. In this way, we can avoid spurious variations in the evaluation scores due to random splitting in train-, test- and validation-sets. We also ensured that less frequent labels are represented in train and test sets in equal proportions. 2 e.g. AAPL from https://www.annualreports.com/ @@ -102,13 +102,13 @@ Table 1 shows the overall frequency and distribution of the labels among the dif In order to accommodate the different types of models currently in use by the community, we provide DocLayNet in an augmented COCO format [ 16 ]. This entails the standard COCO ground-truth file (in JSON format) with the associated page images (in PNG format, 1025 × 1025 pixels). Furthermore, custom fields have been added to each COCO record to specify document category, original document filename and page number. In addition, we also provide the original PDF pages, as well as sidecar files containing parsed PDF text and text-cell coordinates (in JSON). All additional files are linked to the primary page images by their matching filenames. -Despite being cost-intense and far less scalable than automation, human annotation has several benefits over automated ground truth generation. The first and most obvious reason to leverage human annotations is the freedom to annotate any type of doc ument without requiring a programmatic source. For most PDF documents, the original source document is not available. The lat ter is not a hard constraint with human annotation, but it is for automated methods. A second reason to use human annotations is that the latter usually provide a more natural interpretation of the page layout. The human-interpreted layout can significantly devi ate from the programmatic layout used in typesetting. For example, “invisible” tables might be used solely for aligning text paragraphs on columns. Such typesetting tricks might be interpreted by au tomated methods incorrectly as an actual table, while the human annotation will interpret it correctly as Text or other styles. The same applies to multi-line text elements, when authors decided to space them as “invisible” list elements without bullet symbols. A third reason to gather ground-truth through human annotation is to estimate a “natural” upper bound on the segmentation accuracy. As we will show in Section 4, certain documents featuring complex layouts can have different but equally acceptable layout interpre tations. This natural upper bound for segmentation accuracy can be found by annotating the same pages multiple times by different people and evaluating the inter-annotator agreement. Such a base line consistency evaluation is very useful to define expectations for a good target accuracy in trained deep neural network models and avoid overfitting (see Table 1). On the flip side, achieving high annotation consistency proved to be a key challenge in human annotation, as we outline in Section 4. +Despite being cost-intense and far less scalable than automation, human annotation has several benefits over automated groundtruth generation. The first and most obvious reason to leverage human annotations is the freedom to annotate any type of document without requiring a programmatic source. For most PDF documents, the original source document is not available. The latter is not a hard constraint with human annotation, but it is for automated methods. A second reason to use human annotations is that the latter usually provide a more natural interpretation of the page layout. The human-interpreted layout can significantly deviate from the programmatic layout used in typesetting. For example, "invisible" tables might be used solely for aligning text paragraphs on columns. Such typesetting tricks might be interpreted by automated methods incorrectly as an actual table, while the human annotation will interpret it correctly as Text or other styles. The same applies to multi-line text elements, when authors decided to space them as "invisible" list elements without bullet symbols. A third reason to gather ground-truth through human annotation is to estimate a "natural" upper bound on the segmentation accuracy. As we will show in Section 4, certain documents featuring complex layouts can have different but equally acceptable layout interpretations. This natural upper bound for segmentation accuracy can be found by annotating the same pages multiple times by different people and evaluating the inter-annotator agreement. Such a baseline consistency evaluation is very useful to define expectations for a good target accuracy in trained deep neural network models and avoid overfitting (see Table 1). On the flip side, achieving high annotation consistency proved to be a key challenge in human annotation, as we outline in Section 4. ## 4 ANNOTATION CAMPAIGN -The annotation campaign was carried out in four phases. In phase one, we identified and prepared the data sources for annotation. In phase two, we determined the class labels and how annotations should be done on the documents in order to obtain maximum con sistency. The latter was guided by a detailed requirement analysis and exhaustive experiments. In phase three, we trained the annota tion staff and performed exams for quality assurance. In phase four, +The annotation campaign was carried out in four phases. In phase one, we identified and prepared the data sources for annotation. In phase two, we determined the class labels and how annotations should be done on the documents in order to obtain maximum consistency. The latter was guided by a detailed requirement analysis and exhaustive experiments. In phase three, we trained the annotation staff and performed exams for quality assurance. In phase four, -Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row “Total”) in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges. +Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row "Total") in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges. | | | | | | % of Total | | triple inter-annotator mAP @ 0.5-0.95 (%) | |----------------|---------|-------|--------------------|-------------------|--------------|--------------------------------------------|---------------------------------------------| @@ -130,44 +130,44 @@ Table 1: DocLayNet dataset overview. Along with the frequency of each class labe include publication repositories such as arXiv 3 , government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process. -Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [ 22 ], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation in terface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by se lective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains. +Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [ 22 ], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains. -Phase 2: Label selection and guideline. We reviewed the col lected documents and identified the most common structural fea tures they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption Footnote Formula List-item Page , , , , footer, r, Page-header, r, Picture , Section-header, r, Table , Text, t, and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on +Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption Footnote Formula List-item Page, , , , footer, r, Page-header, r, Picture , Section-header, r, Table , Text, t, and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on -Figure 3: Corpus Conversion Service annotation user inter face. The PDF page is shown in the background, with over laid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right. +Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right. we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised. -Phase 1: Data selection and preparation. Our inclusion cri teria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources +Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources 3 https://arxiv.org/ the textual content of an element, which goes beyond visual layout recognition, in particular outside the Scientific Articles category. -At first sight, the task of visual document-layout interpretation appears intuitive enough to obtain plausible annotations in most cases. However, during early trial-runs in the core team, we ob served many cases in which annotators use different annotation styles, especially for documents with challenging layouts. For ex ample, if a figure is presented with subfigures, one annotator might draw a single figure bounding-box, while another might annotate each subfigure separately. The same applies for lists, where one might annotate all list items in one block or each list item sep arately. In essence, we observed that challenging layouts would be annotated in different but plausible ways. To illustrate this, we show in Figure 4 multiple examples of plausible but inconsistent annotations on the same pages. +At first sight, the task of visual document-layout interpretation appears intuitive enough to obtain plausible annotations in most cases. However, during early trial-runs in the core team, we observed many cases in which annotators use different annotation styles, especially for documents with challenging layouts. For example, if a figure is presented with subfigures, one annotator might draw a single figure bounding-box, while another might annotate each subfigure separately. The same applies for lists, where one might annotate all list items in one block or each list item separately. In essence, we observed that challenging layouts would be annotated in different but plausible ways. To illustrate this, we show in Figure 4 multiple examples of plausible but inconsistent annotations on the same pages. -Obviously, this inconsistency in annotations is not desirable for datasets which are intended to be used for model training. To min imise these inconsistencies, we created a detailed annotation guide line. While perfect consistency across 40 annotation staff members is clearly not possible to achieve, we saw a huge improvement in annotation consistency after the introduction of our annotation guideline. A few selected, non-trivial highlights of the guideline are: +Obviously, this inconsistency in annotations is not desirable for datasets which are intended to be used for model training. To minimise these inconsistencies, we created a detailed annotation guideline. While perfect consistency across 40 annotation staff members is clearly not possible to achieve, we saw a huge improvement in annotation consistency after the introduction of our annotation guideline. A few selected, non-trivial highlights of the guideline are: - (1) Every list-item is an individual object instance with class label List-item . This definition is different from PubLayNet and DocBank, where all list-items are grouped together into object. one List -- (2) A List-item is a paragraph with hanging indentation. Single line elements can qualify as List-item if the neighbour ele ments expose hanging indentation. Bullet or enumeration symbols are not a requirement. +- (2) A List-item is a paragraph with hanging indentation. Singleline elements can qualify as List-item if the neighbour elements expose hanging indentation. Bullet or enumeration symbols are not a requirement. - (3) For every Caption , there must be exactly one corresponding Picture Table or . - (4) Connected sub-pictures are grouped together in one Picture object. - (5) Formula numbers are included in a Formula object. - (6) Emphasised text (e.g. in italic or bold) at the beginning of a paragraph is not considered a Section-header, r, unless it appears exclusively on its own line. -The complete annotation guideline is over 100 pages long and a detailed description is obviously out of scope for this paper. Never theless, it will be made publicly available alongside with DocLayNet for future reference. +The complete annotation guideline is over 100 pages long and a detailed description is obviously out of scope for this paper. Nevertheless, it will be made publicly available alongside with DocLayNet for future reference. -Phase 3: Training. After a first trial with a small group of peo ple, we realised that providing the annotation guideline and a set of random practice pages did not yield the desired quality level for lay out annotation. Therefore we prepared a subset of pages with two different complexity levels, each with a practice and an exam part. 974 pages were reference-annotated by one proficient core team member. Annotation staff were then given the task to annotate the same subsets (blinded from the reference). By comparing the an notations of each staff member with the reference annotations, we could quantify how closely their annotations matched the reference. Only after passing two exam levels with high annotation quality, staff were admitted into the production phase. Practice iterations +Phase 3: Training. After a first trial with a small group of people, we realised that providing the annotation guideline and a set of random practice pages did not yield the desired quality level for layout annotation. Therefore we prepared a subset of pages with two different complexity levels, each with a practice and an exam part. 974 pages were reference-annotated by one proficient core team member. Annotation staff were then given the task to annotate the same subsets (blinded from the reference). By comparing the annotations of each staff member with the reference annotations, we could quantify how closely their annotations matched the reference. Only after passing two exam levels with high annotation quality, staff were admitted into the production phase. Practice iterations -Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can re solve cases A to C, while the case D remains ambiguous. +Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous. were carried out over a timeframe of 12 weeks, after which 8 of the 40 initially allocated annotators did not pass the bar. -Phase 4: Production annotation. The previously selected 80K pages were annotated with the defined 11 class labels by 32 annota tors. This production phase took around three months to complete. All annotations were created online through CCS, which visualises the programmatic PDF text-cells as an overlay on the page. The page annotation are obtained by drawing rectangular bounding-boxes, as shown in Figure 3. With regard to the annotation practices, we implemented a few constraints and capabilities on the tooling level. First, we only allow non-overlapping, vertically oriented, rectangu lar boxes. For the large majority of documents, this constraint was sufficient and it speeds up the annotation considerably in compar ison with arbitrary segmentation shapes. Second, annotator staff were not able to see each other’s annotations. This was enforced by design to avoid any bias in the annotation, which could skew the numbers of the inter-annotator agreement (see Table 1). We wanted +Phase 4: Production annotation. The previously selected 80K pages were annotated with the defined 11 class labels by 32 annotators. This production phase took around three months to complete. All annotations were created online through CCS, which visualises the programmatic PDF text-cells as an overlay on the page. The page annotation are obtained by drawing rectangular bounding-boxes, as shown in Figure 3. With regard to the annotation practices, we implemented a few constraints and capabilities on the tooling level. First, we only allow non-overlapping, vertically oriented, rectangular boxes. For the large majority of documents, this constraint was sufficient and it speeds up the annotation considerably in comparison with arbitrary segmentation shapes. Second, annotator staff were not able to see each other's annotations. This was enforced by design to avoid any bias in the annotation, which could skew the numbers of the inter-annotator agreement (see Table 1). We wanted -Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on DocLayNet test set. The MRCNN (Mask R-CNN) and FRCNN (Faster R-CNN) models with ResNet-50 or ResNet-101 backbone were trained based on the network architectures from the detectron2 model zoo (Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN 3x), with default configurations. The YOLO implementation utilized was YOLOv5x6 [13]. All models were initialised us ing pre-trained weights from the COCO 2017 dataset. +Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on DocLayNet test set. The MRCNN (Mask R-CNN) and FRCNN (Faster R-CNN) models with ResNet-50 or ResNet-101 backbone were trained based on the network architectures from the detectron2 model zoo (Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN 3x), with default configurations. The YOLO implementation utilized was YOLOv5x6 [13]. All models were initialised using pre-trained weights from the COCO 2017 dataset. | | human | MRCNN FRCNN YOLO | |----------------|---------|---------------------| @@ -185,7 +185,7 @@ Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on D | Title | 60-72 | 76.7 80.4 79.9 82.7 | | All | 82-83 | 72.4 73.5 73.4 76.8 | -to avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we in troduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture . For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity. +to avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture . For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity. ## 5 EXPERIMENTS @@ -193,7 +193,7 @@ The primary goal of DocLayNet is to obtain high-quality ML models capable of acc -Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNN network with ResNet50 backbone trained on increas ing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions. +Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNN network with ResNet50 backbone trained on increasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions. paper and leave the detailed evaluation of more recent methods mentioned in Section 2 for future work. @@ -203,7 +203,7 @@ In this section, we will present several aspects related to the performance of o In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [ 12 ], Faster R-CNN [ 11 ], and YOLOv5 [ 13 ]. Both training and evaluation were performed on RGB images with dimensions of 1025 × 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text, t, Table and Picture . This is not entirely surprising, as Text, Table and Picture are abundant and the most visually distinctive in a document. t, -Table 3: Performance of a Mask R-CNN R50 network in mAP@0.5-0.95 scores trained on DocLayNet with different class label sets. The reduced label sets were obtained by ei ther down-mapping or dropping labels. +Table 3: Performance of a Mask R-CNN R50 network in mAP@0.5-0.95 scores trained on DocLayNet with different class label sets. The reduced label sets were obtained by either down-mapping or dropping labels. | Class-count | 11 6 5 4 | |----------------|----------------------------| @@ -222,13 +222,13 @@ Table 3: Performance of a Mask R-CNN R50 network in mAP@0.5-0.95 scores trained ## Learning Curve -One of the fundamental questions related to any dataset is if it is “large enough”. To answer this question for DocLayNet, we per formed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the be ginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would prob ably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [ ], or the addition of 23 more document categories and styles. +One of the fundamental questions related to any dataset is if it is "large enough". To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [ ], or the addition of 23 more document categories and styles. ## Impact of Class Labels -The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption Text ) or excluding them → from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data be fore model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of +The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption Text ) or excluding them → from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of -Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. Naive page-wise split will result in ~ 10% point improve ment. +Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. Naive page-wise split will result in ~ 10% point improvement. | Class-count | | 11 | | 5 | |----------------|----------|------|----------|-----| @@ -250,15 +250,15 @@ lists in PubLayNet (grouped list-items) versus DocLayNet (separate list-items), ## Impact of Document Split in Train and Test Set -Many documents in DocLayNet have a unique styling. In order to avoid overfitting on a particular style, we have split the train-, test- and validation-sets of DocLayNet on document boundaries, i.e. every document contributes pages to only one set. To the best of our knowledge, this was not considered in PubLayNet or DocBank. To quantify how this affects model performance, we trained and evaluated a Mask R-CNN R50 model on a modified dataset version. Here, the train-, test- and validation-sets were obtained by a ran domised draw over the individual pages. As can be seen in Table 4, the difference in model performance is surprisingly large: page wise splitting gains 10% in mAP over the document-wise splitting. ˜ Thus, random page-wise splitting of DocLayNet can easily lead to accidental overestimation of model performance and should be avoided. +Many documents in DocLayNet have a unique styling. In order to avoid overfitting on a particular style, we have split the train-, test- and validation-sets of DocLayNet on document boundaries, i.e. every document contributes pages to only one set. To the best of our knowledge, this was not considered in PubLayNet or DocBank. To quantify how this affects model performance, we trained and evaluated a Mask R-CNN R50 model on a modified dataset version. Here, the train-, test- and validation-sets were obtained by a randomised draw over the individual pages. As can be seen in Table 4, the difference in model performance is surprisingly large: pagewise splitting gains 10% in mAP over the document-wise splitting. ˜ Thus, random page-wise splitting of DocLayNet can easily lead to accidental overestimation of model performance and should be avoided. ## Dataset Comparison -Throughout this paper, we claim that DocLayNet’s wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture , +Throughout this paper, we claim that DocLayNet's wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture , -KDD ’22, August 14–18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar +KDD '22, August 14–18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar -Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network across the PubLayNet, DocBank & Do cLayNet data-sets. By evaluating on common label classes of each dataset, we observe that the DocLayNet-trained model has much less pronounced variations in performance across all datasets. +Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network across the PubLayNet, DocBank & DocLayNet data-sets. By evaluating on common label classes of each dataset, we observe that the DocLayNet-trained model has much less pronounced variations in performance across all datasets. | | | | | Testing on | |-----------------|-------------|------------|------------|--------------| @@ -277,32 +277,32 @@ Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network acros | | | Text | 77 - 84 | | | | | total | 59 47 78 | | -Section-header, Table and Text. t. Before training, we either mapped r, or excluded DocLayNet’s other labels as specified in table 3, and also PubLayNet’s List to Text. t. Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text. t. +Section-header, Table and Text. t. Before training, we either mapped r, or excluded DocLayNet's other labels as specified in table 3, and also PubLayNet's List to Text. t. Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text. t. -For comparison of DocBank with DocLayNet, we trained only and Table clusters of each dataset. We had to exclude on Picture Text because successive paragraphs are often grouped together into a single object in DocBank. This paragraph grouping is incompatible with the individual paragraphs of DocLayNet. As can be seen in Table 5, DocLayNet trained models yield better performance com pared to the previous datasets. It is noteworthy that the models trained on PubLayNet and DocBank perform very well on their own test set, but have a much lower performance on the foreign datasets. While this also applies to DocLayNet, the difference is far less pronounced. Thus we conclude that DocLayNet trained models are overall more robust and will produce better results for challenging, unseen layouts. +For comparison of DocBank with DocLayNet, we trained only and Table clusters of each dataset. We had to exclude on Picture Text because successive paragraphs are often grouped together into a single object in DocBank. This paragraph grouping is incompatible with the individual paragraphs of DocLayNet. As can be seen in Table 5, DocLayNet trained models yield better performance compared to the previous datasets. It is noteworthy that the models trained on PubLayNet and DocBank perform very well on their own test set, but have a much lower performance on the foreign datasets. While this also applies to DocLayNet, the difference is far less pronounced. Thus we conclude that DocLayNet trained models are overall more robust and will produce better results for challenging, unseen layouts. ## Example Predictions -To conclude this section, we illustrate the quality of layout predic tions one can expect from DocLayNet-trained models by providing a selection of examples without any further post-processing ap plied. Figure 6 shows selected layout predictions on pages from the test-set of DocLayNet. Results look decent in general across docu ment categories, however one can also observe mistakes such as overlapping clusters of different classes, or entirely missing boxes due to low confidence. +To conclude this section, we illustrate the quality of layout predictions one can expect from DocLayNet-trained models by providing a selection of examples without any further post-processing applied. Figure 6 shows selected layout predictions on pages from the test-set of DocLayNet. Results look decent in general across document categories, however one can also observe mistakes such as overlapping clusters of different classes, or entirely missing boxes due to low confidence. ## 6 CONCLUSION -In this paper, we presented the DocLayNet dataset. It provides the document conversion and layout analysis research community a new and challenging dataset to improve and fine-tune novel ML methods on. In contrast to many other datasets, DocLayNet was created by human annotation in order to obtain reliable layout ground-truth on a wide variety of publication- and typesetting styles. Including a large proportion of documents outside the scien tific publishing domain adds significant value in this respect. +In this paper, we presented the DocLayNet dataset. It provides the document conversion and layout analysis research community a new and challenging dataset to improve and fine-tune novel ML methods on. In contrast to many other datasets, DocLayNet was created by human annotation in order to obtain reliable layout ground-truth on a wide variety of publication- and typesettingstyles. Including a large proportion of documents outside the scientific publishing domain adds significant value in this respect. -From the dataset, we have derived on the one hand reference metrics for human performance on document-layout annotation (through double and triple annotations) and on the other hand eval uated the baseline performance of commonly used object detection methods. We also illustrated the impact of various dataset-related aspects on model performance through data-ablation experiments, both from a size and class-label perspective. Last but not least, we compared the accuracy of models trained on other public datasets and showed that DocLayNet trained models are more robust. +From the dataset, we have derived on the one hand reference metrics for human performance on document-layout annotation (through double and triple annotations) and on the other hand evaluated the baseline performance of commonly used object detection methods. We also illustrated the impact of various dataset-related aspects on model performance through data-ablation experiments, both from a size and class-label perspective. Last but not least, we compared the accuracy of models trained on other public datasets and showed that DocLayNet trained models are more robust. To date, there is still a significant gap between human and ML accuracy on the layout interpretation task, and we hope that this work will inspire the research community to close that gap. ## REFERENCES - [1] Max Göbel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. 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IEEE Transactions on Pattern Analysis and Machine Intelligence , 39(6):1137–1149, 2017. @@ -313,18 +313,18 @@ To date, there is still a significant gap between human and ML accuracy on the l Text Caption List Item Formula Table Picture Section Header Page Header Page Footer Title -Diaconu, Mai Thanh Minh, Marc, albinxavi, fatih, oleg, and wanghao yang. ul tralytics/yolov5: v6.0 - yolov5n nano models, roboflow integration, tensorflow export, opencv dnn support, October 2021. +Diaconu, Mai Thanh Minh, Marc, albinxavi, fatih, oleg, and wanghao yang. ultralytics/yolov5: v6.0 - yolov5n nano models, roboflow integration, tensorflow export, opencv dnn support, October 2021. - [14] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. CoRR , abs/2005.12872, 2020. - [15] Mingxing Tan, Ruoming Pang, and Quoc V. Le. Efficientdet: Scalable and efficient object detection. CoRR , abs/1911.09070, 2019. -- [16] Tsung-Yi Lin, Michael Maire, Serge J. Belongie, Lubomir D. Bourdev, Ross B. Gir shick, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick. Microsoft COCO: common objects in context, 2014. +- [16] Tsung-Yi Lin, Michael Maire, Serge J. Belongie, Lubomir D. Bourdev, Ross B. Girshick, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick. Microsoft COCO: common objects in context, 2014. - [17] Yuxin Wu, Alexander Kirillov, Francisco Massa, Wan-Yen Lo, and Ross Girshick. Detectron2, 2019. - [18] Nikolaos Livathinos, Cesar Berrospi, Maksym Lysak, Viktor Kuropiatnyk, Ahmed Nassar, Andre Carvalho, Michele Dolfi, Christoph Auer, Kasper Dinkla, and Peter W. J. Staar. Robust pdf document conversion using recurrent neural networks. In Proceedings of the 35th Conference on Artificial Intelligence , AAAI, pages 15137– 15145, feb 2021. -- [19] Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, and Ming Zhou. Layoutlm: Pre-training of text and layout for document image understanding. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , KDD, pages 1192–1200, New York, USA, 2020. Asso ciation for Computing Machinery. +- [19] Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, and Ming Zhou. Layoutlm: Pre-training of text and layout for document image understanding. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , KDD, pages 1192–1200, New York, USA, 2020. Association for Computing Machinery. -Figure 6: Example layout predictions on selected pages from the DocLayNet test-set. (A, D) exhibit favourable results on coloured backgrounds. (B, C) show accurate list-item and paragraph differentiation despite densely-spaced lines. (E) demon strates good table and figure distinction. (F) shows predictions on a Chinese patent with multiple overlaps, label confusion and missing boxes. +Figure 6: Example layout predictions on selected pages from the DocLayNet test-set. (A, D) exhibit favourable results on coloured backgrounds. (B, C) show accurate list-item and paragraph differentiation despite densely-spaced lines. (E) demonstrates good table and figure distinction. (F) shows predictions on a Chinese patent with multiple overlaps, label confusion and missing boxes. - [20] Shoubin Li, Xuyan Ma, Shuaiqun Pan, Jun Hu, Lin Shi, and Qing Wang. Vtlayout: Fusion of visual and text features for document layout analysis, 2021. - [21] Peng Zhang, Can Li, Liang Qiao, Zhanzhan Cheng, Shiliang Pu, Yi Niu, and Fei Wu. Vsr: A unified framework for document layout analysis combining vision, semantics and relations, 2021. - [22] Peter W J Staar, Michele Dolfi, Christoph Auer, and Costas Bekas. Corpus conversion service: A machine learning platform to ingest documents at scale. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , KDD, pages 774–782. ACM, 2018. -- [23] Connor Shorten and Taghi M. Khoshgoftaar. A survey on image data augmenta tion for deep learning. Journal of Big Data , 6(1):60, 2019. +- [23] Connor Shorten and Taghi M. Khoshgoftaar. A survey on image data augmentation for deep learning. Journal of Big Data , 6(1):60, 2019. diff --git a/tests/pdf_snapshots/pdf/sources/2305.03393v1-pg9.pdf.md b/tests/pdf_snapshots/pdf/sources/2305.03393v1-pg9.pdf.md index c6a477f6..e85f892e 100644 --- a/tests/pdf_snapshots/pdf/sources/2305.03393v1-pg9.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/2305.03393v1-pg9.pdf.md @@ -4,7 +4,7 @@ order to compute the TED score. Inference timing results for all experiments wer We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML. -Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef fects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart. +Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart. | | # | | # | | | TEDs | | mAP | Inference | | |------------|-----|------------|-----|----------|--------|---------|-------|--------|-------------|------| diff --git a/tests/pdf_snapshots/pdf/sources/2305.03393v1.pdf.md b/tests/pdf_snapshots/pdf/sources/2305.03393v1.pdf.md index d19b1793..6ef03cf7 100644 --- a/tests/pdf_snapshots/pdf/sources/2305.03393v1.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/2305.03393v1.pdf.md @@ -1,38 +1,38 @@ -# Optimized Table Tokenization for Table Structure Recognition +## Optimized Table Tokenization for Table Structure Recognition Maksym Lysak [0000 − 0002 − 3723 − 6960] , Ahmed Nassar [0000 − 0002 − 9468 − 0822] Nikolaos Livathinos [0000 − 0001 − 8513 − 3491] , Christoph Auer [0000 − 0001 − 5761 − 0422] , [0000 0002 8088 0823] , and Peter Staar − − − IBM Research {mly,ahn,nli,cau,taa}@zurich.ibm.com -Abstract. Extracting tables from documents is a crucial task in any document conversion pipeline. Recently, transformer-based models have demonstrated that table-structure can be recognized with impressive ac curacy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking only the image of a table, such models predict a sequence of tokens (e.g. in HTML, LaTeX) which represent the structure of the table. Since the token representation of the table structure has a significant impact on the accuracy and run-time performance of any Im2Seq model, we inves tigate in this paper how table-structure representation can be optimised. We propose a new, optimised table-structure language (OTSL) with a minimized vocabulary and specific rules. The benefits of OTSL are that it reduces the number of tokens to 5 (HTML needs 28+) and shortens the sequence length to half of HTML on average. Consequently, model accuracy improves significantly, inference time is halved compared to HTML-based models, and the predicted table structures are always syn tactically correct. This in turn eliminates most post-processing needs. Popular table structure data-sets will be published in OTSL format to the community. +Abstract. Extracting tables from documents is a crucial task in any document conversion pipeline. Recently, transformer-based models have demonstrated that table-structure can be recognized with impressive accuracy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking only the image of a table, such models predict a sequence of tokens (e.g. in HTML, LaTeX) which represent the structure of the table. Since the token representation of the table structure has a significant impact on the accuracy and run-time performance of any Im2Seq model, we investigate in this paper how table-structure representation can be optimised. We propose a new, optimised table-structure language (OTSL) with a minimized vocabulary and specific rules. The benefits of OTSL are that it reduces the number of tokens to 5 (HTML needs 28+) and shortens the sequence length to half of HTML on average. Consequently, model accuracy improves significantly, inference time is halved compared to HTML-based models, and the predicted table structures are always syntactically correct. This in turn eliminates most post-processing needs. Popular table structure data-sets will be published in OTSL format to the community. -Keywords: Table Structure Recognition · Data Representation · Trans formers · Optimization. +Keywords: Table Structure Recognition · Data Representation · Transformers · Optimization. ## 1 Introduction -Tables are ubiquitous in documents such as scientific papers, patents, reports, manuals, specification sheets or marketing material. They often encode highly valuable information and therefore need to be extracted with high accuracy. Unfortunately, tables appear in documents in various sizes, styling and struc ture, making it difficult to recover their correct structure with simple analyt ical methods. Therefore, accurate table extraction is achieved these days with machine-learning based methods. +Tables are ubiquitous in documents such as scientific papers, patents, reports, manuals, specification sheets or marketing material. They often encode highly valuable information and therefore need to be extracted with high accuracy. Unfortunately, tables appear in documents in various sizes, styling and structure, making it difficult to recover their correct structure with simple analytical methods. Therefore, accurate table extraction is achieved these days with machine-learning based methods. -In modern document understanding systems [1,15], table extraction is typi cally a two-step process. Firstly, every table on a page is located with a bounding box, and secondly, their logical row and column structure is recognized. As of +In modern document understanding systems [1,15], table extraction is typically a two-step process. Firstly, every table on a page is located with a bounding box, and secondly, their logical row and column structure is recognized. As of Fig. 1. Comparison between HTML and OTSL table structure representation: (A) table-example with complex row and column headers, including a 2D empty span, (B) minimal graphical representation of table structure using rectangular layout, (C) HTML representation, (D) OTSL representation. This example demonstrates many of the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case), its reduced sequence length (55 versus 30) and a enhanced internal structure (variable token sequence length per row in HTML versus a fixed length of rows in OTSL). -today, table detection in documents is a well understood problem, and the latest state-of-the-art (SOTA) object detection methods provide an accuracy compa rable to human observers [7,8,10,14,23]. On the other hand, the problem of table structure recognition (TSR) is a lot more challenging and remains a very active area of research, in which many novel machine learning algorithms are being explored [3,4,5,9,11,12,13,14,17,18,21,22]. +today, table detection in documents is a well understood problem, and the latest state-of-the-art (SOTA) object detection methods provide an accuracy comparable to human observers [7,8,10,14,23]. On the other hand, the problem of table structure recognition (TSR) is a lot more challenging and remains a very active area of research, in which many novel machine learning algorithms are being explored [3,4,5,9,11,12,13,14,17,18,21,22]. -Recently emerging SOTA methods for table structure recognition employ transformer-based models, in which an image of the table is provided to the net work in order to predict the structure of the table as a sequence of tokens. These image-to-sequence (Im2Seq) models are extremely powerful, since they allow for a purely data-driven solution. The tokens of the sequence typically belong to a markup language such as HTML, Latex or Markdown, which allow to describe table structure as rows, columns and spanning cells in various configurations. In Figure 1, we illustrate how HTML is used to represent the table-structure of a particular example table. Public table-structure data sets such as PubTab Net [22], and FinTabNet [21], which were created in a semi-automated way from paired PDF and HTML sources (e.g. PubMed Central), popularized primarily the use of HTML as ground-truth representation format for TSR. +Recently emerging SOTA methods for table structure recognition employ transformer-based models, in which an image of the table is provided to the network in order to predict the structure of the table as a sequence of tokens. These image-to-sequence (Im2Seq) models are extremely powerful, since they allow for a purely data-driven solution. The tokens of the sequence typically belong to a markup language such as HTML, Latex or Markdown, which allow to describe table structure as rows, columns and spanning cells in various configurations. In Figure 1, we illustrate how HTML is used to represent the table-structure of a particular example table. Public table-structure data sets such as PubTabNet [22], and FinTabNet [21], which were created in a semi-automated way from paired PDF and HTML sources (e.g. PubMed Central), popularized primarily the use of HTML as ground-truth representation format for TSR. -While the majority of research in TSR is currently focused on the develop ment and application of novel neural model architectures, the table structure representation language (e.g. HTML in PubTabNet and FinTabNet) is usually adopted as is for the sequence tokenization in Im2Seq models. In this paper, we aim for the opposite and investigate the impact of the table structure rep resentation language with an otherwise unmodified Im2Seq transformer-based architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9], we select this model to perform our experiments. +While the majority of research in TSR is currently focused on the development and application of novel neural model architectures, the table structure representation language (e.g. HTML in PubTabNet and FinTabNet) is usually adopted as is for the sequence tokenization in Im2Seq models. In this paper, we aim for the opposite and investigate the impact of the table structure representation language with an otherwise unmodified Im2Seq transformer-based architecture. Since the current state-of-the-art Im2Seq model is TableFormer [9], we select this model to perform our experiments. -The main contribution of this paper is the introduction of a new optimised ta ble structure language (OTSL), specifically designed to describe table-structure in an compact and structured way for Im2Seq models. OTSL has a number of key features, which make it very attractive to use in Im2Seq models. Specifically, compared to other languages such as HTML, OTSL has a minimized vocabulary which yields short sequence length, strong inherent structure (e.g. strict rectan gular layout) and a strict syntax with rules that only look backwards. The latter allows for syntax validation during inference and ensures a syntactically correct table-structure. These OTSL features are illustrated in Figure 1, in comparison to HTML. +The main contribution of this paper is the introduction of a new optimised table structure language (OTSL), specifically designed to describe table-structure in an compact and structured way for Im2Seq models. OTSL has a number of key features, which make it very attractive to use in Im2Seq models. Specifically, compared to other languages such as HTML, OTSL has a minimized vocabulary which yields short sequence length, strong inherent structure (e.g. strict rectangular layout) and a strict syntax with rules that only look backwards. The latter allows for syntax validation during inference and ensures a syntactically correct table-structure. These OTSL features are illustrated in Figure 1, in comparison to HTML. -The paper is structured as follows. In section 2, we give an overview of the latest developments in table-structure reconstruction. In section 3 we review the current HTML table encoding (popularised by PubTabNet and FinTabNet) and discuss its flaws. Subsequently, we introduce OTSL in section 4, which in cludes the language definition, syntax rules and error-correction procedures. In section 5, we apply OTSL on the TableFormer architecture, compare it to Table Former models trained on HTML and ultimately demonstrate the advantages of using OTSL. Finally, in section 6 we conclude our work and outline next potential steps. +The paper is structured as follows. In section 2, we give an overview of the latest developments in table-structure reconstruction. In section 3 we review the current HTML table encoding (popularised by PubTabNet and FinTabNet) and discuss its flaws. Subsequently, we introduce OTSL in section 4, which includes the language definition, syntax rules and error-correction procedures. In section 5, we apply OTSL on the TableFormer architecture, compare it to TableFormer models trained on HTML and ultimately demonstrate the advantages of using OTSL. Finally, in section 6 we conclude our work and outline next potential steps. ## 2 Related Work -Approaches to formalize the logical structure and layout of tables in electronic documents date back more than two decades [16]. In the recent past, a wide variety of computer vision methods have been explored to tackle the prob lem of table structure recognition, i.e. the correct identification of columns, rows and spanning cells in a given table. Broadly speaking, the current deep learning based approaches fall into three categories: object detection (OD) meth ods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence (Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on table structure annotation using (overlapping) bounding boxes for training, and pro duce bounding-box predictions to define table cells, rows, and columns on a table image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name suggests, represent tables as graph structures. The graph nodes represent the content of each table cell, an embedding vector from the table image, or geomet ric coordinates of the table cell. The edges of the graph define the relationship between the nodes, e.g. if they belong to the same column, row, or table cell. +Approaches to formalize the logical structure and layout of tables in electronic documents date back more than two decades [16]. In the recent past, a wide variety of computer vision methods have been explored to tackle the problem of table structure recognition, i.e. the correct identification of columns, rows and spanning cells in a given table. Broadly speaking, the current deeplearning based approaches fall into three categories: object detection (OD) methods, Graph-Neural-Network (GNN) methods and Image-to-Markup-Sequence (Im2Seq) methods. Object-detection based methods [11,12,13,14,21] rely on tablestructure annotation using (overlapping) bounding boxes for training, and produce bounding-box predictions to define table cells, rows, and columns on a table image. Graph Neural Network (GNN) based methods [3,6,17,18], as the name suggests, represent tables as graph structures. The graph nodes represent the content of each table cell, an embedding vector from the table image, or geometric coordinates of the table cell. The edges of the graph define the relationship between the nodes, e.g. if they belong to the same column, row, or table cell. -Other work [20] aims at predicting a grid for each table and deciding which cells must be merged using an attention network. Im2Seq methods cast the problem as a sequence generation task [4,5,9,22], and therefore need an internal table structure representation language, which is often implemented with standard markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods have a natural advantage over the OD and GNN methods by virtue of directly predicting the table-structure. As such, no post-processing or rules are needed in order to obtain the table-structure, which is necessary with OD and GNN approaches. In practice, this is not entirely true, because a predicted sequence of table-structure markup does not necessarily have to be syntactically correct. Hence, depending on the quality of the predicted sequence, some post-processing needs to be performed to ensure a syntactically valid (let alone correct) sequence. +Other work [20] aims at predicting a grid for each table and deciding which cells must be merged using an attention network. Im2Seq methods cast the problem as a sequence generation task [4,5,9,22], and therefore need an internal tablestructure representation language, which is often implemented with standard markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods have a natural advantage over the OD and GNN methods by virtue of directly predicting the table-structure. As such, no post-processing or rules are needed in order to obtain the table-structure, which is necessary with OD and GNN approaches. In practice, this is not entirely true, because a predicted sequence of table-structure markup does not necessarily have to be syntactically correct. Hence, depending on the quality of the predicted sequence, some post-processing needs to be performed to ensure a syntactically valid (let alone correct) sequence. Within the Im2Seq method, we find several popular models, namely the encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders to predict a table in HTML representation. The tag decoder predicts a sequence of HTML tags. For each decoded table cell ( ), the attention is passed to the cell decoder to predict the content with an embedded OCR approach. The latter makes it susceptible to transcription errors in the cell content of the table. TableFormer address this reliance on OCR and uses two transformer decoders for HTML structure and cell bounding box prediction in an end-to-end architecture. The predicted cell bounding box is then used to extract text tokens from an originating (digital) PDF page, circumventing any need for OCR. TabSplitter [2] proposes a compact double-matrix representation of table rows and columns to do error detection and error correction of HTML structure sequences based on predictions from [19]. This compact double-matrix representation can not be used directly by the Img2seq model training, so the model uses HTML as an intermediate form. Chi et. al. [4] introduce a data set and a baseline method using bidirectional LSTMs to predict LaTeX code. Kayal [5] introduces Gated ResNet transformers to predict LaTeX code, and a separate OCR module to extract content. @@ -48,15 +48,15 @@ Fig. 2. Frequency of tokens in HTML and OTSL as they appear in PubTabNet. -Obviously, HTML and other general-purpose markup languages were not de signed for Im2Seq models. As such, they have some serious drawbacks. First, the token vocabulary needs to be artificially large in order to describe all plausible tabular structures. Since most Im2Seq models use an autoregressive approach, they generate the sequence token by token. Therefore, to reduce inference time, a shorter sequence length is critical. Every table-cell is represented by at least two tokens ( and ). Furthermore, when tokenizing the HTML struc ture, one needs to explicitly enumerate possible column-spans and row-spans as words. In practice, this ends up requiring 28 different HTML tokens (when including column- and row-spans up to 10 cells) just to describe every table in the PubTabNet dataset. Clearly, not every token is equally represented, as is depicted in Figure 2. This skewed distribution of tokens in combination with variable token row-length makes it challenging for models to learn the HTML structure. +Obviously, HTML and other general-purpose markup languages were not designed for Im2Seq models. As such, they have some serious drawbacks. First, the token vocabulary needs to be artificially large in order to describe all plausible tabular structures. Since most Im2Seq models use an autoregressive approach, they generate the sequence token by token. Therefore, to reduce inference time, a shorter sequence length is critical. Every table-cell is represented by at least two tokens ( and ). Furthermore, when tokenizing the HTML structure, one needs to explicitly enumerate possible column-spans and row-spans as words. In practice, this ends up requiring 28 different HTML tokens (when including column- and row-spans up to 10 cells) just to describe every table in the PubTabNet dataset. Clearly, not every token is equally represented, as is depicted in Figure 2. This skewed distribution of tokens in combination with variable token row-length makes it challenging for models to learn the HTML structure. Additionally, it would be desirable if the representation would easily allow an early detection of invalid sequences on-the-go, before the prediction of the entire table structure is completed. HTML is not well-suited for this purpose as the verification of incomplete sequences is non-trivial or even impossible. -In a valid HTML table, the token sequence must describe a 2D grid of table cells, serialised in row-major ordering, where each row and each column have the same length (while considering row- and column-spans). Furthermore, every opening tag in HTML needs to be matched by a closing tag in a correct hierar chical manner. Since the number of tokens for each table row and column can vary significantly, especially for large tables with many row- and column-spans, it is complex to verify the consistency of predicted structures during sequence +In a valid HTML table, the token sequence must describe a 2D grid of table cells, serialised in row-major ordering, where each row and each column have the same length (while considering row- and column-spans). Furthermore, every opening tag in HTML needs to be matched by a closing tag in a correct hierarchical manner. Since the number of tokens for each table row and column can vary significantly, especially for large tables with many row- and column-spans, it is complex to verify the consistency of predicted structures during sequence generation. Implicitly, this also means that Im2Seq models need to learn these complex syntax rules, simply to deliver valid output. -In practice, we observe two major issues with prediction quality when train ing Im2Seq models on HTML table structure generation from images. On the one hand, we find that on large tables, the visual attention of the model often starts to drift and is not accurately moving forward cell by cell anymore. This manifests itself in either in an increasing location drift for proposed table-cells in later rows on the same column or even complete loss of vertical alignment, as illustrated in Figure 5. Addressing this with post-processing is partially possible, but clearly undesired. On the other hand, we find many instances of predictions with structural inconsistencies or plain invalid HTML output, as shown in Fig ure 6, which are nearly impossible to properly correct. Both problems seriously impact the TSR model performance, since they reflect not only in the task of pure structure recognition but also in the equally crucial recognition or matching of table cell content. +In practice, we observe two major issues with prediction quality when training Im2Seq models on HTML table structure generation from images. On the one hand, we find that on large tables, the visual attention of the model often starts to drift and is not accurately moving forward cell by cell anymore. This manifests itself in either in an increasing location drift for proposed table-cells in later rows on the same column or even complete loss of vertical alignment, as illustrated in Figure 5. Addressing this with post-processing is partially possible, but clearly undesired. On the other hand, we find many instances of predictions with structural inconsistencies or plain invalid HTML output, as shown in Figure 6, which are nearly impossible to properly correct. Both problems seriously impact the TSR model performance, since they reflect not only in the task of pure structure recognition but also in the equally crucial recognition or matching of table cell content. ## 4 Optimised Table Structure Language @@ -76,7 +76,7 @@ The OTSL vocabulary is comprised of the following tokens: A notable attribute of OTSL is that it has the capability of achieving lossless conversion to HTML. -Fig. 3. OTSL description of table structure: A - table example; B - graphical repre sentation of table structure; C - mapping structure on a grid; D - OTSL structure encoding; E - explanation on cell encoding +Fig. 3. OTSL description of table structure: A - table example; B - graphical representation of table structure; C - mapping structure on a grid; D - OTSL structure encoding; E - explanation on cell encoding @@ -98,21 +98,21 @@ The application of these rules gives OTSL a set of unique properties. First of a These characteristics can be easily learned by sequence generator networks, as we demonstrate further below. We find strong indications that this pattern -reduces significantly the column drift seen in the HTML based models (see Fig ure 5). +reduces significantly the column drift seen in the HTML based models (see Figure 5). ## 4.3 Error-detection and -mitigation -The design of OTSL allows to validate a table structure easily on an unfinished sequence. The detection of an invalid sequence token is a clear indication of a prediction mistake, however a valid sequence by itself does not guarantee pre diction correctness. Different heuristics can be used to correct token errors in an invalid sequence and thus increase the chances for accurate predictions. Such heuristics can be applied either after the prediction of each token, or at the end on the entire predicted sequence. For example a simple heuristic which can cor rect the predicted OTSL sequence on-the-fly is to verify if the token with the highest prediction confidence invalidates the predicted sequence, and replace it by the token with the next highest confidence until OTSL rules are satisfied. +The design of OTSL allows to validate a table structure easily on an unfinished sequence. The detection of an invalid sequence token is a clear indication of a prediction mistake, however a valid sequence by itself does not guarantee prediction correctness. Different heuristics can be used to correct token errors in an invalid sequence and thus increase the chances for accurate predictions. Such heuristics can be applied either after the prediction of each token, or at the end on the entire predicted sequence. For example a simple heuristic which can correct the predicted OTSL sequence on-the-fly is to verify if the token with the highest prediction confidence invalidates the predicted sequence, and replace it by the token with the next highest confidence until OTSL rules are satisfied. ## 5 Experiments -To evaluate the impact of OTSL on prediction accuracy and inference times, we conducted a series of experiments based on the TableFormer model (Figure 4) with two objectives: Firstly we evaluate the prediction quality and performance of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on the canonical PubTabNet data set. Secondly we pick the best hyper-parameters found in the first step and evaluate how OTSL impacts the performance of TableFormer after training on other publicly available data sets (FinTabNet, PubTables-1M [14]). The ground truth (GT) from all data sets has been con verted into OTSL format for this purpose, and will be made publicly available. +To evaluate the impact of OTSL on prediction accuracy and inference times, we conducted a series of experiments based on the TableFormer model (Figure 4) with two objectives: Firstly we evaluate the prediction quality and performance of OTSL vs. HTML after performing Hyper Parameter Optimization (HPO) on the canonical PubTabNet data set. Secondly we pick the best hyper-parameters found in the first step and evaluate how OTSL impacts the performance of TableFormer after training on other publicly available data sets (FinTabNet, PubTables-1M [14]). The ground truth (GT) from all data sets has been converted into OTSL format for this purpose, and will be made publicly available. Fig. 4. Architecture sketch of the TableFormer model, which is a representative for the Im2Seq approach. -We rely on standard metrics such as Tree Edit Distance score (TEDs) for table structure prediction, and Mean Average Precision (mAP) with 0.75 Inter section Over Union (IOU) threshold for the bounding-box predictions of table cells. The predicted OTSL structures were converted back to HTML format in +We rely on standard metrics such as Tree Edit Distance score (TEDs) for table structure prediction, and Mean Average Precision (mAP) with 0.75 Intersection Over Union (IOU) threshold for the bounding-box predictions of table cells. The predicted OTSL structures were converted back to HTML format in order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz. @@ -120,7 +120,7 @@ order to compute the TED score. Inference timing results for all experiments wer We have chosen the PubTabNet data set to perform HPO, since it includes a highly diverse set of tables. Also we report TED scores separately for simple and complex tables (tables with cell spans). Results are presented in Table. 1. It is evident that with OTSL, our model achieves the same TED score and slightly better mAP scores in comparison to HTML. However OTSL yields a 2x speed up in the inference runtime over HTML. -Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Ef fects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart. +Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart. | | # | | # | | | TEDs | | mAP | Inference | | |------------|-----|------------|-----|----------|--------|---------|-------|--------|-------------|------| @@ -145,7 +145,7 @@ We picked the model parameter configuration that produced the best prediction qu Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation. -Table 2. TSR and cell detection results compared between OTSL and HTML on the PubTabNet [22], FinTabNet [21] and PubTables-1M [14] data sets using Table Former [9] (with enc=6, dec=6, heads=8). +Table 2. TSR and cell detection results compared between OTSL and HTML on the PubTabNet [22], FinTabNet [21] and PubTables-1M [14] data sets using TableFormer [9] (with enc=6, dec=6, heads=8). | | | | | TEDs | | | | Inference | | |--------------|----------|----------|--------|---------|-------|-----------|-------|-------------|------| @@ -163,27 +163,27 @@ Table 2. TSR and cell detection results compared between OTSL and HTML on the Pu ## 5.3 Qualitative Results -To illustrate the qualitative differences between OTSL and HTML, Figure 5 demonstrates less overlap and more accurate bounding boxes with OTSL. In Figure 6, OTSL proves to be more effective in handling tables with longer to ken sequences, resulting in even more precise structure prediction and bounding boxes. +To illustrate the qualitative differences between OTSL and HTML, Figure 5 demonstrates less overlap and more accurate bounding boxes with OTSL. In Figure 6, OTSL proves to be more effective in handling tables with longer token sequences, resulting in even more precise structure prediction and bounding boxes. -Fig. 5. The OTSL model produces more accurate bounding boxes with less over lap (E) than the HTML model (D), when predicting the structure of a sparse ta ble (A), at twice the inference speed because of shorter sequence length (B),(C). "PMC2807444_006_00.png" PubTabNet. μ +Fig. 5. The OTSL model produces more accurate bounding boxes with less overlap (E) than the HTML model (D), when predicting the structure of a sparse table (A), at twice the inference speed because of shorter sequence length (B),(C). "PMC2807444_006_00.png" PubTabNet. μ -Fig. 6. Visualization of predicted structure and detected bounding boxes on a complex table with many rows. The OTSL model (B) captured repeating pattern of horizontally merged cells from the GT (A), unlike the HTML model (C). The HTML model also didn’t complete the HTML sequence correctly and displayed a lot more of drift and overlap of bounding boxes. "PMC5406406_003_01.png" PubTabNet. +Fig. 6. Visualization of predicted structure and detected bounding boxes on a complex table with many rows. The OTSL model (B) captured repeating pattern of horizontally merged cells from the GT (A), unlike the HTML model (C). The HTML model also didn't complete the HTML sequence correctly and displayed a lot more of drift and overlap of bounding boxes. "PMC5406406_003_01.png" PubTabNet. ## 6 Conclusion -We demonstrated that representing tables in HTML for the task of table struc ture recognition with Im2Seq models is ill-suited and has serious limitations. Furthermore, we presented in this paper an Optimized Table Structure Language (OTSL) which, when compared to commonly used general purpose languages, has several key benefits. +We demonstrated that representing tables in HTML for the task of table structure recognition with Im2Seq models is ill-suited and has serious limitations. Furthermore, we presented in this paper an Optimized Table Structure Language (OTSL) which, when compared to commonly used general purpose languages, has several key benefits. First and foremost, given the same network configuration, inference time for a table-structure prediction is about 2 times faster compared to the conventional HTML approach. This is primarily owed to the shorter sequence length of the OTSL representation. Additional performance benefits can be obtained with HPO (hyper parameter optimization). As we demonstrate in our experiments, models trained on OTSL can be significantly smaller, e.g. by reducing the number of encoder and decoder layers, while preserving comparatively good prediction quality. This can further improve inference performance, yielding 5-6 times faster inference speed in OTSL with prediction quality comparable to models trained on HTML (see Table 1). -Secondly, OTSL has more inherent structure and a significantly restricted vo cabulary size. This allows autoregressive models to perform better in the TED metric, but especially with regards to prediction accuracy of the table-cell bound ing boxes (see Table 2). As shown in Figure 5, we observe that the OTSL dras tically reduces the drift for table cell bounding boxes at high row count and in sparse tables. This leads to more accurate predictions and a significant reduction in post-processing complexity, which is an undesired necessity in HTML-based Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few, simple and always backwards looking. Each new token can be validated only by analyzing the sequence of previous tokens, without requiring the entire sequence to detect mistakes. This in return allows to perform structural error detection and correction on-the-fly during sequence generation. +Secondly, OTSL has more inherent structure and a significantly restricted vocabulary size. This allows autoregressive models to perform better in the TED metric, but especially with regards to prediction accuracy of the table-cell bounding boxes (see Table 2). As shown in Figure 5, we observe that the OTSL drastically reduces the drift for table cell bounding boxes at high row count and in sparse tables. This leads to more accurate predictions and a significant reduction in post-processing complexity, which is an undesired necessity in HTML-based Im2Seq models. Significant novelty lies in OTSL syntactical rules, which are few, simple and always backwards looking. Each new token can be validated only by analyzing the sequence of previous tokens, without requiring the entire sequence to detect mistakes. This in return allows to perform structural error detection and correction on-the-fly during sequence generation. ## References -- 1. Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering doc ument conversion as a cloud service with high throughput and responsiveness. CoRR abs/2206.00785 (2022). https://doi.org/10.48550/arXiv.2206.00785 , https://doi.org/10.48550/arXiv.2206.00785 +- 1. Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering document conversion as a cloud service with high throughput and responsiveness. CoRR abs/2206.00785 (2022). https://doi.org/10.48550/arXiv.2206.00785 , https://doi.org/10.48550/arXiv.2206.00785 - 2. Chen, B., Peng, D., Zhang, J., Ren, Y., Jin, L.: Complex table structure recognition in the wild using transformer and identity matrix-based augmentation. In: Porwal, U., Fornés, A., Shafait, F. (eds.) Frontiers in Handwriting Recognition. pp. 545– 561. Springer International Publishing, Cham (2022) - 3. Chi, Z., Huang, H., Xu, H.D., Yu, H., Yin, W., Mao, X.L.: Complicated table structure recognition. arXiv preprint arXiv:1908.04729 (2019) - 4. Deng, Y., Rosenberg, D., Mann, G.: Challenges in end-to-end neural scientific table recognition. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 894–901. IEEE (2019) @@ -191,18 +191,18 @@ Secondly, OTSL has more inherent structure and a significantly restricted vo ca - 6. Lee, E., Kwon, J., Yang, H., Park, J., Lee, S., Koo, H.I., Cho, N.I.: Table structure recognition based on grid shape graph. 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In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 1015–1022. IEEE (2019) diff --git a/tests/pdf_snapshots/pdf/sources/code_and_formula.pdf.md b/tests/pdf_snapshots/pdf/sources/code_and_formula.pdf.md index 9fcafb6d..59a539e2 100644 --- a/tests/pdf_snapshots/pdf/sources/code_and_formula.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/code_and_formula.pdf.md @@ -1,6 +1,6 @@ ## JavaScript Code Example -Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eir mod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam volup tua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ip sum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. +Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, @@ -14,7 +14,7 @@ Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie co ## Formula -Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eir mod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam volup tua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ip sum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. +Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt. diff --git a/tests/pdf_snapshots/pdf/sources/multi_page.pdf.md b/tests/pdf_snapshots/pdf/sources/multi_page.pdf.md index fad77844..a38f2c52 100644 --- a/tests/pdf_snapshots/pdf/sources/multi_page.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/multi_page.pdf.md @@ -24,7 +24,7 @@ The advent of personal computers in the late 1970s and early 1980s transformed w - • WordStar (1978) : Developed for the CP/M operating system, WordStar was one of the first widely used word processing programs. It featured early examples of modern features like cut, copy, and paste. - • Microsoft Word (1983) : Microsoft launched Word for MS - DOS in 1983, introducing a graphical user interface (GUI) and mouse support. Over the years, Microsoft Word became the industry standard for word processing. -Other notable software from this era included WordPerfect, which was popular among legal professionals, and Apple’s MacWrite, which leveraged the Macintosh’s graphical capabilities. +Other notable software from this era included WordPerfect, which was popular among legal professionals, and Apple's MacWrite, which leveraged the Macintosh's graphical capabilities. ## The Modern Era (1990s Present) - @@ -62,7 +62,7 @@ The evolution of word processors wasn't just about hardware or software improvem The word processor didn't just change workplaces it changed culture. It democratized writing, enabling anyone with access to a computer to produce professional — quality - documents. This shift had profound implications for education, business, and creative fi elds: -- • Accessibility : Writers no longer needed expensive publishing equipment or training in typesetting to create polished work. This accessibility paved the way for self publishing, blogging, and even fan fiction communities. f +- • Accessibility : Writers no longer needed expensive publishing equipment or training in typesetting to create polished work. This accessibility paved the way for selfpublishing, blogging, and even fan fiction communities. f - • Education : Word processors became a cornerstone of education, teaching students not only how to write essays but also how to use technology effectively. Features like bibliography generators and integrated research tools enhanced learning. - Creative Writing : Writers gained powerful tools to organize their ideas. Programs • like Scrivener allowed authors to manage large projects, from novels to screenplays, with features like chapter outlines and character notes. @@ -84,4 +84,4 @@ The word processor's future lies in adaptability and intelligence. Some exciting - • Immersive Interfaces : As augmented reality (AR) and virtual reality (VR) technology advance, users may be able to write and edit in 3D spaces, collaborating in virtual environments. - • Hyper - Personalization : Word processors could offer dynamic suggestions based on industry specific needs, user habits, or even regional language variations. - -The journey of the word processor— from clunky typewriters to AI powered platforms r— - — reflects humanity’s broader technological progress. What began as a tool to simply replace handwriting has transformed into a powerful ally for creativity, communication, and collaboration. As technology continues to advance, the word processor will undoubtedly remain at the heart of how we express ideas and connect with one another. +The journey of the word processor— from clunky typewriters to AI powered platforms r— - — reflects humanity's broader technological progress. What began as a tool to simply replace handwriting has transformed into a powerful ally for creativity, communication, and collaboration. As technology continues to advance, the word processor will undoubtedly remain at the heart of how we express ideas and connect with one another. diff --git a/tests/pdf_snapshots/pdf/sources/normal_4pages.pdf.md b/tests/pdf_snapshots/pdf/sources/normal_4pages.pdf.md index c5c18380..b3415a86 100644 --- a/tests/pdf_snapshots/pdf/sources/normal_4pages.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/normal_4pages.pdf.md @@ -20,17 +20,17 @@ www.nars.go.kr 1 -2020.3.30. 0시 기준 현재 “코로나바이러스감염 증-19(COVID-19, 이하 “코로나-19”)”의 국내 확 진자는 9,661명, 사망자는 158명으로 나타났다. +2020.3.30. 0시 기준 현재 "코로나바이러스감염 증-19(COVID-19, 이하 "코로나-19")"의 국내 확 진자는 9,661명, 사망자는 158명으로 나타났다. 이와 관련하여 세계보건기구(WHO)는 지난 2020. 3.11. 코로나-19가 세계적 유행(Pandemic) 단계 에 돌입하였음을 선언하였다. 한편 코로나-19가 전 세계적 유행단계에 돌입한 와중에 국내 보험업계에서는 코로나-19를 과연 질 병으로 보아야 할지, 상해 1) 나 재해 2) 로 보아야 할지 -1) 손해보험의 표준약관 규정에 따르면, 보험기간 중에 발생한 급격하고도 우연한 외래의 사고로 신체에 입은 상해라고 규정하고 있는데, 즉, “급 격성, 우연성, 외래성”을 충족하는 사고를 상해로 정의함 +1) 손해보험의 표준약관 규정에 따르면, 보험기간 중에 발생한 급격하고도 우연한 외래의 사고로 신체에 입은 상해라고 규정하고 있는데, 즉, "급 격성, 우연성, 외래성"을 충족하는 사고를 상해로 정의함 -2) 생명보험은 표준약관 “재해분류표”에서 “우발적인 외래의 사고”를 재 해라고 정의하며 보장대상이 되는 재해는 다음 중 어느 하나에 해당하 는 것을 말함 +2) 생명보험은 표준약관 "재해분류표"에서 "우발적인 외래의 사고"를 재 해라고 정의하며 보장대상이 되는 재해는 다음 중 어느 하나에 해당하 는 것을 말함 -1) 한국표준질병·사인분류상의 ‘S80~Y84'에 해당하는 우발적인 외래의 사고 +1) 한국표준질병·사인분류상의 'S80~Y84'에 해당하는 우발적인 외래의 사고 2) 감염병의 예방 및 관리에 관한 법률 제2조 제2호에서 규정한 감염병 그러나, 약관상 한국표준질병·사인분류상의 U00~U99에 해당하는 @@ -58,7 +58,7 @@ www.nars.go.kr ## (1) 「감염병의 예방 및 관리에 관한 법률」 개정 -2020.1.1. 시행된 3) 「감염병의 예방 및 관리에 관 한 법률」(이하 ‘감염병예방법’) 제2조 제2호에 의하 면 제1급감염병이란 생물테러감염병 또는 치명률 이 높거나 집단 발생의 우려가 커서 발생 또는 유행 즉시 신고하여야 하고, 음압격리와 같은 높은 수준 의 격리가 필요한 감염병으로서 제2호 각 목4 목4) 에 규 정된 감염병을 말한다. +2020.1.1. 시행된 3) 「감염병의 예방 및 관리에 관 한 법률」(이하 '감염병예방법') 제2조 제2호에 의하 면 제1급감염병이란 생물테러감염병 또는 치명률 이 높거나 집단 발생의 우려가 커서 발생 또는 유행 즉시 신고하여야 하고, 음압격리와 같은 높은 수준 의 격리가 필요한 감염병으로서 제2호 각 목4 목4) 에 규 정된 감염병을 말한다. 다만, 갑작스러운 국내 유입 또는 유행이 예견되 어 긴급한 예방ㆍ관리가 필요하여 보건복지부장관 이 지정하는 감염병을 포함한다고 설명하고 있다. @@ -85,35 +85,35 @@ www.nars.go.kr ※ 주: 2020.1.1. 기준 -※ 자료: 생명보험협회, ‘20.3.11. +※ 자료: 생명보험협회, '20.3.11. -기존 생명보험에서 재해로 보장하는 법정1군 전염 병은 “콜레라, 장티푸스, 파라티푸스, 세균성이질, 장 출혈성대장균감염증, A형간염”이었으나 이번에 개 +기존 생명보험에서 재해로 보장하는 법정1군 전염 병은 "콜레라, 장티푸스, 파라티푸스, 세균성이질, 장 출혈성대장균감염증, A형간염"이었으나 이번에 개 -3) 개정 이유는 질환의 특성별 ‘군(群)’별로 구분되어 있는 현행 감염병 분 류체계를 감염병의 심각도ㆍ전파력ㆍ격리수준ㆍ신고시기 등을 중심으 로 한 ‘급(級)’별 분류체계로 개편하고, 감염병 위기상황 발생 시 컨트롤 타워 역할을 수행할 수 있는 긴급상황실의 설치ㆍ운영과 감염병환자와 접촉한 자를 격리할 수 있는 접촉자격리시설의 지정을 위한 법적 근거 를 신설하며, 감염병관리위원회 위원장을 보건복지부차관에서 질병관 리본부장으로 변경하는 등 감염병 발생 시 보다 효율적인 대처가 이루 어질 수 있도록 현행 감염병 관리체계를 개선ㆍ보완하려는 것임. +3) 개정 이유는 질환의 특성별 '군(群)'별로 구분되어 있는 현행 감염병 분 류체계를 감염병의 심각도ㆍ전파력ㆍ격리수준ㆍ신고시기 등을 중심으 로 한 '급(級)'별 분류체계로 개편하고, 감염병 위기상황 발생 시 컨트롤 타워 역할을 수행할 수 있는 긴급상황실의 설치ㆍ운영과 감염병환자와 접촉한 자를 격리할 수 있는 접촉자격리시설의 지정을 위한 법적 근거 를 신설하며, 감염병관리위원회 위원장을 보건복지부차관에서 질병관 리본부장으로 변경하는 등 감염병 발생 시 보다 효율적인 대처가 이루 어질 수 있도록 현행 감염병 관리체계를 개선ㆍ보완하려는 것임. 4) 2. 가. ~ 더.; 에볼라바이러스병, 마버그열, 라싸열, 크리미안콩고출혈 열, 남아메리카출혈열, 리프트밸리열, 두창, 페스트, 탄저, 보툴리눔독 신종감염병증후군, 중증급성호흡기증후군(SARS), 중동 소증, 야토병, 호흡기증후군(MERS), 동물인플루엔자 인체감염증, 신종인플루엔자, 디프테리아 등 17종 -정된 법에서 이들은 제2급감염병으로 변경되었다. 대신에 「감염병예방법」 제1급감염병으로 “에볼 라바이러스병, ~ 신종감염증후군, SARS, MERS, ~ 디프테리아”등 17종이 새롭게 포함되었다. +정된 법에서 이들은 제2급감염병으로 변경되었다. 대신에 「감염병예방법」 제1급감염병으로 "에볼 라바이러스병, ~ 신종감염증후군, SARS, MERS, ~ 디프테리아"등 17종이 새롭게 포함되었다. ## (2) 생명보험 표준약관 재해분류표 -현행 생명보험 표준약관상 재해분류표5 표5) 는 위 「감염병예방법」 제2조 제2호의 제1급 감염병들을 질병임에도 불구하고 보장대상이 되는 ‘재해’로 규 정하고 있다. +현행 생명보험 표준약관상 재해분류표5 표5) 는 위 「감염병예방법」 제2조 제2호의 제1급 감염병들을 질병임에도 불구하고 보장대상이 되는 '재해'로 규 정하고 있다. -그러나 제1급 감염병에 포함되는 질병임에도 불 구하고, 생명보험 표준약관 재해분류표의 한국표 준질병·사인분류6 류6) (이하 ‘KCD’라 함)상 U코드 (U00~U99)에 해당하는 질병들(SARS-U04.9, MERS-U19.9 등)은 보장제외 대상으로 분류되어 보험금을 지급하지 않는 재해로 규정되어 있다. +그러나 제1급 감염병에 포함되는 질병임에도 불 구하고, 생명보험 표준약관 재해분류표의 한국표 준질병·사인분류6 류6) (이하 'KCD'라 함)상 U코드 (U00~U99)에 해당하는 질병들(SARS-U04.9, MERS-U19.9 등)은 보장제외 대상으로 분류되어 보험금을 지급하지 않는 재해로 규정되어 있다. -코로나-19 역시 KCD 수록 정식 명칭은 “코로나 바이러스 질환 2019”로 질병분류기호는 “U07.1” 로 표시하고 있어서 보험금이 지급되지 않는 재해 로 분류되어 재해보험금 지급대상에 포함되지 않는 다고 해석될 수 있다. +코로나-19 역시 KCD 수록 정식 명칭은 "코로나 바이러스 질환 2019"로 질병분류기호는 "U07.1" 로 표시하고 있어서 보험금이 지급되지 않는 재해 로 분류되어 재해보험금 지급대상에 포함되지 않는 다고 해석될 수 있다. -한편, 현재 생명보험 표준약관은 2020.1.1.부터 신규 판매되고 있는 보험상품의 약관이며 해당 약 관에는 아직 상기 법 개정사항이 반영되지 않은 상 황이지만, 재해분류표를 보면 별도의 각주를 통해 “감염병에 관한 법률이 제·개정될 경우, 보험사고 발생 당시 제·개정된 법률을 적용합니다.”라고 명 시되어 코로나-19를 재해보험금 지급대상에 포함 되는 것으로도 해석할 수 있다. +한편, 현재 생명보험 표준약관은 2020.1.1.부터 신규 판매되고 있는 보험상품의 약관이며 해당 약 관에는 아직 상기 법 개정사항이 반영되지 않은 상 황이지만, 재해분류표를 보면 별도의 각주를 통해 "감염병에 관한 법률이 제·개정될 경우, 보험사고 발생 당시 제·개정된 법률을 적용합니다."라고 명 시되어 코로나-19를 재해보험금 지급대상에 포함 되는 것으로도 해석할 수 있다. - (3) 「약관의 규제에 관한 법률」상 약관해석의 원칙 현재 「약관의 규제에 관한 법률」 7) 을 보면 약관은 5) 「생명보험 표준약관 부표4」 -6) 한국표준질병사인분류(Korean Standard Cl assification of Diseases, “KCD”)는 대한민국에서 의무기록자료 및 사망원인통계조사 등 질병이 환 및 사망자료를 그 성질의 유사성에 따라 체계적으로 유형화한 것으로, 모든 형태의 보건 및 인구동태 기록에 기재되어 있는 질병 및 기타 보건 문제를 분류하는데 이용하기 위하여 설정한 으로 통계청에서 작성함 +6) 한국표준질병사인분류(Korean Standard Cl assification of Diseases, "KCD")는 대한민국에서 의무기록자료 및 사망원인통계조사 등 질병이 환 및 사망자료를 그 성질의 유사성에 따라 체계적으로 유형화한 것으로, 모든 형태의 보건 및 인구동태 기록에 기재되어 있는 질병 및 기타 보건 문제를 분류하는데 이용하기 위하여 설정한 으로 통계청에서 작성함 7) 제5조(약관의 해석) ① 약관은 신의성실의 원칙에 따라 공정하게 해석 되어야 하며 고객에 따라 다르게 해석되어서는 아니 된다. ② 약관의 뜻이 명백하지 아니한 경우에는 고객에게 유리하게 해석되어 야 한다. -작성자인 사업자에 의하여 일방적으로 유리하게 작 성되고 고객에게는 그 약관내용에 관한 교섭이나 검토의 기회가 제대로 주어지지 않는 것이 일반적 이므로 이러한 형성과정에 비추어 고객보호의 측면 에서 약관내용이 명백하지 못하거나 의심스러운 때 에는 고객에게 유리하게 해석되어야 한다는 ‘작성 자 불이익의 해석원칙’이 있다. +작성자인 사업자에 의하여 일방적으로 유리하게 작 성되고 고객에게는 그 약관내용에 관한 교섭이나 검토의 기회가 제대로 주어지지 않는 것이 일반적 이므로 이러한 형성과정에 비추어 고객보호의 측면 에서 약관내용이 명백하지 못하거나 의심스러운 때 에는 고객에게 유리하게 해석되어야 한다는 '작성 자 불이익의 해석원칙'이 있다. ## (4) 생보업계의 재해보험금 지급에 대한 의견 @@ -123,9 +123,9 @@ www.nars.go.kr ## (1) 상위법에 반하는 보험약관의 해석 -최근 코로나-19는 개정 법령에서 정한 법정감염 병 1급에 해당하는 17종 중 “신종감염병증후군”으 로 분류할 수 있다고 볼 수 있어 재해보상 대상이라 는 주장과 보장제외 대상인 U코드에 해당하여 재해 보장대상에서 제외해야 한다는 일부 생명보험사의 주장 등 의견이 상반된다. +최근 코로나-19는 개정 법령에서 정한 법정감염 병 1급에 해당하는 17종 중 "신종감염병증후군"으 로 분류할 수 있다고 볼 수 있어 재해보상 대상이라 는 주장과 보장제외 대상인 U코드에 해당하여 재해 보장대상에서 제외해야 한다는 일부 생명보험사의 주장 등 의견이 상반된다. -따라서 법정1급 감염병에 포함된 17종의 질환이 보험약관 재해분류표 상 재해에 해당되지만 기존의 “SARS(U04.9)”, “MERS(U19.9)”를 포함하여 “코로 나-19(U07.1)”는 재해분류표 상 면책사유(U00~09) 에 포함된다고 생명보험사가 주장할 경우 보험약관 이 상위법인 「감염병예방법」 개정취지에 반하는 결 과를 가져오게 되는 상황이 발생한다. +따라서 법정1급 감염병에 포함된 17종의 질환이 보험약관 재해분류표 상 재해에 해당되지만 기존의 "SARS(U04.9)", "MERS(U19.9)"를 포함하여 "코로 나-19(U07.1)"는 재해분류표 상 면책사유(U00~09) 에 포함된다고 생명보험사가 주장할 경우 보험약관 이 상위법인 「감염병예방법」 개정취지에 반하는 결 과를 가져오게 되는 상황이 발생한다. ## (2) 감독당국의 표준약관 개정작업 소홀 @@ -165,7 +165,7 @@ www.nars.go.kr 우선 「감염병예방법」 변경에 따른 감독당국의 조속한 표준약관 개정작업이 진행되어야 할 필요가 있다. -보험사가 생명보험에서 ‘일부 감염병’을 재해로 보장하는 이유는 ‘일부 감염병’이 우연성, 외래성, 급격성 등 재해의 특성을 지니고 있기 때문이며, 실 례로 페스트는 질병이지만 재해에 준하는 급격성도 지니고 있어, 정책적으로 재해로 취급하여 보험의 보장 범위를 확대한 바 있다. +보험사가 생명보험에서 '일부 감염병'을 재해로 보장하는 이유는 '일부 감염병'이 우연성, 외래성, 급격성 등 재해의 특성을 지니고 있기 때문이며, 실 례로 페스트는 질병이지만 재해에 준하는 급격성도 지니고 있어, 정책적으로 재해로 취급하여 보험의 보장 범위를 확대한 바 있다. 코로나-19 역시 질병이지만 세계적 유행단계에 돌입하는 등 페스트와 같은 재해에 준하는 성격을 포함하고 있어 이를 재해로 인정할 필요가 있다. diff --git a/tests/pdf_snapshots/pdf/sources/picture_classification.pdf.md b/tests/pdf_snapshots/pdf/sources/picture_classification.pdf.md index 5a14c469..3aa344fb 100644 --- a/tests/pdf_snapshots/pdf/sources/picture_classification.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/picture_classification.pdf.md @@ -1,6 +1,6 @@ ## Figures Example -Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eir mod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam volup tua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ip sum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. +Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. diff --git a/tests/pdf_snapshots/pdf/sources/redp5110_sampled.pdf.md b/tests/pdf_snapshots/pdf/sources/redp5110_sampled.pdf.md index e01ce4e5..fc8540a9 100644 --- a/tests/pdf_snapshots/pdf/sources/redp5110_sampled.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/redp5110_sampled.pdf.md @@ -8,7 +8,7 @@ ibm.com /redbooks -# Row and Column Access Control Support in IBM DB2 for i +## Row and Column Access Control Support in IBM DB2 for i @@ -26,10 +26,10 @@ IBM Systems Lab Services and Training Solution Brief ## Highlights --               --                     --       !   " #  --  ! #      " "       +-              +-                    +-      !   " #  +- ! #      " "       @@ -45,7 +45,7 @@ No one else has the vast consulting experiences, skills sharing and renown servi Because no one else is IBM. -With combined experiences and direct access to development groups, we’re the experts in IBM DB2® for i. The DB2 for i Center of Excellence (CoE) can help you achieve—perhaps reexamine and exceed—your business requirements and gain more confidence and satisfaction in IBM product data management products and solutions. +With combined experiences and direct access to development groups, we're the experts in IBM DB2® for i. The DB2 for i Center of Excellence (CoE) can help you achieve—perhaps reexamine and exceed—your business requirements and gain more confidence and satisfaction in IBM product data management products and solutions. ## Who we are, some of what we do @@ -77,7 +77,7 @@ Jim Bainbridge is a senior DB2 consultant on the DB2 for i Center of Excellence -Hernando Bedoya is a Senior IT Specialist at STG Lab Services and Training in Rochester, Minnesota. He writes extensively and teaches IBM classes worldwide in all areas of DB2 for i. Before jo ining STG Lab Services, he worked in the ITSO for nine years writing multiple IBM Redbooks® publications. He also worked for IBM Colombia as an IBM AS/400® IT Specialist doing presales support for the Andean countries. He has 28 years of experience in the computing field and has taught database classes in Colombian universities. He holds a Master’s degree in Computer Science from EAFIT, Colombia. His areas of expertise are database technology, performance, and data warehousing. Hernando can be contacted at hbedoya@us.ibm.com . +Hernando Bedoya is a Senior IT Specialist at STG Lab Services and Training in Rochester, Minnesota. He writes extensively and teaches IBM classes worldwide in all areas of DB2 for i. Before jo ining STG Lab Services, he worked in the ITSO for nine years writing multiple IBM Redbooks® publications. He also worked for IBM Colombia as an IBM AS/400® IT Specialist doing presales support for the Andean countries. He has 28 years of experience in the computing field and has taught database classes in Colombian universities. He holds a Master's degree in Computer Science from EAFIT, Colombia. His areas of expertise are database technology, performance, and data warehousing. Hernando can be contacted at hbedoya@us.ibm.com . @@ -91,9 +91,9 @@ Businesses must make a seriou s effort to secure their data and recognize that s This chapter describes how you can secure and protect data in DB2 for i. The following topics are covered in this chapter: --  Security fundamentals --  Current state of IBM i security --  DB2 for i security controls +- Security fundamentals +- Current state of IBM i security +- DB2 for i security controls 1 http://www.idtheftcenter.org @@ -103,13 +103,13 @@ This chapter describes how you can secure and protect data in DB2 for i. The fol Before reviewing database security techniques, there are two fundamental steps in securing information assets that must be described: --  First, and most important, is the definition of a company's security policy. y. Without a security policy, there is no definition of what are acceptable practices for using, accessing, and storing information by who, what, when, where, and how. A security policy should minimally address three things: confidentiality, integrity, and availability. +- First, and most important, is the definition of a company's security policy. y. Without a security policy, there is no definition of what are acceptable practices for using, accessing, and storing information by who, what, when, where, and how. A security policy should minimally address three things: confidentiality, integrity, and availability. The monitoring and assessment of adherence to the security policy determines whether your security strategy is working. Often, IBM security consultants are asked to perform security assessments for companies without regard to the security policy. Although these assessments can be useful for observing how the system is defined and how data is being accessed, they cannot determine the level of security without a security policy. Without a security policy, it really is no t an assessment as much as it is a baseline for monitoring the changes in the security settings that are captured. A security policy is what defines whether the system and its settings are secure (or not). --  The second fundamental in securing data assets is the use of resource security. y. If implemented properly, resource security prevents data breaches from both internal and external intrusions. Resource security controls are closely tied to the part of the security policy that defines who should have access to what information resources. A hacker might be good enough to get through your company firewalls and sift his way through to your system, but if they do not have explicit access to your database, the hacker cannot compromise your information assets. +- The second fundamental in securing data assets is the use of resource security. y. If implemented properly, resource security prevents data breaches from both internal and external intrusions. Resource security controls are closely tied to the part of the security policy that defines who should have access to what information resources. A hacker might be good enough to get through your company firewalls and sift his way through to your system, but if they do not have explicit access to your database, the hacker cannot compromise your information assets. With your eyes now open to the importance of securing information assets, the rest of this chapter reviews the methods that are available for securing database resources on IBM i. @@ -117,7 +117,7 @@ With your eyes now open to the importance of securing information assets, the re Because of the inherently secure nature of IBM i, many clients rely on the default system settings to protect their business data that is stored in DB2 for i. In most cases, this means no data protection because the default setting for the Create default public authority (QCRTAUT) system value is *CHANGE. -Even more disturbing is that many IBM i clients remain in this state, despite the news headlines and the significant costs that are involved with databases being compromised. This default security configuration makes it quite challenging to implement basic security policies. A tighter implementation is required if you really want to protect one of your company’s most valuable assets, which is the data. +Even more disturbing is that many IBM i clients remain in this state, despite the news headlines and the significant costs that are involved with databases being compromised. This default security configuration makes it quite challenging to implement basic security policies. A tighter implementation is required if you really want to protect one of your company's most valuable assets, which is the data. Traditionally, IBM i applications have employed menu-based security to counteract this default configuration that gives all users access to the data. The theory is that data is protected by the menu options controlling what database op erations that the user can perform. This approach is ineffective, even if the user profile is restricted from running interactive commands. The reason is that in today's connected world there are a multitude of interfaces into the system, from web browsers to PC clients, that bypass application menus. If there are no object-level controls, users of these newer interfaces have an open door to your data. @@ -139,9 +139,9 @@ Figure 1-2 Existing row and column controls The following CL commands can be used to work with, display, or change function usage IDs: --  Work Function Usage ( WRKFCNUSG ) --  Change Function Usage ( CHGFCNUSG ) --  Display Function Usage ( DSPFCNUSG ) +- Work Function Usage ( WRKFCNUSG ) +- Change Function Usage ( CHGFCNUSG ) +- Display Function Usage ( DSPFCNUSG ) For example, the following CHGFCNUSG command shows granting authorization to user HBEDOYA to administer and manage RCAC rules: @@ -159,11 +159,11 @@ Table 2-1 FUNCTION_USAGE view | USER_NAME VARCHAR(10) Name of the user pr | | | ofile that has a usage setting for this | | | function. | | | | USAGE VARCHAR(7) Usage setting: | | | | -| |  | ALLOWED: The user profile is allowed to use the function. | | -| |  | DENIED: The user profile is not allowed to use the function. | | +| | | ALLOWED: The user profile is allowed to use the function. | | +| | | DENIED: The user profile is not allowed to use the function. | | | USER_TYPE VARCHAR(5) Type of user profile: | | | | -| |  | USER: The user profile is a user. | | -| |  | GROUP: The user profile is a group. | | +| | | USER: The user profile is a user. | | +| | | GROUP: The user profile is a group. | | To discover who has authorization to define and manage RCAC, you can use the query that is shown in Example 2-1. @@ -236,11 +236,11 @@ Table 3-1 Special registers and their corresponding values Figure 3-5 shows the difference in the special register values when an adopted authority is used: --  A user connects to the server using the user profile ALICE. --  USER and CURRENT USER initially have the same value of ALICE. -- ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE  and was created to adopt JOE's authority when it is called. --  While the procedure is running, the special register USER still contains the value of ALICE because it excludes any adopted authority. The special register CURRENT USER contains the value of JOE because it includes any adopted authority. -- When proc1 ends, the session reverts to its original state with both USER and CURRENT  USER having the value of ALICE. +- A user connects to the server using the user profile ALICE. +- USER and CURRENT USER initially have the same value of ALICE. +- ALICE calls an SQL procedure that is named proc1, which is owned by user profile JOE and was created to adopt JOE's authority when it is called. +- While the procedure is running, the special register USER still contains the value of ALICE because it excludes any adopted authority. The special register CURRENT USER contains the value of JOE because it includes any adopted authority. +- When proc1 ends, the session reverts to its original state with both USER and CURRENT USER having the value of ALICE. @@ -336,7 +336,7 @@ Figure 4-69 Index advice with no RCAC THEN C . CUSTOMER_TAX_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ELSE '*************' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_LOGIN_ID RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_LOGIN_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_LOGIN_ID ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ELSE '*****' END ENABLE ; ALTER TABLE BANK_SCHEMA.CUSTOMERS ACTIVATE ROW ACCESS CONTROL ACTIVATE COLUMN ACCESS CONTROL ; -# Row and Column Access Control Support in IBM DB2 for i +## Row and Column Access Control Support in IBM DB2 for i Implement roles and separation of duties diff --git a/tests/pdf_snapshots/pdf/sources/table_mislabeled_as_picture.pdf.md b/tests/pdf_snapshots/pdf/sources/table_mislabeled_as_picture.pdf.md index b2f3b250..da62fec4 100644 --- a/tests/pdf_snapshots/pdf/sources/table_mislabeled_as_picture.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/table_mislabeled_as_picture.pdf.md @@ -2,7 +2,7 @@ |-----|---------------------------------------------------------------------------------------|------------------|-------------------------------------------------------|----| | | represent people eligible for legal aid | | | | | „ | They coordinate appointments of private practitioners (ex officio, or panel appoint | | | | -| | | | |  | +| | | | | | | | ments) to legal aid cases | | | | | „ | They supervise, coach or mentor private practitioners who take legal aid cases | | | | | „ | They conduct or organize training sessions for staff lawyers/paralegals | | | | @@ -13,7 +13,7 @@ | „ | Not applicable, there is no institutional legal aid provider | | | | - „ They work in parallel to State funded private practitioners who take assignments to represent people eligible for legal aid -- „ They coordinate appointments of private practitioners (ex officio, or panel appoint  ments) to legal aid cases +- „ They coordinate appointments of private practitioners (ex officio, or panel appoint ments) to legal aid cases - „ They supervise, coach or mentor private practitioners who take legal aid cases - „ They conduct or organize training sessions for staff lawyers/paralegals - „ They conduct or organize training sessions for all providers of legal aid, including both staff and private lawyers/paralegals @@ -44,14 +44,14 @@ | 25. | If your country has an institutional legal aid provider (e.g. public defender), does it | | | |-------|-------------------------------------------------------------------------------------------|--------------------------------------|----| -| | have specialized providers and/or units for representing child victims, child witness | |  | +| | have specialized providers and/or units for representing child victims, child witness | | | | | es or suspected and accused children? | | | | | „ | Yes, at the national (federal) level | | | | „ | Yes, at regional (district) level | | | | „ | Yes, at the local (municipal) level | | | | „ | No | | -- 25. If your country has an institutional legal aid provider (e.g. public defender), does it have specialized providers and/or units for representing child victims, child witness  es or suspected and accused children? +- 25. If your country has an institutional legal aid provider (e.g. public defender), does it have specialized providers and/or units for representing child victims, child witness es or suspected and accused children? - „ Yes, at the national (federal) level - Yes, at regional (district) level - „ Yes, at the local (municipal) level @@ -59,7 +59,7 @@ - „ Yes, there are specific guidelines for non-lawyers providing legal aid services - „ Yes, there are specific guidelines on faculty/student ratios - „ No, it is up to the discretion of each university -- „ Don’t know +- „ Don't know - „ There are no university-based student law clinics | 27. | If your country allows legal aid services through university-based student law clinics, | | | | | @@ -75,7 +75,7 @@ | | „ | They can provide a full range of legal services in criminal cases regardless of gravity | | | | | | „ | They can conduct mediation | | | | | | „ | They are authorized to provide only those services that a faculty member or practic | | | | -| | | | | |  | +| | | | | | | | | | ing lawyer supervises | | | | | | „ | Don’t know | | | | | | „ | Other | (Please specify) | | | @@ -83,7 +83,7 @@ - 27. If your country allows legal aid services through university-based student law clinics, what type of legal aid services is a student authorized to undertake? (Please select all that apply) - „ They can represent people in administrative or civil law hearings -- 28. Are specialized legal aid services provided focusing on specific disadvantaged popu  lation groups? If yes, please indicate to whom these services are provided, and whether they are provided by State-funded legal aid, civil society organizations, or both. +- 28. Are specialized legal aid services provided focusing on specific disadvantaged popu lation groups? If yes, please indicate to whom these services are provided, and whether they are provided by State-funded legal aid, civil society organizations, or both. (Please select all that apply) diff --git a/tests/pdf_snapshots/tiff/sources/2206.01062.tif.md b/tests/pdf_snapshots/tiff/sources/2206.01062.tif.md index 2294745d..c8a9eb5f 100644 --- a/tests/pdf_snapshots/tiff/sources/2206.01062.tif.md +++ b/tests/pdf_snapshots/tiff/sources/2206.01062.tif.md @@ -18,7 +18,7 @@ KDD 22 Aueaat 24, 2022, Washimgton, DC USA 7@01.8r/10.1145/35346783539043 -# DocLayNet:ALarge Human-Annotated Datasetfor Document-LayoutAnalysis +## DocLayNet:ALarge Human-Annotated Datasetfor Document-LayoutAnalysis Michele Dolfi IBMResearch Rueschlikon, Switzerland dol@zurich.ibm.com From fd276c6a98448d05ecac5b28c78bd37b40610ee5 Mon Sep 17 00:00:00 2001 From: artiz Date: Sun, 28 Jun 2026 12:40:01 +0200 Subject: [PATCH 02/42] feat(pdf): emit formula placeholder and pair captions before figures MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Two docling-conformance fixes in the PDF assembly: - Formula regions now emit `` (docling's standard pipeline does not decode formulas) instead of the garbled raw glyph text. - Figure captions are paired with their picture and emitted *before* the image marker, matching docling (`Figure 1: …` then ``). Previously the caption was emitted in its own lower reading-order slot, after the image. Measured against the committed docling groundtruth, `picture_classification.pdf` is now byte-for-byte exact, and the captioned/formula papers move closer (2203.01017v2 366→346, amt_handbook 20→12, 2206.01062 330→321). pdf_conformance stays 76/76 exact; 12 snapshot fixtures regenerated. Co-Authored-By: Claude Opus 4.8 (1M context) --- crates/fleischwolf-pdf/src/assemble.rs | 58 +++++++++++++++++-- .../sources/2310.06825/images/swa.pdf.md | 8 +-- .../pdf/sources/2203.01017v2.pdf.md | 48 +++++++-------- .../pdf/sources/2206.01062.pdf.md | 25 ++++---- .../pdf/sources/amt_handbook_sample.pdf.md | 8 +-- .../pdf/sources/code_and_formula.pdf.md | 2 +- .../pdf/sources/picture_classification.pdf.md | 8 +-- .../pdf/sources/redp5110_sampled.pdf.md | 28 ++++----- .../pdf/sources/right_to_left_02.pdf.md | 2 +- .../pdf/sources/skipped_1page.pdf.md | 16 ++--- .../pdf/sources/skipped_2pages.pdf.md | 16 ++--- .../sample_with_rotation_mismatch.pdf.md | 4 +- .../tiff/sources/2206.01062.tif.md | 4 +- 13 files changed, 137 insertions(+), 90 deletions(-) diff --git a/crates/fleischwolf-pdf/src/assemble.rs b/crates/fleischwolf-pdf/src/assemble.rs index 3bc974c5..c81bbb68 100644 --- a/crates/fleischwolf-pdf/src/assemble.rs +++ b/crates/fleischwolf-pdf/src/assemble.rs @@ -233,20 +233,63 @@ fn crop_region(page: &PdfPage, region: &Region) -> Option { }) } +/// For each `picture` region, find the `caption` region closest below it (and +/// horizontally overlapping); docling pairs them and emits the caption first. +/// Each caption is claimed by at most one picture. +fn pair_captions(regions: &[Region]) -> Vec> { + let mut pairs = vec![None; regions.len()]; + let mut taken = vec![false; regions.len()]; + for (pi, p) in regions.iter().enumerate() { + if p.label != "picture" { + continue; + } + let mut best: Option<(usize, f32)> = None; + for (ci, c) in regions.iter().enumerate() { + if c.label != "caption" || taken[ci] { + continue; + } + let line_h = (c.b - c.t).abs().max(1.0); + let gap = c.t - p.b; // caption sits below the picture + let h_overlap = (p.r.min(c.r) - p.l.max(c.l)).max(0.0); + if gap > -line_h && gap < line_h * 3.0 && h_overlap > 0.0 { + let dist = gap.abs(); + if best.is_none_or(|(_, bd)| dist < bd) { + best = Some((ci, dist)); + } + } + } + if let Some((ci, _)) = best { + pairs[pi] = Some(ci); + taken[ci] = true; + } + } + pairs +} + /// Assemble one page from its (already overlap-resolved) layout regions and /// text cells. pub fn assemble_page(page: &PdfPage, mut regions: Vec, doc: &mut DoclingDocument) { order_regions(&mut regions, page.width); + // docling emits a figure's caption *before* the image marker. Pair each + // picture with the caption region nearest below it and consume that caption, + // so it isn't also emitted in its own (lower) reading-order position. + let caption_for = pair_captions(®ions); + let mut consumed = vec![false; regions.len()]; + for ci in caption_for.iter().flatten() { + consumed[*ci] = true; + } - for region in ®ions { - if is_skipped(region.label) { + for (i, region) in regions.iter().enumerate() { + if is_skipped(region.label) || consumed[i] { continue; } if region.label == "picture" { - // Caption text, if any, is emitted by its own `caption` region. // The figure pixels are cropped from the page render for image export. + let caption = caption_for[i] + .map(|ci| region_text(®ions[ci], &page.cells)) + .filter(|t| !t.is_empty()); doc.push(Node::Picture { - caption: None, + caption, image: crop_region(page, region), }); continue; @@ -277,7 +320,12 @@ pub fn assemble_page(page: &PdfPage, mut regions: Vec, doc: &mut Docling }; doc.push(Node::Table(Table { rows })); } - // text, caption, footnote, formula, code → paragraph + // docling does not decode formulas in the standard pipeline; it emits + // a placeholder comment rather than the (garbled) raw glyph text. + "formula" => doc.push(Node::Paragraph { + text: "".into(), + }), + // text, caption, footnote, code → paragraph _ => doc.push(Node::Paragraph { text }), } } diff --git a/tests/pdf_snapshots/latex/sources/2310.06825/images/swa.pdf.md b/tests/pdf_snapshots/latex/sources/2310.06825/images/swa.pdf.md index 826cc7bf..ad1ed05c 100644 --- a/tests/pdf_snapshots/latex/sources/2310.06825/images/swa.pdf.md +++ b/tests/pdf_snapshots/latex/sources/2310.06825/images/swa.pdf.md @@ -1,13 +1,13 @@ - +Effective Context Length - +Vanilla Attention -Effective Context Length + Sliding Window Attention -Vanilla Attention + diff --git a/tests/pdf_snapshots/pdf/sources/2203.01017v2.pdf.md b/tests/pdf_snapshots/pdf/sources/2203.01017v2.pdf.md index f6bf03ad..7cf722c5 100644 --- a/tests/pdf_snapshots/pdf/sources/2203.01017v2.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/2203.01017v2.pdf.md @@ -14,6 +14,8 @@ The occurrence of tables in documents is ubiquitous. They often summarise quanti ## a. Picture of a table: +Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: 'PMC2944238 004 02'. + | | | 1 | @@ -34,8 +36,6 @@ The occurrence of tables in documents is ubiquitous. They often summarise quanti | 8 | 2 | 13 | | 14 | 15 | | 16 | | | | 17 | | 18 | 19 | | 20 | -Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: 'PMC2944238 004 02'. - Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document. The first problem is called table-location and has been previously addressed [ 30 38 19 21 23 26 8 ] with stateof-the-art object-detection networks (e.g. YOLO and later , , , , , , on Mask-RCNN [ 9 ]). For all practical purposes, it can be @@ -79,10 +79,10 @@ Hybrid Deep Learning-Rule-Based approach : A popular current model for table-str We rely on large-scale datasets such as PubTabNet [ 37 ], FinTabNet [ 36 ], and TableBank [ 17 ] datasets to train and evaluate our models. These datasets span over various appearance styles and content. We also introduce our own synthetically generated SynthTabNet dataset to fix an im - - Figure 2: Distribution of the tables across different table dimensions in PubTabNet + FinTabNet datasets + + balance in the previous datasets. The PubTabNet dataset contains 509k tables delivered as annotated PNG images. The annotations consist of the table structure represented in HTML format, the tokenized text and its bounding boxes per table cell. Fig. 1 shows the appearance style of PubTabNet. Depending on its complexity, a table is characterized as "simple" when it does not contain row spans or column spans, otherwise it is "complex". The dataset is divided into Train and Val splits (roughly 98% and 2%). The Train split consists of 54% simple and 46% complex tables and the Val split of 51% and 49% respectively. The FinTabNet dataset contains 112k tables delivered as single-page PDF documents with mixed table structures and text content. Similarly to the PubTabNet, the annotations of FinTabNet include the table structure in HTML, the tokenized text and the bounding boxes on a table cell basis. The dataset is divided into Train, Test and Val splits (81%, 9.5%, 9.5%), and each one is almost equally divided into simple and complex tables (Train: 48% simple, 52% complex, Test: 48% simple, 52% complex, Test: 53% simple, 47% complex). Finally the TableBank dataset consists of 145k tables provided as JPEG images. The latter has annotations for the table structure, but only few with bounding boxes of the table cells. The entire dataset consists of simple tables and it is divided into 90% Train, 3% Test and 7% Val splits. @@ -124,14 +124,14 @@ We now describe in detail the proposed method, which is composed of three main c CNN Backbone Network. A ResNet-18 CNN is the backbone that receives the table image and encodes it as a vector of predefined length. The network has been modified by removing the linear and pooling layer, as we are not per - - Figure 3: TableFormer takes in an image of the PDF and creates bounding box and HTML structure predictions that are synchronized. The bounding boxes grabs the content from the PDF and inserts it in the structure. Figure 4: Given an input image of a table, the Encoder produces fixed-length features that represent the input image. The features are then passed to both the Structure Decoder and Cell BBox Decoder . During training, the Structure Decoder receives 'tokenized tags' of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. Simultaneously, the Cell BBox Decoder selects features referring to the data cells (' < td > ', ' < ') and passes them through an attention network, an MLP, and a linear layer to predict the bounding boxes. + + forming classification, and adding an adaptive pooling layer of size 28*28. ResNet by default downsamples the image resolution by 32 and then the encoded image is provided to both the Structure Decoder , and Cell BBox Decoder . Structure Decoder. The transformer architecture of this component is based on the work proposed in [ 31 ]. After extensive experimentation, the Structure Decoder is modeled as a transformer encoder with two encoder layers and a transformer decoder made from a stack of 4 decoder layers that comprise mainly of multi-head attention and feed forward layers. This configuration uses fewer layers and heads in comparison to networks applied to other problems (e.g. "Scene Understanding", "Image Captioning"), something which we relate to the simplicity of table images. @@ -150,7 +150,7 @@ Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The C The loss used to train the TableFormer can be defined as following: -l box = λ iou l iou + λ l 1 (1) l = λl + (1 − λ ) l box s + where λ ∈ [0, 1], and λ , λ ∈ R are hyper-parameters. iou l 1 @@ -160,7 +160,7 @@ where λ ∈ [0, 1], and λ , λ ∈ R are hyper-parameters. iou l 1 TableFormer uses ResNet-18 as the CNN Backbone Network . The input images are resized to 448*448 pixels and the feature map has a dimension of 28*28. Additionally, we enforce the following input constraints: -Image width and height ≤ 1024 pixels (2) Structural tags length ≤ 512 tokens. + Although input constraints are used also by other methods, such as EDD, ours are less restrictive due to the improved @@ -182,7 +182,7 @@ We also share our baseline results on the challenging SynthTabNet dataset. Throu The Tree-Edit-Distance-Based Similarity (TEDS) metric was introduced in [ 37 ]. It represents the prediction, and ground-truth as a tree structure of HTML tags. This similarity is calculated as: -EditDist ( Ta , Tb Tb ) TEDS ( Ta Ta , Tb Tb ) = 1 − max ( | Ta | Ta | Tb | ) (3) Ta , Tb + where Ta and Tb represent tables in tree structure HTML format. EditDist denotes the tree-edit distance, and Ta Tb | T | represents the number of nodes in T . @@ -242,16 +242,16 @@ a. Red - PDF cells, Green - predicted bounding boxes, Blue - post-processed pred Japanese language (previously unseen by TableFormer): +Figure 5: One of the benefits of TableFormer is that it is language agnostic, as an example, the left part of the illustration demonstrates TableFormer predictions on previously unseen language (Japanese). Additionally, we see that TableFormer is robust to variability in style and content, right side of the illustration shows the example of the TableFormer prediction from the FinTabNet dataset. + Text is aligned to match original for ease of viewing -Figure 5: One of the benefits of TableFormer is that it is language agnostic, as an example, the left part of the illustration demonstrates TableFormer predictions on previously unseen language (Japanese). Additionally, we see that TableFormer is robust to variability in style and content, right side of the illustration shows the example of the TableFormer prediction from the FinTabNet dataset. +Figure 6: An example of TableFormer predictions (bounding boxes and structure) from generated SynthTabNet table. -Figure 6: An example of TableFormer predictions (bounding boxes and structure) from generated SynthTabNet table. - ## 6. Future Work & Conclusion ## 5.5. Qualitative Analysis @@ -344,10 +344,10 @@ The process of generating a synthetic dataset can be decomposed into the followi Although TableFormer can predict the table structure and the bounding boxes for tables recognized inside PDF documents, this is not enough when a full reconstruction of the original table is required. This happens mainly due the following reasons: - - Figure 7: Distribution of the tables across different dimensions per dataset. Simple vs complex tables per dataset and split, strict vs non strict html structures per dataset and table complexity, missing bboxes per dataset and table complexity. + + - • TableFormer output does not include the table cell content. - • There are occasional inaccuracies in the predictions of the bounding boxes. @@ -361,7 +361,7 @@ Here is a step-by-step description of the prediction postprocessing: - 3.a. If all IOU scores in a column are below the threshold, discard all predictions (structure and bounding boxes) for that column. - 4. Find the best-fitting content alignment for the predicted cells with good IOU per each column. The alignment of the column can be identified by the following formula: -alignment = arg min { D c } c (4) D c = max { x c } − min { x c } + where c is one of { left, centroid, right } and x is the xcoordinate for the corresponding point. c @@ -385,8 +385,6 @@ phan cell. Aditional images with examples of TableFormer predictions and post-processing can be found below. - - Figure 8: Example of a table with multi-line header. @@ -395,30 +393,32 @@ Figure 9: Example of a table with big empty distance between cells. -Figure 10: Example of a complex table with empty cells. - - +Figure 10: Example of a complex table with empty cells. Figure 11: Simple table with different style and empty cells. + + Figure 12: Simple table predictions and post processing. Figure 13: Table predictions example on colorful table. -Figure 14: Example with multi-line text. - -Figure 15: Example with triangular table. +Figure 14: Example with multi-line text. -Figure 16: Example of how post-processing helps to restore mis-aligned bounding boxes prediction artifact. +Figure 15: Example with triangular table. +Figure 16: Example of how post-processing helps to restore mis-aligned bounding boxes prediction artifact. + Figure 17: Example of long table. End-to-end example from initial PDF cells to prediction of bounding boxes, post processing and prediction of structure. + + diff --git a/tests/pdf_snapshots/pdf/sources/2206.01062.pdf.md b/tests/pdf_snapshots/pdf/sources/2206.01062.pdf.md index 5bc3f827..cf200820 100644 --- a/tests/pdf_snapshots/pdf/sources/2206.01062.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/2206.01062.pdf.md @@ -30,7 +30,7 @@ Peter Staar IBM Research Rueschlikon, Switzerland taa@zurich.ibm.com - +Figure 1: Four examples of complex page layouts across different document categories @@ -38,7 +38,7 @@ Peter Staar IBM Research Rueschlikon, Switzerland taa@zurich.ibm.com -Figure 1: Four examples of complex page layouts across different document categories + ## KEYWORDS @@ -84,10 +84,10 @@ DocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of hum In addition to open intellectual property constraints for the source documents, we required that the documents in DocLayNet adhere to a few conditions. Firstly, we kept scanned documents - - Figure 2: Distribution of DocLayNet pages across document categories. + + to a minimum, since they introduce difficulties in annotation (see Section 4). As a second condition, we focussed on medium to large documents ( > 10 pages) with technical content, dense in complex tables, figures, plots and captions. Such documents carry a lot of information value, but are often hard to analyse with high accuracy due to their challenging layouts. Counterexamples of documents not included in the dataset are receipts, invoices, hand-written documents or photographs showing "text in the wild". The pages in DocLayNet can be grouped into six distinct categories, namely Financial Reports Manuals Scientific Articles Laws & Regulations Patents and Government Tenders , , . Each document cate, gory was sourced from various repositories. For example, Financial , Reports contain both free-style format annual reports 2 which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories ( Financial Reports and Manuals ) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes. @@ -126,6 +126,8 @@ Table 1: DocLayNet dataset overview. Along with the frequency of each class labe | Title | | 5071 | | 0.47 0.30 0.50 | | 60-72 24-63 50-63 94-100 82-96 68-79 24-56 | | | Total | 1107470 | | 941123 99816 66531 | | | 82-83 71-74 79-81 89-94 86-91 71-76 68-85 | | +Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right. + include publication repositories such as arXiv 3 , government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process. @@ -134,8 +136,6 @@ Preparation work included uploading and parsing the sourced PDF documents in the Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption Footnote Formula List-item Page, , , , footer, r, Page-header, r, Picture , Section-header, r, Table , Text, t, and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on -Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right. - we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised. Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources @@ -159,10 +159,10 @@ The complete annotation guideline is over 100 pages long and a detailed descript Phase 3: Training. After a first trial with a small group of people, we realised that providing the annotation guideline and a set of random practice pages did not yield the desired quality level for layout annotation. Therefore we prepared a subset of pages with two different complexity levels, each with a practice and an exam part. 974 pages were reference-annotated by one proficient core team member. Annotation staff were then given the task to annotate the same subsets (blinded from the reference). By comparing the annotations of each staff member with the reference annotations, we could quantify how closely their annotations matched the reference. Only after passing two exam levels with high annotation quality, staff were admitted into the production phase. Practice iterations - - Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous. + + were carried out over a timeframe of 12 weeks, after which 8 of the 40 initially allocated annotators did not pass the bar. Phase 4: Production annotation. The previously selected 80K pages were annotated with the defined 11 class labels by 32 annotators. This production phase took around three months to complete. All annotations were created online through CCS, which visualises the programmatic PDF text-cells as an overlay on the page. The page annotation are obtained by drawing rectangular bounding-boxes, as shown in Figure 3. With regard to the annotation practices, we implemented a few constraints and capabilities on the tooling level. First, we only allow non-overlapping, vertically oriented, rectangular boxes. For the large majority of documents, this constraint was sufficient and it speeds up the annotation considerably in comparison with arbitrary segmentation shapes. Second, annotator staff were not able to see each other's annotations. This was enforced by design to avoid any bias in the annotation, which could skew the numbers of the inter-annotator agreement (see Table 1). We wanted @@ -191,10 +191,10 @@ to avoid this at any cost in order to have clear, unbiased baseline numbers for The primary goal of DocLayNet is to obtain high-quality ML models capable of accurate document-layout analysis on a wide variety of challenging layouts. As discussed in Section 2, object detection models are currently the easiest to use, due to the standardisation of ground-truth data in COCO format [ 16 ] and the availability of general frameworks such as detectron2 [ 17 ]. Furthermore, baseline numbers in PubLayNet and DocBank were obtained using standard object detection models such as Mask R-CNN and Faster R-CNN. As such, we will relate to these object detection methods in this - - Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNN network with ResNet50 backbone trained on increasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions. + + paper and leave the detailed evaluation of more recent methods mentioned in Section 2 for future work. In this section, we will present several aspects related to the performance of object detection models on DocLayNet. Similarly as in PubLayNet, we will evaluate the quality of their predictions using mean average precision (mAP) with 10 overlaps that range from 0.5 to 0.95 in steps of 0.05 (mAP@0.5-0.95). These scores are computed by leveraging the evaluation code provided by the COCO API [16]. @@ -309,6 +309,8 @@ To date, there is still a significant gap between human and ML accuracy on the l - [12] Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross B. Girshick. Mask R-CNN. In IEEE International Conference on Computer Vision , ICCV, pages 2980–2988. IEEE Computer Society, Oct 2017. - [13] Glenn Jocher, Alex Stoken, Ayush Chaurasia, Jirka Borovec, NanoCode012, TaoXie, Yonghye Kwon, Kalen Michael, Liu Changyu, Jiacong Fang, Abhiram V, Laughing, tkianai, yxNONG, Piotr Skalski, Adam Hogan, Jebastin Nadar, imyhxy, Lorenzo Mammana, Alex Wang, Cristi Fati, Diego Montes, Jan Hajek, Laurentiu +Figure 6: Example layout predictions on selected pages from the DocLayNet test-set. (A, D) exhibit favourable results on coloured backgrounds. (B, C) show accurate list-item and paragraph differentiation despite densely-spaced lines. (E) demonstrates good table and figure distinction. (F) shows predictions on a Chinese patent with multiple overlaps, label confusion and missing boxes. + Text Caption List Item Formula Table Picture Section Header Page Header Page Footer Title @@ -321,9 +323,6 @@ Diaconu, Mai Thanh Minh, Marc, albinxavi, fatih, oleg, and wanghao yang. ultraly - [17] Yuxin Wu, Alexander Kirillov, Francisco Massa, Wan-Yen Lo, and Ross Girshick. Detectron2, 2019. - [18] Nikolaos Livathinos, Cesar Berrospi, Maksym Lysak, Viktor Kuropiatnyk, Ahmed Nassar, Andre Carvalho, Michele Dolfi, Christoph Auer, Kasper Dinkla, and Peter W. J. Staar. Robust pdf document conversion using recurrent neural networks. In Proceedings of the 35th Conference on Artificial Intelligence , AAAI, pages 15137– 15145, feb 2021. - [19] Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, and Ming Zhou. Layoutlm: Pre-training of text and layout for document image understanding. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , KDD, pages 1192–1200, New York, USA, 2020. Association for Computing Machinery. - -Figure 6: Example layout predictions on selected pages from the DocLayNet test-set. (A, D) exhibit favourable results on coloured backgrounds. (B, C) show accurate list-item and paragraph differentiation despite densely-spaced lines. (E) demonstrates good table and figure distinction. (F) shows predictions on a Chinese patent with multiple overlaps, label confusion and missing boxes. - - [20] Shoubin Li, Xuyan Ma, Shuaiqun Pan, Jun Hu, Lin Shi, and Qing Wang. Vtlayout: Fusion of visual and text features for document layout analysis, 2021. - [21] Peng Zhang, Can Li, Liang Qiao, Zhanzhan Cheng, Shiliang Pu, Yi Niu, and Fei Wu. Vsr: A unified framework for document layout analysis combining vision, semantics and relations, 2021. - [22] Peter W J Staar, Michele Dolfi, Christoph Auer, and Costas Bekas. Corpus conversion service: A machine learning platform to ingest documents at scale. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , KDD, pages 774–782. ACM, 2018. diff --git a/tests/pdf_snapshots/pdf/sources/amt_handbook_sample.pdf.md b/tests/pdf_snapshots/pdf/sources/amt_handbook_sample.pdf.md index 33ecc611..5735bdad 100644 --- a/tests/pdf_snapshots/pdf/sources/amt_handbook_sample.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/amt_handbook_sample.pdf.md @@ -12,10 +12,10 @@ The spring, through the medium of the locking section, exerts a constant locking Boots self-locking nuts are made with three different spring styles and in various shapes and sizes. The wing type that is - - Figure 7-26. Self-locking nuts. + + the most common ranges in size for No. 6 up to 1 ⁄ 1 ⁄4 ⁄4 inch, the Rol-top ranges from 1 ⁄ 1 ⁄4 inch to 1 ⁄ 1 ⁄6 inch, and the bellows type ⁄4 ⁄6 ranges in size from No. 8 up to 3 ⁄ 3 ⁄8 ⁄8 inch. Wing-type nuts are made of anodized aluminum alloy, cadmium-plated carbon steel, or stainless steel. The Rol-top nut is cadmium-plated steel, and the bellows type is made of aluminum alloy only. ## Stainless Steel Self-Locking Nut @@ -26,6 +26,6 @@ The stainless steel self-locking nut may be spun on and off by hand as its locki The elastic stop nut is a standard nut with the height increased to accommodate a fiber locking collar. This - - Figure 7-27. Stainless steel self-locking nut. + + diff --git a/tests/pdf_snapshots/pdf/sources/code_and_formula.pdf.md b/tests/pdf_snapshots/pdf/sources/code_and_formula.pdf.md index 59a539e2..b1d6bf8b 100644 --- a/tests/pdf_snapshots/pdf/sources/code_and_formula.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/code_and_formula.pdf.md @@ -18,7 +18,7 @@ Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt. -a 2 + 8 = 12 + Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. diff --git a/tests/pdf_snapshots/pdf/sources/picture_classification.pdf.md b/tests/pdf_snapshots/pdf/sources/picture_classification.pdf.md index 3aa344fb..b12796ac 100644 --- a/tests/pdf_snapshots/pdf/sources/picture_classification.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/picture_classification.pdf.md @@ -2,16 +2,16 @@ Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. - - Figure 1: This is an example image. + + Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. - - Figure 2: This is an example image. + + Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. diff --git a/tests/pdf_snapshots/pdf/sources/redp5110_sampled.pdf.md b/tests/pdf_snapshots/pdf/sources/redp5110_sampled.pdf.md index fc8540a9..a248ba77 100644 --- a/tests/pdf_snapshots/pdf/sources/redp5110_sampled.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/redp5110_sampled.pdf.md @@ -131,10 +131,10 @@ Using SQL views to limit access to a subset of the data in a table also has its Even if you are willing to live with these perf ormance and management issues, a user with *ALLOBJ access still can directly access all of th e data in the underlying DB2 table and easily bypass the security controls that are built into an SQL view. - - Figure 1-2 Existing row and column controls + + ## 2.1.6 Change Function Usage CL command The following CL commands can be used to work with, display, or change function usage IDs: @@ -214,10 +214,10 @@ Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority The SQL CREATE PERMISSION statement that is shown in Figure 3-1 is used to define and initially enable or disable the row access rules. - - Figure 3-1 CREATE PER ERMISSION SQL statement + + ## Column mask A column mask is a database object that manifests a column value access control rule for a specific column in a specific table. It uses a CASE expression that describes what you see when you access the column. For example, a teller can see only the last four digits of a tax identification number. @@ -242,10 +242,10 @@ Figure 3-5 shows the difference in the special register values when an adopted a - While the procedure is running, the special register USER still contains the value of ALICE because it excludes any adopted authority. The special register CURRENT USER contains the value of JOE because it includes any adopted authority. - When proc1 ends, the session reverts to its original state with both USER and CURRENT USER having the value of ALICE. - - Figure 3-5 Special registers and adopted authority + + ## 3.2.2 Built-in global variables Built-in global variables are provided with the database manager and are used in SQL statements to retrieve scalar values that are associated with the variables. @@ -302,10 +302,10 @@ CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ON HR_SCHEMA.EMPLOYEES AS EMPLOYE - 3. Figure 3-10 shows the masks that are created in the HR_SCHEMA. - - Figure 3-10 Column masks shown in System i Navigator + + ## 3.6.6 Activating RCAC Now that you have created the row permission and the two column masks, RCAC must be activated. The row permission and the two column masks are enabled (last clause in the scripts), but now you must activate RCAC on the table. To do so, complete the following steps: @@ -318,22 +318,22 @@ Example 3-10 Activating RCAC on the EMPLOYEES table - 2. Look at the definitio n of the EMPLOYEE table, as shown in Figure 3-11. To do this, from the main navigation pane of System i Navigator, click Schemas  HR_SCHEMA  Tables , right-click the EMPLOYEES table, and click Definition . - - Figure 3-11 Selecting the EMPL OYEES table from System i Navigator -- 2. Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC enabled. It is clear that the implementation of the SQL statement is more complex because the row permission rule becomes part of the WHERE clause. - -Figure 4-68 Visual Explain with RCAC enabled +- 2. Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC enabled. It is clear that the implementation of the SQL statement is more complex because the row permission rule becomes part of the WHERE clause. -- 3. Compare the advised indexes that are provided by the Optimizer without RCAC and with RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC enabled. The index being advised is for the ORDER BY clause. +Figure 4-68 Visual Explain with RCAC enabled +- 3. Compare the advised indexes that are provided by the Optimizer without RCAC and with RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC enabled. The index being advised is for the ORDER BY clause. + Figure 4-69 Index advice with no RCAC + + THEN C . CUSTOMER_TAX_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ELSE '*************' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_LOGIN_ID RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_LOGIN_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_LOGIN_ID ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ELSE '*****' END ENABLE ; ALTER TABLE BANK_SCHEMA.CUSTOMERS ACTIVATE ROW ACCESS CONTROL ACTIVATE COLUMN ACCESS CONTROL ; ## Row and Column Access Control Support in IBM DB2 for i diff --git a/tests/pdf_snapshots/pdf/sources/right_to_left_02.pdf.md b/tests/pdf_snapshots/pdf/sources/right_to_left_02.pdf.md index c9b548e4..9b84d40b 100644 --- a/tests/pdf_snapshots/pdf/sources/right_to_left_02.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/right_to_left_02.pdf.md @@ -4,6 +4,6 @@ - 2024 ) ووف ًخ لمخ سبق، يسولاد برنوخما الحكوموة المصورية لوال ال لور : تح يق عربعة عهدا اسلراتيجية رئيسة، وها ياى الهحو اآلتا ( 2026 -قرار تحقيق اظستقر مايــــــــة اممــــــــن بنـــــاء ا تصـــــاع بنـــاء ا نســـا السياســــــــــــــــــــــــي القومي المصـر تنابســــــــــــــــــــــي المصـــــــــــــــــــر + د باوكل تجدر اإل خر إلى عنه قد تل تحديد مسولادفخت البرنوخما بخحسولهخ وتوصووويخت واسوووخت الحووووار ، 2023 رئووويس ياوووى مسووولادفخت ر يوووة مصووور كايوة، الوو،ها، ومسولادفخت الوو ارات، والبرنوخما الوو،ها ليصوا خت الايكا ومبلاف احسلراتيجيخت الو،هية . diff --git a/tests/pdf_snapshots/pdf/sources/skipped_1page.pdf.md b/tests/pdf_snapshots/pdf/sources/skipped_1page.pdf.md index 381e8b76..196f7854 100644 --- a/tests/pdf_snapshots/pdf/sources/skipped_1page.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/skipped_1page.pdf.md @@ -14,15 +14,15 @@ - +엣지컴퓨팅 (Edge Computing) - +차세대 AI (Next AI) -엣지컴퓨팅 (Edge Computing) + -차세대 AI (Next AI) + (DATA) @@ -34,10 +34,10 @@ - - 차세대 AI (Next AI) + +  ## 젊기몒 인터페이스 몋에컪픦 캏오윦이캏맞지 기술 @@ -46,12 +46,12 @@ +데이터 (DATA) + 웨어러블 컨트롤 인터페이스 -데이터 (DATA) - (Wearable Control ) Interfaces) ## [그림 4-5] 8대 미래 유망 보안기술 도출 diff --git a/tests/pdf_snapshots/pdf/sources/skipped_2pages.pdf.md b/tests/pdf_snapshots/pdf/sources/skipped_2pages.pdf.md index 129c9d27..92893d77 100644 --- a/tests/pdf_snapshots/pdf/sources/skipped_2pages.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/skipped_2pages.pdf.md @@ -20,15 +20,15 @@ - +엣지컴퓨팅 (Edge Computing) - +차세대 AI (Next AI) -엣지컴퓨팅 (Edge Computing) + -차세대 AI (Next AI) + (DATA) @@ -40,10 +40,10 @@ - - 차세대 AI (Next AI) + +  ## 젊기몒 인터페이스 몋에컪픦 캏오윦이캏맞지 기술 @@ -52,12 +52,12 @@ +데이터 (DATA) + 웨어러블 컨트롤 인터페이스 -데이터 (DATA) - (Wearable Control ) Interfaces) ## [그림 4-5] 8대 미래 유망 보안기술 도출 diff --git a/tests/pdf_snapshots/scanned/sources/sample_with_rotation_mismatch.pdf.md b/tests/pdf_snapshots/scanned/sources/sample_with_rotation_mismatch.pdf.md index 43218e15..2e0fcf82 100644 --- a/tests/pdf_snapshots/scanned/sources/sample_with_rotation_mismatch.pdf.md +++ b/tests/pdf_snapshots/scanned/sources/sample_with_rotation_mismatch.pdf.md @@ -28,6 +28,6 @@ Probably you have uses for this facility in your organisation. Yours sincerely, - - P.J.CROSS Group Leader - Facsimile Research + + diff --git a/tests/pdf_snapshots/tiff/sources/2206.01062.tif.md b/tests/pdf_snapshots/tiff/sources/2206.01062.tif.md index c8a9eb5f..f0639e7f 100644 --- a/tests/pdf_snapshots/tiff/sources/2206.01062.tif.md +++ b/tests/pdf_snapshots/tiff/sources/2206.01062.tif.md @@ -28,7 +28,7 @@ PeterStaar IBMResearch Rueschlikon,Switzerland taa@zurich.ibm.com - +Figure 1: Four examples of complex page layouts across dif ferent document categories @@ -36,7 +36,7 @@ PeterStaar IBMResearch Rueschlikon,Switzerland taa@zurich.ibm.com -Figure 1: Four examples of complex page layouts across dif ferent document categories + ## KEYWORDS From 7d565a91f05047b586aae5f99f01f111afbf9732 Mon Sep 17 00:00:00 2001 From: artiz Date: Sun, 28 Jun 2026 12:40:01 +0200 Subject: [PATCH 03/42] feat(markdown): strict mode tightens punctuation spacing MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Strict mode already diverges from docling for readability (underscore un-escaping, code-fence languages). Extend it to tighten stray spaces around punctuation: `[ 37 , 36 ]` → `[37, 36]`, `( x )` → `(x)`, `*c* .` → `*c*.`. This cleans up both the PDF backend's glyph-split citation spacing and the space the legacy emphasis serialization leaves before punctuation. Default/legacy output is untouched (still byte-for-byte with docling, which keeps those spaces). Only inline text nodes are affected — code blocks and table cells are left alone. Regenerated the strict regression fixtures; added a unit test pinning legacy-spacing vs strict-tightened. Co-Authored-By: Claude Opus 4.8 (1M context) --- crates/fleischwolf-core/src/markdown.rs | 35 ++- .../expected/docx_checkboxes.docx.strict.md | 2 +- .../expected/docx_rich_cells.docx.strict.md | 2 +- .../docx/expected/equations.docx.strict.md | 8 +- .../data/docx/expected/textbox.docx.strict.md | 2 +- .../epub_purvis_poetry.epub.strict.md | 32 +-- .../html/expected/formatting.html.strict.md | 12 +- .../html_code_snippets.html.strict.md | 6 +- .../html/expected/hyperlink_01.html.strict.md | 2 +- .../html/expected/hyperlink_06.html.strict.md | 4 +- .../html/expected/wiki_duck.html.strict.md | 202 +++++++++--------- .../jats/expected/elife-56337.nxml.strict.md | 2 +- .../jats/expected/elife-56337.xml.strict.md | 2 +- .../expected/text_document_01.odt.strict.md | 2 +- .../expected/text_document_03.odt.strict.md | 2 +- .../powerpoint_comments.pptx.strict.md | 2 +- .../expected/ipa20110039701.xml.strict.md | 2 +- .../uspto/expected/ipg07997973.xml.strict.md | 2 +- .../expected/webvtt_example_02.vtt.strict.md | 2 +- .../xbrl/expected/mlac-20251231.xml.strict.md | 4 +- 20 files changed, 173 insertions(+), 154 deletions(-) diff --git a/crates/fleischwolf-core/src/markdown.rs b/crates/fleischwolf-core/src/markdown.rs index 21024d34..8678dca1 100644 --- a/crates/fleischwolf-core/src/markdown.rs +++ b/crates/fleischwolf-core/src/markdown.rs @@ -61,16 +61,25 @@ pub fn to_markdown_images( (md, ctx.artifacts) } -/// In `strict` mode, undo the legacy `\_` underscore escaping the backends bake -/// into inline text. Legacy output keeps `\_` (byte-for-byte with docling, which -/// escapes underscores); strict prefers literal `_` for readability. Only inline -/// text nodes are escaped — code blocks and table cells are left alone. +/// In `strict` mode, rewrite inline text for readability rather than byte-for-byte +/// docling fidelity: undo the legacy `\_` underscore escaping, and tighten stray +/// spaces around punctuation (`[ 37 , 36 ]` → `[37, 36]`, `( x )` → `(x)`). This +/// cleans up both the PDF backend's glyph-split spacing and the space the legacy +/// emphasis serialization leaves before punctuation (`*a* ,` → `*a*,`). +/// Legacy/default output keeps docling's spacing untouched. Only inline text +/// nodes pass through here — code blocks and table cells are left alone. fn strict_text(text: &str, strict: bool) -> String { - if strict { - text.replace("\\_", "_") - } else { - text.to_string() + if !strict { + return text.to_string(); } + text.replace("\\_", "_") + .replace(" ,", ",") + .replace(" .", ".") + .replace(" ;", ";") + .replace(" )", ")") + .replace("( ", "(") + .replace(" ]", "]") + .replace("[ ", "[") } fn render(nodes: &[Node], blocks: &mut Vec, ctx: &mut Ctx) { @@ -363,4 +372,14 @@ mod tests { // Strict prefers literal underscores (Rust-only readability mode). assert_eq!(doc.export_to_markdown_with(true), "# a_b\n\nx_y\n\n- i_j\n"); } + + #[test] + fn strict_tightens_punctuation_spacing_legacy_keeps_it() { + let mut doc = DoclingDocument::new("t"); + doc.add_paragraph("see [ 37 , 36 ] and ( x ) ."); + // Legacy keeps docling's spacing byte-for-byte. + assert_eq!(doc.export_to_markdown(), "see [ 37 , 36 ] and ( x ) .\n"); + // Strict tightens punctuation for readable Markdown. + assert_eq!(doc.export_to_markdown_with(true), "see [37, 36] and (x).\n"); + } } diff --git a/crates/fleischwolf/tests/data/docx/expected/docx_checkboxes.docx.strict.md b/crates/fleischwolf/tests/data/docx/expected/docx_checkboxes.docx.strict.md index 1b8b2196..fc903c1a 100644 --- a/crates/fleischwolf/tests/data/docx/expected/docx_checkboxes.docx.strict.md +++ b/crates/fleischwolf/tests/data/docx/expected/docx_checkboxes.docx.strict.md @@ -14,4 +14,4 @@ Tasks before release: - [x] Implementation -- [ ] Documentation of the task with a very long text that goes beyond the maximum length of a single line. +- [] Documentation of the task with a very long text that goes beyond the maximum length of a single line. diff --git a/crates/fleischwolf/tests/data/docx/expected/docx_rich_cells.docx.strict.md b/crates/fleischwolf/tests/data/docx/expected/docx_rich_cells.docx.strict.md index ea17fcd1..41456194 100644 --- a/crates/fleischwolf/tests/data/docx/expected/docx_rich_cells.docx.strict.md +++ b/crates/fleischwolf/tests/data/docx/expected/docx_rich_cells.docx.strict.md @@ -15,7 +15,7 @@ Before table | Simple cell upper left | Simple cell with **bold** and *italic* text | | A B C Cell 1 Cell 2 Cell 3 | Rich cell A nested table A B C Cell 1 Cell 2 Cell 3 | -After table with **bold** , underline , ~~strikethrough~~ , and *italic* formatting +After table with **bold**, underline, ~~strikethrough~~, and *italic* formatting ### Table with pictures diff --git a/crates/fleischwolf/tests/data/docx/expected/equations.docx.strict.md b/crates/fleischwolf/tests/data/docx/expected/equations.docx.strict.md index bb8602c5..404e021f 100644 --- a/crates/fleischwolf/tests/data/docx/expected/equations.docx.strict.md +++ b/crates/fleischwolf/tests/data/docx/expected/equations.docx.strict.md @@ -1,4 +1,4 @@ -This is a word document and this is an inline equation: $A= \pi r^{2}$ . +This is a word document and this is an inline equation: $A= \pi r^{2}$. First item with inline equation: $A= \pi r^{2}$ is the area formula. @@ -18,7 +18,7 @@ $$f\left(x\right)=a_{0}+\sum_{n=1}^{ \infty }\left(a_{n}\cos(\frac{n \pi x}{L})+ This is text. This is text. This is text. This is text. This is text. This is text. This is text. This is text. This is text. This is text. This is text. This is text. This is text. This is text. This is text. This is text. This is text. This is text. This is text. This is text. This is text. This is text. This is text. This is text. This is text. This is text. This is text. This is text. This is text. This is text. This is text. -This is a word document and this is an inline equation: $A= \pi r^{2}$ . If instead, I want an equation by line, I can do this: +This is a word document and this is an inline equation: $A= \pi r^{2}$. If instead, I want an equation by line, I can do this: $$\left(x+a\right)^{n}=\sum_{k=0}^{n}\left(\genfrac{}{}{0pt}{}{n}{k}\right)x^{k}a^{n-k}$$ @@ -32,7 +32,7 @@ This is text. This is text. This is text. This is text. This is text. This is te This is a word document and these are inline equations: $N_{s}^{H}$ / $N_{s}^{P}$ ​. If instead, I want an equation by line, I can do this: -$$e^{x}=1+\frac{x}{1!}+\frac{x^{2}}{2!}+\frac{x^{3}}{3!}+ \text{ \textellipsis } , - \infty @@ -10,13 +10,13 @@ The Standard Ebooks logo. -This ebook is the product of many hours of hard work by volunteers for [Standard Ebooks](https://standardebooks.org/) , and builds on the hard work of other literature lovers made possible by the public domain. +This ebook is the product of many hours of hard work by volunteers for [Standard Ebooks](https://standardebooks.org/), and builds on the hard work of other literature lovers made possible by the public domain. -This particular ebook is based on digital scans from the [Internet Archive](https://standardebooks.org/ebooks/sarah-louisa-forten-purvis/poetry#page-scans) . +This particular ebook is based on digital scans from the [Internet Archive](https://standardebooks.org/ebooks/sarah-louisa-forten-purvis/poetry#page-scans). -The source text and artwork in this ebook are believed to be in the United States public domain; that is, they are believed to be free of copyright restrictions in the United States. They may still be copyrighted in other countries, so users located outside of the United States must check their local laws before using this ebook. The creators of, and contributors to, this ebook dedicate their contributions to the worldwide public domain via the terms in the [CC0 1.0 Universal Public Domain Dedication](https://creativecommons.org/publicdomain/zero/1.0/) . For full license information, see the [Uncopyright](uncopyright.xhtml) at the end of this ebook. +The source text and artwork in this ebook are believed to be in the United States public domain; that is, they are believed to be free of copyright restrictions in the United States. They may still be copyrighted in other countries, so users located outside of the United States must check their local laws before using this ebook. The creators of, and contributors to, this ebook dedicate their contributions to the worldwide public domain via the terms in the [CC0 1.0 Universal Public Domain Dedication](https://creativecommons.org/publicdomain/zero/1.0/). For full license information, see the [Uncopyright](uncopyright.xhtml) at the end of this ebook. -Standard Ebooks is a volunteer-driven project that produces ebook editions of public domain literature using modern typography, technology, and editorial standards, and distributes them free of cost. You can download this and other ebooks carefully produced for true book lovers at [standardebooks.org](https://standardebooks.org/) . +Standard Ebooks is a volunteer-driven project that produces ebook editions of public domain literature using modern typography, technology, and editorial standards, and distributes them free of cost. You can download this and other ebooks carefully produced for true book lovers at [standardebooks.org](https://standardebooks.org/). ## The Grave of the Slave @@ -200,7 +200,7 @@ This bursting heart relief? There's nothing left for me to love This earth holds nothing dear, -Since *he* , my sweet-my gentle one, +Since *he*, my sweet-my gentle one, Is now no longer here. My poor fond heart had counted on @@ -465,34 +465,34 @@ The Standard Ebooks logo. *Poetry* was compiled from poems published between 1831 and 1836 by -[Sarah Louisa Forten Purvis](https://en.wikipedia.org/wiki/Sarah_Louisa_Forten_Purvis) . +[Sarah Louisa Forten Purvis](https://en.wikipedia.org/wiki/Sarah_Louisa_Forten_Purvis). This ebook was transcribed and produced for [Standard Ebooks](https://standardebooks.org/) by -[Weijia Cheng](https://weijiarhymeswith.asia/) , +[Weijia Cheng](https://weijiarhymeswith.asia/), and is based on digital scans from the -[Internet Archive](https://standardebooks.org/ebooks/sarah-louisa-forten-purvis/poetry#page-scans) . +[Internet Archive](https://standardebooks.org/ebooks/sarah-louisa-forten-purvis/poetry#page-scans). The cover page is adapted from -*Sunday Morning* , +*Sunday Morning*, a painting completed in 1877 by -[Thomas Waterman Wood](https://en.wikipedia.org/wiki/Thomas_Waterman_Wood) . +[Thomas Waterman Wood](https://en.wikipedia.org/wiki/Thomas_Waterman_Wood). The cover and title pages feature the **League Spartan** and **Sorts Mill Goudy** typefaces created in 2014 and 2009 by -[The League of Moveable Type](https://www.theleagueofmoveabletype.com/) . +[The League of Moveable Type](https://www.theleagueofmoveabletype.com/). This edition was released on **May 21, 2026, 6:29** **p.m.** and is based on -**revision 936122b** . +**revision 936122b**. The first edition of this ebook was released on December 6, 2023, 8:52 p.m. You can check for updates to this ebook, view its revision history, or download it for different ereading systems at -[standardebooks.org/ebooks/sarah-louisa-forten-purvis/poetry](https://standardebooks.org/ebooks/sarah-louisa-forten-purvis/poetry) . +[standardebooks.org/ebooks/sarah-louisa-forten-purvis/poetry](https://standardebooks.org/ebooks/sarah-louisa-forten-purvis/poetry). -The volunteer-driven Standard Ebooks project relies on readers like you to submit typos, corrections, and other improvements. Anyone can contribute at [standardebooks.org](https://standardebooks.org/) . +The volunteer-driven Standard Ebooks project relies on readers like you to submit typos, corrections, and other improvements. Anyone can contribute at [standardebooks.org](https://standardebooks.org/). ## Uncopyright @@ -504,4 +504,4 @@ Copyright pages exist to tell you that you *can't* do something. Unlike them, th Copyright laws are different all over the world, and the source text or artwork in this ebook may still be copyrighted in other countries. If you're not located in the United States, you must check your local laws before using this ebook. Standard Ebooks makes no representations regarding the copyright status of the source text or artwork in this ebook in any country other than the United States. -Non-authorship activities performed on items that are in the public domain-so-called "sweat of the brow" work-don't create a new copyright. That means that nobody can claim a new copyright on an item that is in the public domain for, among other things, work like digitization, markup, or typography. Regardless, the contributors to this ebook release their contributions under the terms in the [CC0 1.0 Universal Public Domain Dedication](https://creativecommons.org/publicdomain/zero/1.0/) , thus dedicating to the worldwide public domain all of the work they've done on this ebook, including but not limited to metadata, the titlepage, imprint, colophon, this Uncopyright, and any changes or enhancements to, or markup on, the original text and artwork. This dedication doesn't change the copyright status of the source text or artwork. We make this dedication in the interest of enriching our global cultural heritage, to promote free and libre culture around the world, and to give back to the unrestricted culture that has given all of us so much. +Non-authorship activities performed on items that are in the public domain-so-called "sweat of the brow" work-don't create a new copyright. That means that nobody can claim a new copyright on an item that is in the public domain for, among other things, work like digitization, markup, or typography. Regardless, the contributors to this ebook release their contributions under the terms in the [CC0 1.0 Universal Public Domain Dedication](https://creativecommons.org/publicdomain/zero/1.0/), thus dedicating to the worldwide public domain all of the work they've done on this ebook, including but not limited to metadata, the titlepage, imprint, colophon, this Uncopyright, and any changes or enhancements to, or markup on, the original text and artwork. This dedication doesn't change the copyright status of the source text or artwork. We make this dedication in the interest of enriching our global cultural heritage, to promote free and libre culture around the world, and to give back to the unrestricted culture that has given all of us so much. diff --git a/crates/fleischwolf/tests/data/html/expected/formatting.html.strict.md b/crates/fleischwolf/tests/data/html/expected/formatting.html.strict.md index 2e292d5d..ecf0484b 100644 --- a/crates/fleischwolf/tests/data/html/expected/formatting.html.strict.md +++ b/crates/fleischwolf/tests/data/html/expected/formatting.html.strict.md @@ -1,13 +1,13 @@ # HTML Text Formatting Examples -This is a **bold (b)** example and right next to it we have a **strong emphasis (strong)** . Notice that **strong + bold mixed** looks similar but carries additional semantic meaning. +This is a **bold (b)** example and right next to it we have a **strong emphasis (strong)**. Notice that **strong + bold mixed** looks similar but carries additional semantic meaning. -Here is an *italic (i)* word and an *emphasis (em)* example. Sometimes we combine them like *italic + emphasis together* . +Here is an *italic (i)* word and an *emphasis (em)* example. Sometimes we combine them like *italic + emphasis together*. -Now let's look at text that appears crossed out: ~~strikethrough with s~~ and ~~deleted with del~~ . You can also mix them: ~~double strikethrough (s + del)~~ . +Now let's look at text that appears crossed out: ~~strikethrough with s~~ and ~~deleted with del~~. You can also mix them: ~~double strikethrough (s + del)~~. -To highlight insertions or underlines: underlined with u , inserted with ins . A combination could be: underline + insertion together . +To highlight insertions or underlines: underlined with u, inserted with ins. A combination could be: underline + insertion together. -Subscript and superscript examples: Water is written as H 2 O using sub. The mathematical expression x 2 + y 3 uses sup. They can also be combined: CO 2 * . +Subscript and superscript examples: Water is written as H 2 O using sub. The mathematical expression x 2 + y 3 uses sup. They can also be combined: CO 2 *. -Mixing several: This sentence has ***strong + emphasis*** , some **bold + underline** , and a formula like a 2 + b 3 . +Mixing several: This sentence has ***strong + emphasis***, some **bold + underline**, and a formula like a 2 + b 3. diff --git a/crates/fleischwolf/tests/data/html/expected/html_code_snippets.html.strict.md b/crates/fleischwolf/tests/data/html/expected/html_code_snippets.html.strict.md index cd5b4347..2054b772 100644 --- a/crates/fleischwolf/tests/data/html/expected/html_code_snippets.html.strict.md +++ b/crates/fleischwolf/tests/data/html/expected/html_code_snippets.html.strict.md @@ -1,10 +1,10 @@ # Code snippets -The Pythagorean theorem can be written as an equation relating the lengths of the sides *a* , *b* and the hypotenuse *c* . +The Pythagorean theorem can be written as an equation relating the lengths of the sides *a*, *b* and the hypotenuse *c*. To use Docling, simply install `docling` from your package manager, e.g. pip: `pip install docling` -To convert individual documents with python, use `convert()` , for example: +To convert individual documents with python, use `convert()`, for example: ``` from docling.document_converter import DocumentConverter @@ -20,5 +20,5 @@ The program will output: `## Docling Technical Report[...]` Prefetch the models: - Use the `docling-tools models download` utility: -- Alternatively, models can be programmatically downloaded using `docling.utils.model_downloader.download_models()` . +- Alternatively, models can be programmatically downloaded using `docling.utils.model_downloader.download_models()`. - Also, you can use download-hf-repo parameter to download arbitrary models from HuggingFace by specifying repo id: `$ docling-tools models download-hf-repo ds4sd/SmolDocling-256M-preview Downloading ds4sd/SmolDocling-256M-preview model from HuggingFace...` diff --git a/crates/fleischwolf/tests/data/html/expected/hyperlink_01.html.strict.md b/crates/fleischwolf/tests/data/html/expected/hyperlink_01.html.strict.md index da30dbf7..e3386f4f 100644 --- a/crates/fleischwolf/tests/data/html/expected/hyperlink_01.html.strict.md +++ b/crates/fleischwolf/tests/data/html/expected/hyperlink_01.html.strict.md @@ -1,3 +1,3 @@ # Something -Please follow the link to: [This page](#) . +Please follow the link to: [This page](#). diff --git a/crates/fleischwolf/tests/data/html/expected/hyperlink_06.html.strict.md b/crates/fleischwolf/tests/data/html/expected/hyperlink_06.html.strict.md index dfec31f5..29dd67c0 100644 --- a/crates/fleischwolf/tests/data/html/expected/hyperlink_06.html.strict.md +++ b/crates/fleischwolf/tests/data/html/expected/hyperlink_06.html.strict.md @@ -1,7 +1,7 @@ # Anchor Links Test -Jump to [Section 2](#section-2) or visit [Example](https://example.com/) . +Jump to [Section 2](#section-2) or visit [Example](https://example.com/). ## Section 2 -Content for section 2 with a [top link](#) . +Content for section 2 with a [top link](#). diff --git a/crates/fleischwolf/tests/data/html/expected/wiki_duck.html.strict.md b/crates/fleischwolf/tests/data/html/expected/wiki_duck.html.strict.md index 9d69a560..9f95910a 100644 --- a/crates/fleischwolf/tests/data/html/expected/wiki_duck.html.strict.md +++ b/crates/fleischwolf/tests/data/html/expected/wiki_duck.html.strict.md @@ -270,13 +270,13 @@ Page semi-protected From Wikipedia, the free encyclopedia -(Redirected from [Duckling](/w/index.php?title=Duckling&redirect=no) ) +(Redirected from [Duckling](/w/index.php?title=Duckling&redirect=no)) Common name for many species of bird -This article is about the bird. For duck as a food, see [Duck as food](/wiki/Duck_as_food) . For other uses, see [Duck (disambiguation)](/wiki/Duck_(disambiguation)) . +This article is about the bird. For duck as a food, see [Duck as food](/wiki/Duck_as_food). For other uses, see [Duck (disambiguation)](/wiki/Duck_(disambiguation)). -"Duckling" redirects here. For other uses, see [Duckling (disambiguation)](/wiki/Duckling_(disambiguation)) . +"Duckling" redirects here. For other uses, see [Duckling (disambiguation)](/wiki/Duckling_(disambiguation)). | Duck | Duck | |-------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------| @@ -293,9 +293,9 @@ This article is about the bird. For duck as a food, see [Duck as food](/wiki/Duc | Subfamilies | Subfamilies | | See text | See text | -**Duck** is the common name for numerous species of [waterfowl](/wiki/Waterfowl) in the [family](/wiki/Family_(biology)) [Anatidae](/wiki/Anatidae) . Ducks are generally smaller and shorter-necked than [swans](/wiki/Swan) and [geese](/wiki/Goose) , which are members of the same family. Divided among several subfamilies, they are a [form taxon](/wiki/Form_taxon) ; they do not represent a [monophyletic group](/wiki/Monophyletic_group) (the group of all descendants of a single common ancestral species), since swans and geese are not considered ducks. Ducks are mostly [aquatic birds](/wiki/Aquatic_bird) , and may be found in both fresh water and sea water. +**Duck** is the common name for numerous species of [waterfowl](/wiki/Waterfowl) in the [family](/wiki/Family_(biology)) [Anatidae](/wiki/Anatidae). Ducks are generally smaller and shorter-necked than [swans](/wiki/Swan) and [geese](/wiki/Goose), which are members of the same family. Divided among several subfamilies, they are a [form taxon](/wiki/Form_taxon); they do not represent a [monophyletic group](/wiki/Monophyletic_group) (the group of all descendants of a single common ancestral species), since swans and geese are not considered ducks. Ducks are mostly [aquatic birds](/wiki/Aquatic_bird), and may be found in both fresh water and sea water. -Ducks are sometimes confused with several types of unrelated water birds with similar forms, such as [loons](/wiki/Loon) or divers, [grebes](/wiki/Grebe) , [gallinules](/wiki/Gallinule) and [coots](/wiki/Coot) . +Ducks are sometimes confused with several types of unrelated water birds with similar forms, such as [loons](/wiki/Loon) or divers, [grebes](/wiki/Grebe), [gallinules](/wiki/Gallinule) and [coots](/wiki/Coot). ## Etymology @@ -305,7 +305,7 @@ Pacific black duck displaying the characteristic upending "duck" -This word replaced Old English *ened* / *ænid* 'duck', possibly to avoid confusion with other words, such as *ende* 'end' with similar forms. Other Germanic languages still have similar words for *duck* , for example, Dutch *eend* , German *Ente* and [Norwegian](/wiki/Norwegian_language) *and* . The word *ened* / *ænid* was inherited from [Proto-Indo-European](/wiki/Proto-Indo-European_language) ; [cf.](/wiki/Cf.) [Latin](/wiki/Latin) *anas* "duck", [Lithuanian](/wiki/Lithuanian_language) *ántis* 'duck', [Ancient Greek](/wiki/Ancient_Greek_language) νῆσσα / νῆττα ( *nēssa* / *nētta* ) 'duck', and [Sanskrit](/wiki/Sanskrit) *ātí* 'water bird', among others. +This word replaced Old English *ened* / *ænid* 'duck', possibly to avoid confusion with other words, such as *ende* 'end' with similar forms. Other Germanic languages still have similar words for *duck*, for example, Dutch *eend*, German *Ente* and [Norwegian](/wiki/Norwegian_language) *and*. The word *ened* / *ænid* was inherited from [Proto-Indo-European](/wiki/Proto-Indo-European_language); [cf.](/wiki/Cf.) [Latin](/wiki/Latin) *anas* "duck", [Lithuanian](/wiki/Lithuanian_language) *ántis* 'duck', [Ancient Greek](/wiki/Ancient_Greek_language) νῆσσα / νῆττα (*nēssa* / *nētta*) 'duck', and [Sanskrit](/wiki/Sanskrit) *ātí* 'water bird', among others. A duckling is a young duck in downy plumage [[](#cite_note-1) [1](#cite_note-1) []](#cite_note-1) or baby duck, [[](#cite_note-2) [2](#cite_note-2) []](#cite_note-2) but in the food trade a young domestic duck which has just reached adult size and bulk and its meat is still fully tender, is sometimes labelled as a duckling. @@ -321,7 +321,7 @@ Wood ducks. ## Taxonomy -All ducks belong to the [biological order](/wiki/Order_(biology)) [Anseriformes](/wiki/Anseriformes) , a group that contains the ducks, geese and swans, as well as the [screamers](/wiki/Screamer) , and the [magpie goose](/wiki/Magpie_goose) . [[](#cite_note-FOOTNOTECarboneras1992536-5) [5](#cite_note-FOOTNOTECarboneras1992536-5) []](#cite_note-FOOTNOTECarboneras1992536-5) All except the screamers belong to the [biological family](/wiki/Family_(biology)) [Anatidae](/wiki/Anatidae) . [[](#cite_note-FOOTNOTECarboneras1992536-5) [5](#cite_note-FOOTNOTECarboneras1992536-5) []](#cite_note-FOOTNOTECarboneras1992536-5) Within the family, ducks are split into a variety of subfamilies and 'tribes'. The number and composition of these subfamilies and tribes is the cause of considerable disagreement among taxonomists. [[](#cite_note-FOOTNOTECarboneras1992536-5) [5](#cite_note-FOOTNOTECarboneras1992536-5) []](#cite_note-FOOTNOTECarboneras1992536-5) Some base their decisions on [morphological characteristics](/wiki/Morphology_(biology)) , others on shared behaviours or genetic studies. [[](#cite_note-FOOTNOTELivezey1986737–738-6) [6](#cite_note-FOOTNOTELivezey1986737–738-6) []](#cite_note-FOOTNOTELivezey1986737–738-6) [[](#cite_note-FOOTNOTEMadsenMcHughde_Kloet1988452-7) [7](#cite_note-FOOTNOTEMadsenMcHughde_Kloet1988452-7) []](#cite_note-FOOTNOTEMadsenMcHughde_Kloet1988452-7) The number of suggested subfamilies containing ducks ranges from two to five. [[](#cite_note-FOOTNOTEDonne-GousséLaudetHänni2002353–354-8) [8](#cite_note-FOOTNOTEDonne-GousséLaudetHänni2002353–354-8) []](#cite_note-FOOTNOTEDonne-GousséLaudetHänni2002353–354-8) [[](#cite_note-FOOTNOTECarboneras1992540-9) [9](#cite_note-FOOTNOTECarboneras1992540-9) []](#cite_note-FOOTNOTECarboneras1992540-9) The significant level of [hybridisation](/wiki/Hybrid_(biology)) that occurs among wild ducks complicates efforts to tease apart the relationships between various species. [[](#cite_note-FOOTNOTECarboneras1992540-9) [9](#cite_note-FOOTNOTECarboneras1992540-9) []](#cite_note-FOOTNOTECarboneras1992540-9) +All ducks belong to the [biological order](/wiki/Order_(biology)) [Anseriformes](/wiki/Anseriformes), a group that contains the ducks, geese and swans, as well as the [screamers](/wiki/Screamer), and the [magpie goose](/wiki/Magpie_goose). [[](#cite_note-FOOTNOTECarboneras1992536-5) [5](#cite_note-FOOTNOTECarboneras1992536-5) []](#cite_note-FOOTNOTECarboneras1992536-5) All except the screamers belong to the [biological family](/wiki/Family_(biology)) [Anatidae](/wiki/Anatidae). [[](#cite_note-FOOTNOTECarboneras1992536-5) [5](#cite_note-FOOTNOTECarboneras1992536-5) []](#cite_note-FOOTNOTECarboneras1992536-5) Within the family, ducks are split into a variety of subfamilies and 'tribes'. The number and composition of these subfamilies and tribes is the cause of considerable disagreement among taxonomists. [[](#cite_note-FOOTNOTECarboneras1992536-5) [5](#cite_note-FOOTNOTECarboneras1992536-5) []](#cite_note-FOOTNOTECarboneras1992536-5) Some base their decisions on [morphological characteristics](/wiki/Morphology_(biology)), others on shared behaviours or genetic studies. [[](#cite_note-FOOTNOTELivezey1986737–738-6) [6](#cite_note-FOOTNOTELivezey1986737–738-6) []](#cite_note-FOOTNOTELivezey1986737–738-6) [[](#cite_note-FOOTNOTEMadsenMcHughde_Kloet1988452-7) [7](#cite_note-FOOTNOTEMadsenMcHughde_Kloet1988452-7) []](#cite_note-FOOTNOTEMadsenMcHughde_Kloet1988452-7) The number of suggested subfamilies containing ducks ranges from two to five. [[](#cite_note-FOOTNOTEDonne-GousséLaudetHänni2002353–354-8) [8](#cite_note-FOOTNOTEDonne-GousséLaudetHänni2002353–354-8) []](#cite_note-FOOTNOTEDonne-GousséLaudetHänni2002353–354-8) [[](#cite_note-FOOTNOTECarboneras1992540-9) [9](#cite_note-FOOTNOTECarboneras1992540-9) []](#cite_note-FOOTNOTECarboneras1992540-9) The significant level of [hybridisation](/wiki/Hybrid_(biology)) that occurs among wild ducks complicates efforts to tease apart the relationships between various species. [[](#cite_note-FOOTNOTECarboneras1992540-9) [9](#cite_note-FOOTNOTECarboneras1992540-9) []](#cite_note-FOOTNOTECarboneras1992540-9) Mallard landing in approach @@ -337,9 +337,9 @@ Male Mandarin duck -The overall [body plan](/wiki/Body_plan) of ducks is elongated and broad, and they are also relatively long-necked, albeit not as long-necked as the geese and swans. The body shape of diving ducks varies somewhat from this in being more rounded. The [bill](/wiki/Beak) is usually broad and contains serrated [pectens](/wiki/Pecten_(biology)) , which are particularly well defined in the filter-feeding species. In the case of some fishing species the bill is long and strongly serrated. The scaled legs are strong and well developed, and generally set far back on the body, more so in the highly aquatic species. The wings are very strong and are generally short and pointed, and the [flight](/wiki/Bird_flight) of ducks requires fast continuous strokes, requiring in turn strong wing muscles. Three species of [steamer duck](/wiki/Steamer_duck) are almost flightless, however. Many species of duck are temporarily flightless while [moulting](/wiki/Moult) ; they seek out protected habitat with good food supplies during this period. This moult typically precedes [migration](/wiki/Bird_migration) . +The overall [body plan](/wiki/Body_plan) of ducks is elongated and broad, and they are also relatively long-necked, albeit not as long-necked as the geese and swans. The body shape of diving ducks varies somewhat from this in being more rounded. The [bill](/wiki/Beak) is usually broad and contains serrated [pectens](/wiki/Pecten_(biology)), which are particularly well defined in the filter-feeding species. In the case of some fishing species the bill is long and strongly serrated. The scaled legs are strong and well developed, and generally set far back on the body, more so in the highly aquatic species. The wings are very strong and are generally short and pointed, and the [flight](/wiki/Bird_flight) of ducks requires fast continuous strokes, requiring in turn strong wing muscles. Three species of [steamer duck](/wiki/Steamer_duck) are almost flightless, however. Many species of duck are temporarily flightless while [moulting](/wiki/Moult); they seek out protected habitat with good food supplies during this period. This moult typically precedes [migration](/wiki/Bird_migration). -The drakes of northern species often have extravagant [plumage](/wiki/Plumage) , but that is [moulted](/wiki/Moult) in summer to give a more female-like appearance, the "eclipse" plumage. Southern resident species typically show less [sexual dimorphism](/wiki/Sexual_dimorphism) , although there are exceptions such as the [paradise shelduck](/wiki/Paradise_shelduck) of [New Zealand](/wiki/New_Zealand) , which is both strikingly sexually dimorphic and in which the female's plumage is brighter than that of the male. The plumage of juvenile birds generally resembles that of the female. Female ducks have evolved to have a corkscrew shaped vagina to prevent rape. +The drakes of northern species often have extravagant [plumage](/wiki/Plumage), but that is [moulted](/wiki/Moult) in summer to give a more female-like appearance, the "eclipse" plumage. Southern resident species typically show less [sexual dimorphism](/wiki/Sexual_dimorphism), although there are exceptions such as the [paradise shelduck](/wiki/Paradise_shelduck) of [New Zealand](/wiki/New_Zealand), which is both strikingly sexually dimorphic and in which the female's plumage is brighter than that of the male. The plumage of juvenile birds generally resembles that of the female. Female ducks have evolved to have a corkscrew shaped vagina to prevent rape. ## Distribution and habitat @@ -349,7 +349,7 @@ Flying steamer ducks in Ushuaia, Argentina -Ducks have a [cosmopolitan distribution](/wiki/Cosmopolitan_distribution) , and are found on every continent except Antarctica. [[](#cite_note-FOOTNOTECarboneras1992536-5) [5](#cite_note-FOOTNOTECarboneras1992536-5) []](#cite_note-FOOTNOTECarboneras1992536-5) Several species manage to live on subantarctic islands, including [South Georgia](/wiki/South_Georgia_and_the_South_Sandwich_Islands) and the [Auckland Islands](/wiki/Auckland_Islands) . [[](#cite_note-FOOTNOTEShirihai2008239,_245-20) [20](#cite_note-FOOTNOTEShirihai2008239,_245-20) []](#cite_note-FOOTNOTEShirihai2008239,_245-20) Ducks have reached a number of isolated oceanic islands, including the [Hawaiian Islands](/wiki/Hawaiian_Islands) , [Micronesia](/wiki/Micronesia) and the [Galápagos Islands](/wiki/Gal%C3%A1pagos_Islands) , where they are often [vagrants](/wiki/Glossary_of_bird_terms#vagrants) and less often [residents](/wiki/Glossary_of_bird_terms#residents) . [[](#cite_note-FOOTNOTEPrattBrunerBerrett198798–107-21) [21](#cite_note-FOOTNOTEPrattBrunerBerrett198798–107-21) []](#cite_note-FOOTNOTEPrattBrunerBerrett198798–107-21) [[](#cite_note-FOOTNOTEFitterFitterHosking200052–3-22) [22](#cite_note-FOOTNOTEFitterFitterHosking200052–3-22) []](#cite_note-FOOTNOTEFitterFitterHosking200052–3-22) A handful are [endemic](/wiki/Endemic) to such far-flung islands. [[](#cite_note-FOOTNOTEPrattBrunerBerrett198798–107-21) [21](#cite_note-FOOTNOTEPrattBrunerBerrett198798–107-21) []](#cite_note-FOOTNOTEPrattBrunerBerrett198798–107-21) +Ducks have a [cosmopolitan distribution](/wiki/Cosmopolitan_distribution), and are found on every continent except Antarctica. [[](#cite_note-FOOTNOTECarboneras1992536-5) [5](#cite_note-FOOTNOTECarboneras1992536-5) []](#cite_note-FOOTNOTECarboneras1992536-5) Several species manage to live on subantarctic islands, including [South Georgia](/wiki/South_Georgia_and_the_South_Sandwich_Islands) and the [Auckland Islands](/wiki/Auckland_Islands). [[](#cite_note-FOOTNOTEShirihai2008239,_245-20) [20](#cite_note-FOOTNOTEShirihai2008239,_245-20) []](#cite_note-FOOTNOTEShirihai2008239,_245-20) Ducks have reached a number of isolated oceanic islands, including the [Hawaiian Islands](/wiki/Hawaiian_Islands), [Micronesia](/wiki/Micronesia) and the [Galápagos Islands](/wiki/Gal%C3%A1pagos_Islands), where they are often [vagrants](/wiki/Glossary_of_bird_terms#vagrants) and less often [residents](/wiki/Glossary_of_bird_terms#residents). [[](#cite_note-FOOTNOTEPrattBrunerBerrett198798–107-21) [21](#cite_note-FOOTNOTEPrattBrunerBerrett198798–107-21) []](#cite_note-FOOTNOTEPrattBrunerBerrett198798–107-21) [[](#cite_note-FOOTNOTEFitterFitterHosking200052–3-22) [22](#cite_note-FOOTNOTEFitterFitterHosking200052–3-22) []](#cite_note-FOOTNOTEFitterFitterHosking200052–3-22) A handful are [endemic](/wiki/Endemic) to such far-flung islands. [[](#cite_note-FOOTNOTEPrattBrunerBerrett198798–107-21) [21](#cite_note-FOOTNOTEPrattBrunerBerrett198798–107-21) []](#cite_note-FOOTNOTEPrattBrunerBerrett198798–107-21) Female mallard in Cornwall, England @@ -369,15 +369,15 @@ Mallard duckling preening -Ducks eat food sources such as [grasses](/wiki/Poaceae) , aquatic plants, fish, insects, small amphibians, worms, and small [molluscs](/wiki/Mollusc) . +Ducks eat food sources such as [grasses](/wiki/Poaceae), aquatic plants, fish, insects, small amphibians, worms, and small [molluscs](/wiki/Mollusc). -[Dabbling ducks](/wiki/Dabbling_duck) feed on the surface of water or on land, or as deep as they can reach by up-ending without completely submerging. [[](#cite_note-24) [24](#cite_note-24) []](#cite_note-24) Along the edge of the bill, there is a comb-like structure called a [pecten](/wiki/Pecten_(biology)) . This strains the water squirting from the side of the bill and traps any food. The pecten is also used to preen feathers and to hold slippery food items. +[Dabbling ducks](/wiki/Dabbling_duck) feed on the surface of water or on land, or as deep as they can reach by up-ending without completely submerging. [[](#cite_note-24) [24](#cite_note-24) []](#cite_note-24) Along the edge of the bill, there is a comb-like structure called a [pecten](/wiki/Pecten_(biology)). This strains the water squirting from the side of the bill and traps any food. The pecten is also used to preen feathers and to hold slippery food items. [Diving ducks](/wiki/Diving_duck) and [sea ducks](/wiki/Sea_duck) forage deep underwater. To be able to submerge more easily, the diving ducks are heavier than dabbling ducks, and therefore have more difficulty taking off to fly. A few specialized species such as the [mergansers](/wiki/Merganser) are adapted to catch and swallow large fish. -The others have the characteristic wide flat bill adapted to [dredging](/wiki/Dredging) -type jobs such as pulling up waterweed, pulling worms and small molluscs out of mud, searching for insect larvae, and bulk jobs such as dredging out, holding, turning head first, and swallowing a squirming frog. To avoid injury when digging into sediment it has no [cere](/wiki/Cere) , but the nostrils come out through hard horn. +The others have the characteristic wide flat bill adapted to [dredging](/wiki/Dredging) -type jobs such as pulling up waterweed, pulling worms and small molluscs out of mud, searching for insect larvae, and bulk jobs such as dredging out, holding, turning head first, and swallowing a squirming frog. To avoid injury when digging into sediment it has no [cere](/wiki/Cere), but the nostrils come out through hard horn. [*The Guardian*](/wiki/The_Guardian) published an article advising that ducks should not be fed with bread because it [damages the health of the ducks](/wiki/Angel_wing) and pollutes waterways. [[](#cite_note-25) [25](#cite_note-25) []](#cite_note-25) @@ -387,13 +387,13 @@ A Muscovy duckling -Ducks generally [only have one partner at a time](/wiki/Monogamy_in_animals) , although the partnership usually only lasts one year. [[](#cite_note-26) [26](#cite_note-26) []](#cite_note-26) Larger species and the more sedentary species (like fast-river specialists) tend to have pair-bonds that last numerous years. [[](#cite_note-27) [27](#cite_note-27) []](#cite_note-27) Most duck species breed once a year, choosing to do so in favourable conditions ( [spring](/wiki/Spring_(season)) /summer or wet seasons). Ducks also tend to make a [nest](/wiki/Bird_nest) before breeding, and, after hatching, lead their ducklings to water. Mother ducks are very caring and protective of their young, but may abandon some of their ducklings if they are physically stuck in an area they cannot get out of (such as nesting in an enclosed [courtyard](/wiki/Courtyard) ) or are not prospering due to genetic defects or sickness brought about by hypothermia, starvation, or disease. Ducklings can also be orphaned by inconsistent late hatching where a few eggs hatch after the mother has abandoned the nest and led her ducklings to water. [[](#cite_note-28) [28](#cite_note-28) []](#cite_note-28) +Ducks generally [only have one partner at a time](/wiki/Monogamy_in_animals), although the partnership usually only lasts one year. [[](#cite_note-26) [26](#cite_note-26) []](#cite_note-26) Larger species and the more sedentary species (like fast-river specialists) tend to have pair-bonds that last numerous years. [[](#cite_note-27) [27](#cite_note-27) []](#cite_note-27) Most duck species breed once a year, choosing to do so in favourable conditions ([spring](/wiki/Spring_(season)) /summer or wet seasons). Ducks also tend to make a [nest](/wiki/Bird_nest) before breeding, and, after hatching, lead their ducklings to water. Mother ducks are very caring and protective of their young, but may abandon some of their ducklings if they are physically stuck in an area they cannot get out of (such as nesting in an enclosed [courtyard](/wiki/Courtyard)) or are not prospering due to genetic defects or sickness brought about by hypothermia, starvation, or disease. Ducklings can also be orphaned by inconsistent late hatching where a few eggs hatch after the mother has abandoned the nest and led her ducklings to water. [[](#cite_note-28) [28](#cite_note-28) []](#cite_note-28) ### Communication -Female [mallard](/wiki/Mallard) ducks (as well as several other species in the genus *Anas* , such as the [American](/wiki/American_black_duck) and [Pacific black ducks](/wiki/Pacific_black_duck) , [spot-billed duck](/wiki/Spot-billed_duck) , [northern pintail](/wiki/Northern_pintail) and [common teal](/wiki/Common_teal) ) make the classic "quack" sound while males make a similar but raspier sound that is sometimes written as "breeeeze", [[](#cite_note-29) [29](#cite_note-29) []](#cite_note-29) [ [*self-published source?*](/wiki/Wikipedia:Verifiability#Self-published_sources) ] but, despite widespread misconceptions, most species of duck do not "quack". [[](#cite_note-30) [30](#cite_note-30) []](#cite_note-30) In general, ducks make a range of [calls](/wiki/Bird_vocalisation) , including whistles, cooing, yodels and grunts. For example, the [scaup](/wiki/Scaup) - which are [diving ducks](/wiki/Diving_duck) - make a noise like "scaup" (hence their name). Calls may be loud displaying calls or quieter contact calls. +Female [mallard](/wiki/Mallard) ducks (as well as several other species in the genus *Anas*, such as the [American](/wiki/American_black_duck) and [Pacific black ducks](/wiki/Pacific_black_duck), [spot-billed duck](/wiki/Spot-billed_duck), [northern pintail](/wiki/Northern_pintail) and [common teal](/wiki/Common_teal)) make the classic "quack" sound while males make a similar but raspier sound that is sometimes written as "breeeeze", [[](#cite_note-29) [29](#cite_note-29) []](#cite_note-29) [[*self-published source?*](/wiki/Wikipedia:Verifiability#Self-published_sources)] but, despite widespread misconceptions, most species of duck do not "quack". [[](#cite_note-30) [30](#cite_note-30) []](#cite_note-30) In general, ducks make a range of [calls](/wiki/Bird_vocalisation), including whistles, cooing, yodels and grunts. For example, the [scaup](/wiki/Scaup) - which are [diving ducks](/wiki/Diving_duck) - make a noise like "scaup" (hence their name). Calls may be loud displaying calls or quieter contact calls. -A common [urban legend](/wiki/Urban_legend) claims that duck quacks do not echo; however, this has been proven to be false. This myth was first debunked by the Acoustics Research Centre at the [University of Salford](/wiki/University_of_Salford) in 2003 as part of the [British Association](/wiki/British_Association) 's Festival of Science. [[](#cite_note-31) [31](#cite_note-31) []](#cite_note-31) It was also debunked in [one of the earlier episodes](/wiki/MythBusters_(2003_season)#Does_a_Duck's_Quack_Echo?) of the popular Discovery Channel television show [*MythBusters*](/wiki/MythBusters) . [[](#cite_note-32) [32](#cite_note-32) []](#cite_note-32) +A common [urban legend](/wiki/Urban_legend) claims that duck quacks do not echo; however, this has been proven to be false. This myth was first debunked by the Acoustics Research Centre at the [University of Salford](/wiki/University_of_Salford) in 2003 as part of the [British Association](/wiki/British_Association) 's Festival of Science. [[](#cite_note-31) [31](#cite_note-31) []](#cite_note-31) It was also debunked in [one of the earlier episodes](/wiki/MythBusters_(2003_season)#Does_a_Duck's_Quack_Echo?) of the popular Discovery Channel television show [*MythBusters*](/wiki/MythBusters). [[](#cite_note-32) [32](#cite_note-32) []](#cite_note-32) ### Predators @@ -401,9 +401,9 @@ Ringed teal -Ducks have many predators. Ducklings are particularly vulnerable, since their inability to fly makes them easy prey not only for predatory birds but also for large fish like [pike](/wiki/Esox) , [crocodilians](/wiki/Crocodilia) , predatory [testudines](/wiki/Testudines) such as the [alligator snapping turtle](/wiki/Alligator_snapping_turtle) , and other aquatic hunters, including fish-eating birds such as [herons](/wiki/Heron) . Ducks' nests are raided by land-based predators, and brooding females may be caught unaware on the nest by mammals, such as [foxes](/wiki/Fox) , or large birds, such as [hawks](/wiki/Hawk) or [owls](/wiki/Owl) . +Ducks have many predators. Ducklings are particularly vulnerable, since their inability to fly makes them easy prey not only for predatory birds but also for large fish like [pike](/wiki/Esox), [crocodilians](/wiki/Crocodilia), predatory [testudines](/wiki/Testudines) such as the [alligator snapping turtle](/wiki/Alligator_snapping_turtle), and other aquatic hunters, including fish-eating birds such as [herons](/wiki/Heron). Ducks' nests are raided by land-based predators, and brooding females may be caught unaware on the nest by mammals, such as [foxes](/wiki/Fox), or large birds, such as [hawks](/wiki/Hawk) or [owls](/wiki/Owl). -Adult ducks are fast fliers, but may be caught on the water by large aquatic predators including big fish such as the North American [muskie](/wiki/Muskellunge) and the European [pike](/wiki/Esox) . In flight, ducks are safe from all but a few predators such as humans and the [peregrine falcon](/wiki/Peregrine_falcon) , which uses its speed and strength to catch ducks. +Adult ducks are fast fliers, but may be caught on the water by large aquatic predators including big fish such as the North American [muskie](/wiki/Muskellunge) and the European [pike](/wiki/Esox). In flight, ducks are safe from all but a few predators such as humans and the [peregrine falcon](/wiki/Peregrine_falcon), which uses its speed and strength to catch ducks. ## Relationship with humans @@ -411,9 +411,9 @@ Adult ducks are fast fliers, but may be caught on the water by large aquatic pre Main article: [Waterfowl hunting](/wiki/Waterfowl_hunting) -Humans have hunted ducks since prehistoric times. Excavations of [middens](/wiki/Midden) in California dating to 7800 - 6400 [BP](/wiki/Before_present) have turned up bones of ducks, including at least one now-extinct flightless species. [[](#cite_note-FOOTNOTEErlandson1994171-33) [33](#cite_note-FOOTNOTEErlandson1994171-33) []](#cite_note-FOOTNOTEErlandson1994171-33) Ducks were captured in "significant numbers" by [Holocene](/wiki/Holocene) inhabitants of the lower [Ohio River](/wiki/Ohio_River) valley, suggesting they took advantage of the seasonal bounty provided by migrating waterfowl. [[](#cite_note-FOOTNOTEJeffries2008168,_243-34) [34](#cite_note-FOOTNOTEJeffries2008168,_243-34) []](#cite_note-FOOTNOTEJeffries2008168,_243-34) Neolithic hunters in locations as far apart as the Caribbean, [[](#cite_note-FOOTNOTESued-Badillo200365-35) [35](#cite_note-FOOTNOTESued-Badillo200365-35) []](#cite_note-FOOTNOTESued-Badillo200365-35) Scandinavia, [[](#cite_note-FOOTNOTEThorpe199668-36) [36](#cite_note-FOOTNOTEThorpe199668-36) []](#cite_note-FOOTNOTEThorpe199668-36) Egypt, [[](#cite_note-FOOTNOTEMaisels199942-37) [37](#cite_note-FOOTNOTEMaisels199942-37) []](#cite_note-FOOTNOTEMaisels199942-37) Switzerland, [[](#cite_note-FOOTNOTERau1876133-38) [38](#cite_note-FOOTNOTERau1876133-38) []](#cite_note-FOOTNOTERau1876133-38) and China relied on ducks as a source of protein for some or all of the year. [[](#cite_note-FOOTNOTEHigman201223-39) [39](#cite_note-FOOTNOTEHigman201223-39) []](#cite_note-FOOTNOTEHigman201223-39) Archeological evidence shows that [Māori people](/wiki/M%C4%81ori_people) in New Zealand hunted the flightless [Finsch's duck](/wiki/Finsch%27s_duck) , possibly to extinction, though rat predation may also have contributed to its fate. [[](#cite_note-FOOTNOTEHume201253-40) [40](#cite_note-FOOTNOTEHume201253-40) []](#cite_note-FOOTNOTEHume201253-40) A similar end awaited the [Chatham duck](/wiki/Chatham_duck) , a species with reduced flying capabilities which went extinct shortly after its island was colonised by Polynesian settlers. [[](#cite_note-FOOTNOTEHume201252-41) [41](#cite_note-FOOTNOTEHume201252-41) []](#cite_note-FOOTNOTEHume201252-41) It is probable that duck eggs were gathered by Neolithic hunter-gathers as well, though hard evidence of this is uncommon. [[](#cite_note-FOOTNOTESued-Badillo200365-35) [35](#cite_note-FOOTNOTESued-Badillo200365-35) []](#cite_note-FOOTNOTESued-Badillo200365-35) [[](#cite_note-FOOTNOTEFieldhouse2002167-42) [42](#cite_note-FOOTNOTEFieldhouse2002167-42) []](#cite_note-FOOTNOTEFieldhouse2002167-42) +Humans have hunted ducks since prehistoric times. Excavations of [middens](/wiki/Midden) in California dating to 7800 - 6400 [BP](/wiki/Before_present) have turned up bones of ducks, including at least one now-extinct flightless species. [[](#cite_note-FOOTNOTEErlandson1994171-33) [33](#cite_note-FOOTNOTEErlandson1994171-33) []](#cite_note-FOOTNOTEErlandson1994171-33) Ducks were captured in "significant numbers" by [Holocene](/wiki/Holocene) inhabitants of the lower [Ohio River](/wiki/Ohio_River) valley, suggesting they took advantage of the seasonal bounty provided by migrating waterfowl. [[](#cite_note-FOOTNOTEJeffries2008168,_243-34) [34](#cite_note-FOOTNOTEJeffries2008168,_243-34) []](#cite_note-FOOTNOTEJeffries2008168,_243-34) Neolithic hunters in locations as far apart as the Caribbean, [[](#cite_note-FOOTNOTESued-Badillo200365-35) [35](#cite_note-FOOTNOTESued-Badillo200365-35) []](#cite_note-FOOTNOTESued-Badillo200365-35) Scandinavia, [[](#cite_note-FOOTNOTEThorpe199668-36) [36](#cite_note-FOOTNOTEThorpe199668-36) []](#cite_note-FOOTNOTEThorpe199668-36) Egypt, [[](#cite_note-FOOTNOTEMaisels199942-37) [37](#cite_note-FOOTNOTEMaisels199942-37) []](#cite_note-FOOTNOTEMaisels199942-37) Switzerland, [[](#cite_note-FOOTNOTERau1876133-38) [38](#cite_note-FOOTNOTERau1876133-38) []](#cite_note-FOOTNOTERau1876133-38) and China relied on ducks as a source of protein for some or all of the year. [[](#cite_note-FOOTNOTEHigman201223-39) [39](#cite_note-FOOTNOTEHigman201223-39) []](#cite_note-FOOTNOTEHigman201223-39) Archeological evidence shows that [Māori people](/wiki/M%C4%81ori_people) in New Zealand hunted the flightless [Finsch's duck](/wiki/Finsch%27s_duck), possibly to extinction, though rat predation may also have contributed to its fate. [[](#cite_note-FOOTNOTEHume201253-40) [40](#cite_note-FOOTNOTEHume201253-40) []](#cite_note-FOOTNOTEHume201253-40) A similar end awaited the [Chatham duck](/wiki/Chatham_duck), a species with reduced flying capabilities which went extinct shortly after its island was colonised by Polynesian settlers. [[](#cite_note-FOOTNOTEHume201252-41) [41](#cite_note-FOOTNOTEHume201252-41) []](#cite_note-FOOTNOTEHume201252-41) It is probable that duck eggs were gathered by Neolithic hunter-gathers as well, though hard evidence of this is uncommon. [[](#cite_note-FOOTNOTESued-Badillo200365-35) [35](#cite_note-FOOTNOTESued-Badillo200365-35) []](#cite_note-FOOTNOTESued-Badillo200365-35) [[](#cite_note-FOOTNOTEFieldhouse2002167-42) [42](#cite_note-FOOTNOTEFieldhouse2002167-42) []](#cite_note-FOOTNOTEFieldhouse2002167-42) -In many areas, wild ducks (including ducks farmed and released into the wild) are hunted for food or sport, [[](#cite_note-43) [43](#cite_note-43) []](#cite_note-43) by shooting, or by being trapped using [duck decoys](/wiki/Duck_decoy_(structure)) . Because an idle floating duck or a duck squatting on land cannot react to fly or move quickly, "a sitting duck" has come to mean "an easy target". These ducks may be [contaminated by pollutants](/wiki/Duck_(food)#Pollution) such as [PCBs](/wiki/Polychlorinated_biphenyl) . [[](#cite_note-44) [44](#cite_note-44) []](#cite_note-44) +In many areas, wild ducks (including ducks farmed and released into the wild) are hunted for food or sport, [[](#cite_note-43) [43](#cite_note-43) []](#cite_note-43) by shooting, or by being trapped using [duck decoys](/wiki/Duck_decoy_(structure)). Because an idle floating duck or a duck squatting on land cannot react to fly or move quickly, "a sitting duck" has come to mean "an easy target". These ducks may be [contaminated by pollutants](/wiki/Duck_(food)#Pollution) such as [PCBs](/wiki/Polychlorinated_biphenyl). [[](#cite_note-44) [44](#cite_note-44) []](#cite_note-44) ### Domestication @@ -423,7 +423,7 @@ Indian Runner ducks, a common breed of domestic ducks -Ducks have many economic uses, being farmed for their meat, eggs, and feathers (particularly their [down](/wiki/Down_feather) ). Approximately 3 billion ducks are slaughtered each year for meat worldwide. [[](#cite_note-45) [45](#cite_note-45) []](#cite_note-45) They are also kept and bred by aviculturists and often displayed in zoos. Almost all the varieties of domestic ducks are descended from the [mallard](/wiki/Mallard) ( *Anas platyrhynchos* ), apart from the [Muscovy duck](/wiki/Muscovy_duck) ( *Cairina moschata* ). [[](#cite_note-46) [46](#cite_note-46) []](#cite_note-46) [[](#cite_note-47) [47](#cite_note-47) []](#cite_note-47) The [Call duck](/wiki/Call_duck) is another example of a domestic duck breed. Its name comes from its original use established by hunters, as a decoy to attract wild mallards from the sky, into traps set for them on the ground. The call duck is the world's smallest domestic duck breed, as it weighs less than 1 kg (2.2 lb). [[](#cite_note-48) [48](#cite_note-48) []](#cite_note-48) +Ducks have many economic uses, being farmed for their meat, eggs, and feathers (particularly their [down](/wiki/Down_feather)). Approximately 3 billion ducks are slaughtered each year for meat worldwide. [[](#cite_note-45) [45](#cite_note-45) []](#cite_note-45) They are also kept and bred by aviculturists and often displayed in zoos. Almost all the varieties of domestic ducks are descended from the [mallard](/wiki/Mallard) (*Anas platyrhynchos*), apart from the [Muscovy duck](/wiki/Muscovy_duck) (*Cairina moschata*). [[](#cite_note-46) [46](#cite_note-46) []](#cite_note-46) [[](#cite_note-47) [47](#cite_note-47) []](#cite_note-47) The [Call duck](/wiki/Call_duck) is another example of a domestic duck breed. Its name comes from its original use established by hunters, as a decoy to attract wild mallards from the sky, into traps set for them on the ground. The call duck is the world's smallest domestic duck breed, as it weighs less than 1 kg (2.2 lb). [[](#cite_note-48) [48](#cite_note-48) []](#cite_note-48) ### Heraldry @@ -431,13 +431,13 @@ Three black-colored ducks in the coat of arms of Maaninka[49] -Ducks appear on several [coats of arms](/wiki/Coats_of_arms) , including the coat of arms of [Lubāna](/wiki/Lub%C4%81na) ( [Latvia](/wiki/Latvia) ) [[](#cite_note-50) [50](#cite_note-50) []](#cite_note-50) and the coat of arms of [Föglö](/wiki/F%C3%B6gl%C3%B6) ( [Åland](/wiki/%C3%85land) ). [[](#cite_note-51) [51](#cite_note-51) []](#cite_note-51) +Ducks appear on several [coats of arms](/wiki/Coats_of_arms), including the coat of arms of [Lubāna](/wiki/Lub%C4%81na) ([Latvia](/wiki/Latvia)) [[](#cite_note-50) [50](#cite_note-50) []](#cite_note-50) and the coat of arms of [Föglö](/wiki/F%C3%B6gl%C3%B6) ([Åland](/wiki/%C3%85land)). [[](#cite_note-51) [51](#cite_note-51) []](#cite_note-51) ### Cultural references -In 2002, psychologist [Richard Wiseman](/wiki/Richard_Wiseman) and colleagues at the [University of Hertfordshire](/wiki/University_of_Hertfordshire) , [UK](/wiki/UK) , finished a year-long [LaughLab](/wiki/LaughLab) experiment, concluding that of all animals, ducks attract the most humor and silliness; he said, "If you're going to tell a joke involving an animal, make it a duck." [[](#cite_note-52) [52](#cite_note-52) []](#cite_note-52) The word "duck" may have become an [inherently funny word](/wiki/Inherently_funny_word) in many languages, possibly because ducks are seen as silly in their looks or behavior. Of the many [ducks in fiction](/wiki/List_of_fictional_ducks) , many are cartoon characters, such as [Walt Disney](/wiki/The_Walt_Disney_Company) 's [Donald Duck](/wiki/Donald_Duck) , and [Warner Bros.](/wiki/Warner_Bros.) ' [Daffy Duck](/wiki/Daffy_Duck) . [Howard the Duck](/wiki/Howard_the_Duck) started as a comic book character in 1973 [[](#cite_note-53) [53](#cite_note-53) []](#cite_note-53) [[](#cite_note-54) [54](#cite_note-54) []](#cite_note-54) and was made into a [movie](/wiki/Howard_the_Duck_(film)) in 1986. +In 2002, psychologist [Richard Wiseman](/wiki/Richard_Wiseman) and colleagues at the [University of Hertfordshire](/wiki/University_of_Hertfordshire), [UK](/wiki/UK), finished a year-long [LaughLab](/wiki/LaughLab) experiment, concluding that of all animals, ducks attract the most humor and silliness; he said, "If you're going to tell a joke involving an animal, make it a duck." [[](#cite_note-52) [52](#cite_note-52) []](#cite_note-52) The word "duck" may have become an [inherently funny word](/wiki/Inherently_funny_word) in many languages, possibly because ducks are seen as silly in their looks or behavior. Of the many [ducks in fiction](/wiki/List_of_fictional_ducks), many are cartoon characters, such as [Walt Disney](/wiki/The_Walt_Disney_Company) 's [Donald Duck](/wiki/Donald_Duck), and [Warner Bros.](/wiki/Warner_Bros.) ' [Daffy Duck](/wiki/Daffy_Duck). [Howard the Duck](/wiki/Howard_the_Duck) started as a comic book character in 1973 [[](#cite_note-53) [53](#cite_note-53) []](#cite_note-53) [[](#cite_note-54) [54](#cite_note-54) []](#cite_note-54) and was made into a [movie](/wiki/Howard_the_Duck_(film)) in 1986. -The 1992 Disney film [*The Mighty Ducks*](/wiki/The_Mighty_Ducks_(film)) , starring [Emilio Estevez](/wiki/Emilio_Estevez) , chose the duck as the mascot for the fictional youth hockey team who are protagonists of the movie, based on the duck being described as a fierce fighter. This led to the duck becoming the nickname and mascot for the eventual [National Hockey League](/wiki/National_Hockey_League) professional team of the [Anaheim Ducks](/wiki/Anaheim_Ducks) , who were founded with the name the Mighty Ducks of Anaheim. [ [*citation needed*](/wiki/Wikipedia:Citation_needed) ] The duck is also the nickname of the [University of Oregon](/wiki/University_of_Oregon) sports teams as well as the [Long Island Ducks](/wiki/Long_Island_Ducks) minor league [baseball](/wiki/Baseball) team. [[](#cite_note-55) [55](#cite_note-55) []](#cite_note-55) +The 1992 Disney film [*The Mighty Ducks*](/wiki/The_Mighty_Ducks_(film)), starring [Emilio Estevez](/wiki/Emilio_Estevez), chose the duck as the mascot for the fictional youth hockey team who are protagonists of the movie, based on the duck being described as a fierce fighter. This led to the duck becoming the nickname and mascot for the eventual [National Hockey League](/wiki/National_Hockey_League) professional team of the [Anaheim Ducks](/wiki/Anaheim_Ducks), who were founded with the name the Mighty Ducks of Anaheim. [[*citation needed*](/wiki/Wikipedia:Citation_needed)] The duck is also the nickname of the [University of Oregon](/wiki/University_of_Oregon) sports teams as well as the [Long Island Ducks](/wiki/Long_Island_Ducks) minor league [baseball](/wiki/Baseball) team. [[](#cite_note-55) [55](#cite_note-55) []](#cite_note-55) ## See also @@ -453,84 +453,84 @@ The 1992 Disney film [*The Mighty Ducks*](/wiki/The_Mighty_Ducks_(film)) , starr ### Citations -1. [**^**](#cite_ref-1) ["Duckling"](http://dictionary.reference.com/browse/duckling) . *The American Heritage Dictionary of the English Language, Fourth Edition* . Houghton Mifflin Company. 2006 . Retrieved 2015-05-22 . -2. [**^**](#cite_ref-2) ["Duckling"](http://dictionary.reference.com/browse/duckling) . *Kernerman English Multilingual Dictionary (Beta Version)* . K. Dictionaries Ltd. 2000-2006 . Retrieved 2015-05-22 . -3. [**^**](#cite_ref-3) Dohner, Janet Vorwald (2001). [*The Encyclopedia of Historic and Endangered Livestock and Poultry Breeds*](https://books.google.com/books?id=WJCTL_mC5w4C&q=male+duck+is+called+a+drake+and+the+female+is+called+a+duck&pg=PA457) . Yale University Press. [ISBN](/wiki/ISBN_(identifier)) [978-0300138139](/wiki/Special:BookSources/978-0300138139) . -4. [**^**](#cite_ref-4) Visca, Curt; Visca, Kelley (2003). [*How to Draw Cartoon Birds*](https://books.google.com/books?id=VqSquCLNrZcC&q=male+duck+is+called+a+drake+and+the+female+is+called+a+duck+%28or+hen%29&pg=PA16) . The Rosen Publishing Group. [ISBN](/wiki/ISBN_(identifier)) [9780823961566](/wiki/Special:BookSources/9780823961566) . -5. ^ [***a***](#cite_ref-FOOTNOTECarboneras1992536_5-0) [***b***](#cite_ref-FOOTNOTECarboneras1992536_5-1) [***c***](#cite_ref-FOOTNOTECarboneras1992536_5-2) [***d***](#cite_ref-FOOTNOTECarboneras1992536_5-3) [Carboneras 1992](#CITEREFCarboneras1992) , p. 536. -6. [**^**](#cite_ref-FOOTNOTELivezey1986737–738_6-0) [Livezey 1986](#CITEREFLivezey1986) , pp. 737-738. -7. [**^**](#cite_ref-FOOTNOTEMadsenMcHughde_Kloet1988452_7-0) [Madsen, McHugh & de Kloet 1988](#CITEREFMadsenMcHughde_Kloet1988) , p. 452. -8. [**^**](#cite_ref-FOOTNOTEDonne-GousséLaudetHänni2002353–354_8-0) [Donne-Goussé, Laudet & Hänni 2002](#CITEREFDonne-GousséLaudetHänni2002) , pp. 353-354. -9. ^ [***a***](#cite_ref-FOOTNOTECarboneras1992540_9-0) [***b***](#cite_ref-FOOTNOTECarboneras1992540_9-1) [***c***](#cite_ref-FOOTNOTECarboneras1992540_9-2) [***d***](#cite_ref-FOOTNOTECarboneras1992540_9-3) [***e***](#cite_ref-FOOTNOTECarboneras1992540_9-4) [***f***](#cite_ref-FOOTNOTECarboneras1992540_9-5) [Carboneras 1992](#CITEREFCarboneras1992) , p. 540. -10. [**^**](#cite_ref-FOOTNOTEElphickDunningSibley2001191_10-0) [Elphick, Dunning & Sibley 2001](#CITEREFElphickDunningSibley2001) , p. 191. -11. [**^**](#cite_ref-FOOTNOTEKear2005448_11-0) [Kear 2005](#CITEREFKear2005) , p. 448. -12. [**^**](#cite_ref-FOOTNOTEKear2005622–623_12-0) [Kear 2005](#CITEREFKear2005) , p. 622-623. -13. [**^**](#cite_ref-FOOTNOTEKear2005686_13-0) [Kear 2005](#CITEREFKear2005) , p. 686. -14. [**^**](#cite_ref-FOOTNOTEElphickDunningSibley2001193_14-0) [Elphick, Dunning & Sibley 2001](#CITEREFElphickDunningSibley2001) , p. 193. -15. ^ [***a***](#cite_ref-FOOTNOTECarboneras1992537_15-0) [***b***](#cite_ref-FOOTNOTECarboneras1992537_15-1) [***c***](#cite_ref-FOOTNOTECarboneras1992537_15-2) [***d***](#cite_ref-FOOTNOTECarboneras1992537_15-3) [***e***](#cite_ref-FOOTNOTECarboneras1992537_15-4) [***f***](#cite_ref-FOOTNOTECarboneras1992537_15-5) [***g***](#cite_ref-FOOTNOTECarboneras1992537_15-6) [Carboneras 1992](#CITEREFCarboneras1992) , p. 537. -16. [**^**](#cite_ref-FOOTNOTEAmerican_Ornithologists'_Union1998xix_16-0) [American Ornithologists' Union 1998](#CITEREFAmerican_Ornithologists'_Union1998) , p. xix. -17. [**^**](#cite_ref-FOOTNOTEAmerican_Ornithologists'_Union1998_17-0) [American Ornithologists' Union 1998](#CITEREFAmerican_Ornithologists'_Union1998) . -18. [**^**](#cite_ref-FOOTNOTECarboneras1992538_18-0) [Carboneras 1992](#CITEREFCarboneras1992) , p. 538. -19. [**^**](#cite_ref-FOOTNOTEChristidisBoles200862_19-0) [Christidis & Boles 2008](#CITEREFChristidisBoles2008) , p. 62. -20. [**^**](#cite_ref-FOOTNOTEShirihai2008239,_245_20-0) [Shirihai 2008](#CITEREFShirihai2008) , pp. 239, 245. -21. ^ [***a***](#cite_ref-FOOTNOTEPrattBrunerBerrett198798–107_21-0) [***b***](#cite_ref-FOOTNOTEPrattBrunerBerrett198798–107_21-1) [Pratt, Bruner & Berrett 1987](#CITEREFPrattBrunerBerrett1987) , pp. 98-107. -22. [**^**](#cite_ref-FOOTNOTEFitterFitterHosking200052–3_22-0) [Fitter, Fitter & Hosking 2000](#CITEREFFitterFitterHosking2000) , pp. 52-3. -23. [**^**](#cite_ref-23) ["Pacific Black Duck"](http://www.wiresnr.org/pacificblackduck.html) . *www.wiresnr.org* . Retrieved 2018-04-27 . -24. [**^**](#cite_ref-24) Ogden, Evans. ["Dabbling Ducks"](https://www.sfu.ca/biology/wildberg/species/dabbducks.html) . CWE . Retrieved 2006-11-02 . -25. [**^**](#cite_ref-25) Karl Mathiesen (16 March 2015). ["Don't feed the ducks bread, say conservationists"](https://www.theguardian.com/environment/2015/mar/16/dont-feed-the-ducks-bread-say-conservationists) . *The Guardian* . Retrieved 13 November 2016 . -26. [**^**](#cite_ref-26) Rohwer, Frank C.; Anderson, Michael G. (1988). "Female-Biased Philopatry, Monogamy, and the Timing of Pair Formation in Migratory Waterfowl". *Current Ornithology* . pp. 187-221. [doi](/wiki/Doi_(identifier)) : [10.1007/978-1-4615-6787-5_4](https://doi.org/10.1007%2F978-1-4615-6787-5_4) . [ISBN](/wiki/ISBN_(identifier)) [978-1-4615-6789-9](/wiki/Special:BookSources/978-1-4615-6789-9) . -27. [**^**](#cite_ref-27) Smith, Cyndi M.; Cooke, Fred; Robertson, Gregory J.; Goudie, R. Ian; Boyd, W. Sean (2000). ["Long-Term Pair Bonds in Harlequin Ducks"](https://doi.org/10.1093%2Fcondor%2F102.1.201) . *The Condor* . **102** (1): 201-205. [doi](/wiki/Doi_(identifier)) : [10.1093/condor/102.1.201](https://doi.org/10.1093%2Fcondor%2F102.1.201) . [hdl](/wiki/Hdl_(identifier)) : [10315/13797](https://hdl.handle.net/10315%2F13797) . -28. [**^**](#cite_ref-28) ["If You Find An Orphaned Duckling - Wildlife Rehabber"](https://web.archive.org/web/20180923152911/http://wildliferehabber.com/content/if-you-find-duckling) . *wildliferehabber.com* . Archived from [the original](https://wildliferehabber.com/content/if-you-find-duckling) on 2018-09-23 . Retrieved 2018-12-22 . -29. [**^**](#cite_ref-29) Carver, Heather (2011). [*The Duck Bible*](https://books.google.com/books?id=VGofAwAAQBAJ&q=mallard+sound+deep+and+raspy&pg=PA39) . Lulu.com. [ISBN](/wiki/ISBN_(identifier)) [9780557901562](/wiki/Special:BookSources/9780557901562) . [ [*self-published source*](/wiki/Wikipedia:Verifiability#Self-published_sources) ] -30. [**^**](#cite_ref-30) Titlow, Budd (2013-09-03). [*Bird Brains: Inside the Strange Minds of Our Fine Feathered Friends*](https://books.google.com/books?id=fXJBBAAAQBAJ&q=Females+of+most+dabbling+ducks+make+the+classic+%22quack%22+sound+but+most+ducks+don%27t+quack&pg=PA123) . Rowman & Littlefield. [ISBN](/wiki/ISBN_(identifier)) [9780762797707](/wiki/Special:BookSources/9780762797707) . -31. [**^**](#cite_ref-31) Amos, Jonathan (2003-09-08). ["Sound science is quackers"](http://news.bbc.co.uk/2/hi/science/nature/3086890.stm) . *BBC News* . Retrieved 2006-11-02 . -32. [**^**](#cite_ref-32) ["Mythbusters Episode 8"](http://mythbustersresults.com/episode8) . 12 December 2003. -33. [**^**](#cite_ref-FOOTNOTEErlandson1994171_33-0) [Erlandson 1994](#CITEREFErlandson1994) , p. 171. -34. [**^**](#cite_ref-FOOTNOTEJeffries2008168,_243_34-0) [Jeffries 2008](#CITEREFJeffries2008) , pp. 168, 243. -35. ^ [***a***](#cite_ref-FOOTNOTESued-Badillo200365_35-0) [***b***](#cite_ref-FOOTNOTESued-Badillo200365_35-1) [Sued-Badillo 2003](#CITEREFSued-Badillo2003) , p. 65. -36. [**^**](#cite_ref-FOOTNOTEThorpe199668_36-0) [Thorpe 1996](#CITEREFThorpe1996) , p. 68. -37. [**^**](#cite_ref-FOOTNOTEMaisels199942_37-0) [Maisels 1999](#CITEREFMaisels1999) , p. 42. -38. [**^**](#cite_ref-FOOTNOTERau1876133_38-0) [Rau 1876](#CITEREFRau1876) , p. 133. -39. [**^**](#cite_ref-FOOTNOTEHigman201223_39-0) [Higman 2012](#CITEREFHigman2012) , p. 23. -40. [**^**](#cite_ref-FOOTNOTEHume201253_40-0) [Hume 2012](#CITEREFHume2012) , p. 53. -41. [**^**](#cite_ref-FOOTNOTEHume201252_41-0) [Hume 2012](#CITEREFHume2012) , p. 52. -42. [**^**](#cite_ref-FOOTNOTEFieldhouse2002167_42-0) [Fieldhouse 2002](#CITEREFFieldhouse2002) , p. 167. -43. [**^**](#cite_ref-43) Livingston, A. D. (1998-01-01). [*Guide to Edible Plants and Animals*](https://books.google.com/books?id=NViSMffyaSgC&q=%C2%A0%C2%A0In+many+areas,+wild+ducks+of+various+species+are+hunted+for+food+or+sport) . Wordsworth Editions, Limited. [ISBN](/wiki/ISBN_(identifier)) [9781853263774](/wiki/Special:BookSources/9781853263774) . -44. [**^**](#cite_ref-44) ["Study plan for waterfowl injury assessment: Determining PCB concentrations in Hudson river resident waterfowl"](https://www.dec.ny.gov/docs/fish_marine_pdf/wfp09a.pdf) (PDF) . *New York State Department of Environmental Conservation* . US Department of Commerce. December 2008. p. 3. [Archived](https://ghostarchive.org/archive/20221009/https://www.dec.ny.gov/docs/fish_marine_pdf/wfp09a.pdf) (PDF) from the original on 2022-10-09 . Retrieved 2 July 2019 . -45. [**^**](#cite_ref-45) ["FAOSTAT"](http://www.fao.org/faostat/en/#data/QL) . *www.fao.org* . Retrieved 2019-10-25 . -46. [**^**](#cite_ref-46) ["Anas platyrhynchos, Domestic Duck; DigiMorph Staff - The University of Texas at Austin"](http://digimorph.org/specimens/anas_platyrhynchos/skull/) . Digimorph.org . Retrieved 2012-12-23 . -47. [**^**](#cite_ref-47) Sy Montgomery. ["Mallard; Encyclopædia Britannica"](https://www.britannica.com/eb/topic-360302/mallard) . *Britannica.com* . Retrieved 2012-12-23 . -48. [**^**](#cite_ref-48) Glenday, Craig (2014). [*Guinness World Records*](https://archive.org/details/guinnessworldrec0000unse_r3e7/page/135) . Guinness World Records Limited. pp. [135](https://archive.org/details/guinnessworldrec0000unse_r3e7/page/135) . [ISBN](/wiki/ISBN_(identifier)) [978-1-908843-15-9](/wiki/Special:BookSources/978-1-908843-15-9) . -49. [**^**](#cite_ref-49) *Suomen kunnallisvaakunat* (in Finnish). Suomen Kunnallisliitto. 1982. p. 147. [ISBN](/wiki/ISBN_(identifier)) [951-773-085-3](/wiki/Special:BookSources/951-773-085-3) . -50. [**^**](#cite_ref-50) ["Lubānas simbolika"](http://www.lubana.lv/index.php/lv/homepage/lubanas-pilseta-2) (in Latvian) . Retrieved September 9, 2021 . -51. [**^**](#cite_ref-51) ["Föglö"](http://digi.narc.fi/digi/view.ka?kuid=1738595) (in Swedish) . Retrieved September 9, 2021 . -52. [**^**](#cite_ref-52) Young, Emma. ["World's funniest joke revealed"](https://www.newscientist.com/article/dn2876-worlds-funniest-joke-revealed/) . *New Scientist* . Retrieved 7 January 2019 . -53. [**^**](#cite_ref-53) ["Howard the Duck (character)"](http://www.comics.org/character/name/Howard%20the%20Duck/sort/chrono/) . [*Grand Comics Database*](/wiki/Grand_Comics_Database) . -54. [**^**](#cite_ref-54) [Sanderson, Peter](/wiki/Peter_Sanderson) ; Gilbert, Laura (2008). "1970s". *Marvel Chronicle A Year by Year History* . London, United Kingdom: [Dorling Kindersley](/wiki/Dorling_Kindersley) . p. 161. [ISBN](/wiki/ISBN_(identifier)) [978-0756641238](/wiki/Special:BookSources/978-0756641238) . December saw the debut of the cigar-smoking Howard the Duck. In this story by writer Steve Gerber and artist Val Mayerik, various beings from different realities had begun turning up in the Man-Thing's Florida swamp, including this bad-tempered talking duck. -55. [**^**](#cite_ref-55) ["The Duck"](https://goducks.com/sports/2003/8/28/153778.aspx) . *University of Oregon Athletics* . Retrieved 2022-01-20 . +1. [**^**](#cite_ref-1) ["Duckling"](http://dictionary.reference.com/browse/duckling). *The American Heritage Dictionary of the English Language, Fourth Edition*. Houghton Mifflin Company. 2006. Retrieved 2015-05-22. +2. [**^**](#cite_ref-2) ["Duckling"](http://dictionary.reference.com/browse/duckling). *Kernerman English Multilingual Dictionary (Beta Version)*. K. Dictionaries Ltd. 2000-2006. Retrieved 2015-05-22. +3. [**^**](#cite_ref-3) Dohner, Janet Vorwald (2001). [*The Encyclopedia of Historic and Endangered Livestock and Poultry Breeds*](https://books.google.com/books?id=WJCTL_mC5w4C&q=male+duck+is+called+a+drake+and+the+female+is+called+a+duck&pg=PA457). Yale University Press. [ISBN](/wiki/ISBN_(identifier)) [978-0300138139](/wiki/Special:BookSources/978-0300138139). +4. [**^**](#cite_ref-4) Visca, Curt; Visca, Kelley (2003). [*How to Draw Cartoon Birds*](https://books.google.com/books?id=VqSquCLNrZcC&q=male+duck+is+called+a+drake+and+the+female+is+called+a+duck+%28or+hen%29&pg=PA16). The Rosen Publishing Group. [ISBN](/wiki/ISBN_(identifier)) [9780823961566](/wiki/Special:BookSources/9780823961566). +5. ^ [***a***](#cite_ref-FOOTNOTECarboneras1992536_5-0) [***b***](#cite_ref-FOOTNOTECarboneras1992536_5-1) [***c***](#cite_ref-FOOTNOTECarboneras1992536_5-2) [***d***](#cite_ref-FOOTNOTECarboneras1992536_5-3) [Carboneras 1992](#CITEREFCarboneras1992), p. 536. +6. [**^**](#cite_ref-FOOTNOTELivezey1986737–738_6-0) [Livezey 1986](#CITEREFLivezey1986), pp. 737-738. +7. [**^**](#cite_ref-FOOTNOTEMadsenMcHughde_Kloet1988452_7-0) [Madsen, McHugh & de Kloet 1988](#CITEREFMadsenMcHughde_Kloet1988), p. 452. +8. [**^**](#cite_ref-FOOTNOTEDonne-GousséLaudetHänni2002353–354_8-0) [Donne-Goussé, Laudet & Hänni 2002](#CITEREFDonne-GousséLaudetHänni2002), pp. 353-354. +9. ^ [***a***](#cite_ref-FOOTNOTECarboneras1992540_9-0) [***b***](#cite_ref-FOOTNOTECarboneras1992540_9-1) [***c***](#cite_ref-FOOTNOTECarboneras1992540_9-2) [***d***](#cite_ref-FOOTNOTECarboneras1992540_9-3) [***e***](#cite_ref-FOOTNOTECarboneras1992540_9-4) [***f***](#cite_ref-FOOTNOTECarboneras1992540_9-5) [Carboneras 1992](#CITEREFCarboneras1992), p. 540. +10. [**^**](#cite_ref-FOOTNOTEElphickDunningSibley2001191_10-0) [Elphick, Dunning & Sibley 2001](#CITEREFElphickDunningSibley2001), p. 191. +11. [**^**](#cite_ref-FOOTNOTEKear2005448_11-0) [Kear 2005](#CITEREFKear2005), p. 448. +12. [**^**](#cite_ref-FOOTNOTEKear2005622–623_12-0) [Kear 2005](#CITEREFKear2005), p. 622-623. +13. [**^**](#cite_ref-FOOTNOTEKear2005686_13-0) [Kear 2005](#CITEREFKear2005), p. 686. +14. [**^**](#cite_ref-FOOTNOTEElphickDunningSibley2001193_14-0) [Elphick, Dunning & Sibley 2001](#CITEREFElphickDunningSibley2001), p. 193. +15. ^ [***a***](#cite_ref-FOOTNOTECarboneras1992537_15-0) [***b***](#cite_ref-FOOTNOTECarboneras1992537_15-1) [***c***](#cite_ref-FOOTNOTECarboneras1992537_15-2) [***d***](#cite_ref-FOOTNOTECarboneras1992537_15-3) [***e***](#cite_ref-FOOTNOTECarboneras1992537_15-4) [***f***](#cite_ref-FOOTNOTECarboneras1992537_15-5) [***g***](#cite_ref-FOOTNOTECarboneras1992537_15-6) [Carboneras 1992](#CITEREFCarboneras1992), p. 537. +16. [**^**](#cite_ref-FOOTNOTEAmerican_Ornithologists'_Union1998xix_16-0) [American Ornithologists' Union 1998](#CITEREFAmerican_Ornithologists'_Union1998), p. xix. +17. [**^**](#cite_ref-FOOTNOTEAmerican_Ornithologists'_Union1998_17-0) [American Ornithologists' Union 1998](#CITEREFAmerican_Ornithologists'_Union1998). +18. [**^**](#cite_ref-FOOTNOTECarboneras1992538_18-0) [Carboneras 1992](#CITEREFCarboneras1992), p. 538. +19. [**^**](#cite_ref-FOOTNOTEChristidisBoles200862_19-0) [Christidis & Boles 2008](#CITEREFChristidisBoles2008), p. 62. +20. [**^**](#cite_ref-FOOTNOTEShirihai2008239,_245_20-0) [Shirihai 2008](#CITEREFShirihai2008), pp. 239, 245. +21. ^ [***a***](#cite_ref-FOOTNOTEPrattBrunerBerrett198798–107_21-0) [***b***](#cite_ref-FOOTNOTEPrattBrunerBerrett198798–107_21-1) [Pratt, Bruner & Berrett 1987](#CITEREFPrattBrunerBerrett1987), pp. 98-107. +22. [**^**](#cite_ref-FOOTNOTEFitterFitterHosking200052–3_22-0) [Fitter, Fitter & Hosking 2000](#CITEREFFitterFitterHosking2000), pp. 52-3. +23. [**^**](#cite_ref-23) ["Pacific Black Duck"](http://www.wiresnr.org/pacificblackduck.html). *www.wiresnr.org*. Retrieved 2018-04-27. +24. [**^**](#cite_ref-24) Ogden, Evans. ["Dabbling Ducks"](https://www.sfu.ca/biology/wildberg/species/dabbducks.html). CWE. Retrieved 2006-11-02. +25. [**^**](#cite_ref-25) Karl Mathiesen (16 March 2015). ["Don't feed the ducks bread, say conservationists"](https://www.theguardian.com/environment/2015/mar/16/dont-feed-the-ducks-bread-say-conservationists). *The Guardian*. Retrieved 13 November 2016. +26. [**^**](#cite_ref-26) Rohwer, Frank C.; Anderson, Michael G. (1988). "Female-Biased Philopatry, Monogamy, and the Timing of Pair Formation in Migratory Waterfowl". *Current Ornithology*. pp. 187-221. [doi](/wiki/Doi_(identifier)) : [10.1007/978-1-4615-6787-5_4](https://doi.org/10.1007%2F978-1-4615-6787-5_4). [ISBN](/wiki/ISBN_(identifier)) [978-1-4615-6789-9](/wiki/Special:BookSources/978-1-4615-6789-9). +27. [**^**](#cite_ref-27) Smith, Cyndi M.; Cooke, Fred; Robertson, Gregory J.; Goudie, R. Ian; Boyd, W. Sean (2000). ["Long-Term Pair Bonds in Harlequin Ducks"](https://doi.org/10.1093%2Fcondor%2F102.1.201). *The Condor*. **102** (1): 201-205. [doi](/wiki/Doi_(identifier)) : [10.1093/condor/102.1.201](https://doi.org/10.1093%2Fcondor%2F102.1.201). [hdl](/wiki/Hdl_(identifier)) : [10315/13797](https://hdl.handle.net/10315%2F13797). +28. [**^**](#cite_ref-28) ["If You Find An Orphaned Duckling - Wildlife Rehabber"](https://web.archive.org/web/20180923152911/http://wildliferehabber.com/content/if-you-find-duckling). *wildliferehabber.com*. Archived from [the original](https://wildliferehabber.com/content/if-you-find-duckling) on 2018-09-23. Retrieved 2018-12-22. +29. [**^**](#cite_ref-29) Carver, Heather (2011). [*The Duck Bible*](https://books.google.com/books?id=VGofAwAAQBAJ&q=mallard+sound+deep+and+raspy&pg=PA39). Lulu.com. [ISBN](/wiki/ISBN_(identifier)) [9780557901562](/wiki/Special:BookSources/9780557901562). [[*self-published source*](/wiki/Wikipedia:Verifiability#Self-published_sources)] +30. [**^**](#cite_ref-30) Titlow, Budd (2013-09-03). [*Bird Brains: Inside the Strange Minds of Our Fine Feathered Friends*](https://books.google.com/books?id=fXJBBAAAQBAJ&q=Females+of+most+dabbling+ducks+make+the+classic+%22quack%22+sound+but+most+ducks+don%27t+quack&pg=PA123). Rowman & Littlefield. [ISBN](/wiki/ISBN_(identifier)) [9780762797707](/wiki/Special:BookSources/9780762797707). +31. [**^**](#cite_ref-31) Amos, Jonathan (2003-09-08). ["Sound science is quackers"](http://news.bbc.co.uk/2/hi/science/nature/3086890.stm). *BBC News*. Retrieved 2006-11-02. +32. [**^**](#cite_ref-32) ["Mythbusters Episode 8"](http://mythbustersresults.com/episode8). 12 December 2003. +33. [**^**](#cite_ref-FOOTNOTEErlandson1994171_33-0) [Erlandson 1994](#CITEREFErlandson1994), p. 171. +34. [**^**](#cite_ref-FOOTNOTEJeffries2008168,_243_34-0) [Jeffries 2008](#CITEREFJeffries2008), pp. 168, 243. +35. ^ [***a***](#cite_ref-FOOTNOTESued-Badillo200365_35-0) [***b***](#cite_ref-FOOTNOTESued-Badillo200365_35-1) [Sued-Badillo 2003](#CITEREFSued-Badillo2003), p. 65. +36. [**^**](#cite_ref-FOOTNOTEThorpe199668_36-0) [Thorpe 1996](#CITEREFThorpe1996), p. 68. +37. [**^**](#cite_ref-FOOTNOTEMaisels199942_37-0) [Maisels 1999](#CITEREFMaisels1999), p. 42. +38. [**^**](#cite_ref-FOOTNOTERau1876133_38-0) [Rau 1876](#CITEREFRau1876), p. 133. +39. [**^**](#cite_ref-FOOTNOTEHigman201223_39-0) [Higman 2012](#CITEREFHigman2012), p. 23. +40. [**^**](#cite_ref-FOOTNOTEHume201253_40-0) [Hume 2012](#CITEREFHume2012), p. 53. +41. [**^**](#cite_ref-FOOTNOTEHume201252_41-0) [Hume 2012](#CITEREFHume2012), p. 52. +42. [**^**](#cite_ref-FOOTNOTEFieldhouse2002167_42-0) [Fieldhouse 2002](#CITEREFFieldhouse2002), p. 167. +43. [**^**](#cite_ref-43) Livingston, A. D. (1998-01-01). [*Guide to Edible Plants and Animals*](https://books.google.com/books?id=NViSMffyaSgC&q=%C2%A0%C2%A0In+many+areas,+wild+ducks+of+various+species+are+hunted+for+food+or+sport). Wordsworth Editions, Limited. [ISBN](/wiki/ISBN_(identifier)) [9781853263774](/wiki/Special:BookSources/9781853263774). +44. [**^**](#cite_ref-44) ["Study plan for waterfowl injury assessment: Determining PCB concentrations in Hudson river resident waterfowl"](https://www.dec.ny.gov/docs/fish_marine_pdf/wfp09a.pdf) (PDF). *New York State Department of Environmental Conservation*. US Department of Commerce. December 2008. p. 3. [Archived](https://ghostarchive.org/archive/20221009/https://www.dec.ny.gov/docs/fish_marine_pdf/wfp09a.pdf) (PDF) from the original on 2022-10-09. Retrieved 2 July 2019. +45. [**^**](#cite_ref-45) ["FAOSTAT"](http://www.fao.org/faostat/en/#data/QL). *www.fao.org*. Retrieved 2019-10-25. +46. [**^**](#cite_ref-46) ["Anas platyrhynchos, Domestic Duck; DigiMorph Staff - The University of Texas at Austin"](http://digimorph.org/specimens/anas_platyrhynchos/skull/). Digimorph.org. Retrieved 2012-12-23. +47. [**^**](#cite_ref-47) Sy Montgomery. ["Mallard; Encyclopædia Britannica"](https://www.britannica.com/eb/topic-360302/mallard). *Britannica.com*. Retrieved 2012-12-23. +48. [**^**](#cite_ref-48) Glenday, Craig (2014). [*Guinness World Records*](https://archive.org/details/guinnessworldrec0000unse_r3e7/page/135). Guinness World Records Limited. pp. [135](https://archive.org/details/guinnessworldrec0000unse_r3e7/page/135). [ISBN](/wiki/ISBN_(identifier)) [978-1-908843-15-9](/wiki/Special:BookSources/978-1-908843-15-9). +49. [**^**](#cite_ref-49) *Suomen kunnallisvaakunat* (in Finnish). Suomen Kunnallisliitto. 1982. p. 147. [ISBN](/wiki/ISBN_(identifier)) [951-773-085-3](/wiki/Special:BookSources/951-773-085-3). +50. [**^**](#cite_ref-50) ["Lubānas simbolika"](http://www.lubana.lv/index.php/lv/homepage/lubanas-pilseta-2) (in Latvian). Retrieved September 9, 2021. +51. [**^**](#cite_ref-51) ["Föglö"](http://digi.narc.fi/digi/view.ka?kuid=1738595) (in Swedish). Retrieved September 9, 2021. +52. [**^**](#cite_ref-52) Young, Emma. ["World's funniest joke revealed"](https://www.newscientist.com/article/dn2876-worlds-funniest-joke-revealed/). *New Scientist*. Retrieved 7 January 2019. +53. [**^**](#cite_ref-53) ["Howard the Duck (character)"](http://www.comics.org/character/name/Howard%20the%20Duck/sort/chrono/). [*Grand Comics Database*](/wiki/Grand_Comics_Database). +54. [**^**](#cite_ref-54) [Sanderson, Peter](/wiki/Peter_Sanderson); Gilbert, Laura (2008). "1970s". *Marvel Chronicle A Year by Year History*. London, United Kingdom: [Dorling Kindersley](/wiki/Dorling_Kindersley). p. 161. [ISBN](/wiki/ISBN_(identifier)) [978-0756641238](/wiki/Special:BookSources/978-0756641238). December saw the debut of the cigar-smoking Howard the Duck. In this story by writer Steve Gerber and artist Val Mayerik, various beings from different realities had begun turning up in the Man-Thing's Florida swamp, including this bad-tempered talking duck. +55. [**^**](#cite_ref-55) ["The Duck"](https://goducks.com/sports/2003/8/28/153778.aspx). *University of Oregon Athletics*. Retrieved 2022-01-20. ### Sources -- American Ornithologists' Union (1998). [*Checklist of North American Birds*](https://americanornithology.org/wp-content/uploads/2019/07/AOSChecklistTin-Falcon.pdf) (PDF) . Washington, DC: American Ornithologists' Union. [ISBN](/wiki/ISBN_(identifier)) [978-1-891276-00-2](/wiki/Special:BookSources/978-1-891276-00-2) . [Archived](https://ghostarchive.org/archive/20221009/https://americanornithology.org/wp-content/uploads/2019/07/AOSChecklistTin-Falcon.pdf) (PDF) from the original on 2022-10-09. -- Carboneras, Carlos (1992). del Hoyo, Josep; Elliott, Andrew; Sargatal, Jordi (eds.). *Handbook of the Birds of the World* . Vol. 1: Ostrich to Ducks. Barcelona: Lynx Edicions. [ISBN](/wiki/ISBN_(identifier)) [978-84-87334-10-8](/wiki/Special:BookSources/978-84-87334-10-8) . -- Christidis, Les; Boles, Walter E., eds. (2008). *Systematics and Taxonomy of Australian Birds* . Collingwood, VIC: Csiro Publishing. [ISBN](/wiki/ISBN_(identifier)) [978-0-643-06511-6](/wiki/Special:BookSources/978-0-643-06511-6) . -- Donne-Goussé, Carole; Laudet, Vincent; Hänni, Catherine (July 2002). "A molecular phylogeny of Anseriformes based on mitochondrial DNA analysis". *Molecular Phylogenetics and Evolution* . **23** (3): 339-356. [Bibcode](/wiki/Bibcode_(identifier)) : [2002MolPE..23..339D](https://ui.adsabs.harvard.edu/abs/2002MolPE..23..339D) . [doi](/wiki/Doi_(identifier)) : [10.1016/S1055-7903(02)00019-2](https://doi.org/10.1016%2FS1055-7903%2802%2900019-2) . [PMID](/wiki/PMID_(identifier)) [12099792](https://pubmed.ncbi.nlm.nih.gov/12099792) . -- Elphick, Chris; Dunning, John B. Jr.; Sibley, David, eds. (2001). *The Sibley Guide to Bird Life and Behaviour* . London: Christopher Helm. [ISBN](/wiki/ISBN_(identifier)) [978-0-7136-6250-4](/wiki/Special:BookSources/978-0-7136-6250-4) . -- Erlandson, Jon M. (1994). [*Early Hunter-Gatherers of the California Coast*](https://books.google.com/books?id=nGTaBwAAQBAJ&pg=171) . New York, NY: Springer Science & Business Media. [ISBN](/wiki/ISBN_(identifier)) [978-1-4419-3231-0](/wiki/Special:BookSources/978-1-4419-3231-0) . -- Fieldhouse, Paul (2002). [*Food, Feasts, and Faith: An Encyclopedia of Food Culture in World Religions*](https://books.google.com/books?id=P-FqDgAAQBAJ&pg=PA167) . Vol. I: A-K. Santa Barbara: ABC-CLIO. [ISBN](/wiki/ISBN_(identifier)) [978-1-61069-412-4](/wiki/Special:BookSources/978-1-61069-412-4) . -- Fitter, Julian; Fitter, Daniel; Hosking, David (2000). *Wildlife of the Galápagos* . Princeton, NJ: Princeton University Press. [ISBN](/wiki/ISBN_(identifier)) [978-0-691-10295-5](/wiki/Special:BookSources/978-0-691-10295-5) . -- Higman, B. W. (2012). [*How Food Made History*](https://books.google.com/books?id=YIUoz98yMvgC&pg=RA1-PA1801) . Chichester, UK: John Wiley & Sons. [ISBN](/wiki/ISBN_(identifier)) [978-1-4051-8947-7](/wiki/Special:BookSources/978-1-4051-8947-7) . -- Hume, Julian H. (2012). [*Extinct Birds*](https://books.google.com/books?id=40sxDwAAQBAJ&pg=PA53) . London: Christopher Helm. [ISBN](/wiki/ISBN_(identifier)) [978-1-4729-3744-5](/wiki/Special:BookSources/978-1-4729-3744-5) . -- Jeffries, Richard (2008). [*Holocene Hunter-Gatherers of the Lower Ohio River Valley*](https://archive.org/details/holocenehunterga0000jeff/mode/2up) . Tuscaloosa: University of Alabama Press. [ISBN](/wiki/ISBN_(identifier)) [978-0-8173-1658-7](/wiki/Special:BookSources/978-0-8173-1658-7) . -- Kear, Janet, ed. (2005). *Ducks, Geese and Swans: Species Accounts (* Cairina *to* Mergus *)* . Bird Families of the World. Oxford: Oxford University Press. [ISBN](/wiki/ISBN_(identifier)) [978-0-19-861009-0](/wiki/Special:BookSources/978-0-19-861009-0) . -- Livezey, Bradley C. (October 1986). ["A phylogenetic analysis of recent Anseriform genera using morphological characters"](https://sora.unm.edu/sites/default/files/journals/auk/v103n04/p0737-p0754.pdf) (PDF) . *The Auk* . **103** (4): 737-754. [doi](/wiki/Doi_(identifier)) : [10.1093/auk/103.4.737](https://doi.org/10.1093%2Fauk%2F103.4.737) . [Archived](https://ghostarchive.org/archive/20221009/https://sora.unm.edu/sites/default/files/journals/auk/v103n04/p0737-p0754.pdf) (PDF) from the original on 2022-10-09. -- Madsen, Cort S.; McHugh, Kevin P.; de Kloet, Siwo R. (July 1988). ["A partial classification of waterfowl (Anatidae) based on single-copy DNA"](https://sora.unm.edu/sites/default/files/journals/auk/v105n03/p0452-p0459.pdf) (PDF) . *The Auk* . **105** (3): 452-459. [doi](/wiki/Doi_(identifier)) : [10.1093/auk/105.3.452](https://doi.org/10.1093%2Fauk%2F105.3.452) . [Archived](https://ghostarchive.org/archive/20221009/https://sora.unm.edu/sites/default/files/journals/auk/v105n03/p0452-p0459.pdf) (PDF) from the original on 2022-10-09. -- Maisels, Charles Keith (1999). [*Early Civilizations of the Old World*](https://books.google.com/books?id=I2dgI2ijww8C&pg=PA42) . London: Routledge. [ISBN](/wiki/ISBN_(identifier)) [978-0-415-10975-8](/wiki/Special:BookSources/978-0-415-10975-8) . -- Pratt, H. Douglas; Bruner, Phillip L.; Berrett, Delwyn G. (1987). *A Field Guide to the Birds of Hawaii and the Tropical Pacific* . Princeton, NJ: Princeton University Press. [ISBN](/wiki/ISBN_(identifier)) [0-691-02399-9](/wiki/Special:BookSources/0-691-02399-9) . -- Rau, Charles (1876). [*Early Man in Europe*](https://books.google.com/books?id=9XBgAAAAIAAJ&pg=133) . New York: Harper & Brothers. [LCCN](/wiki/LCCN_(identifier)) [05040168](https://lccn.loc.gov/05040168) . -- Shirihai, Hadoram (2008). *A Complete Guide to Antarctic Wildlife* . Princeton, NJ, US: Princeton University Press. [ISBN](/wiki/ISBN_(identifier)) [978-0-691-13666-0](/wiki/Special:BookSources/978-0-691-13666-0) . -- Sued-Badillo, Jalil (2003). [*Autochthonous Societies*](https://books.google.com/books?id=zexcW7q-4LgC&pg=PA65) . General History of the Caribbean. Paris: UNESCO. [ISBN](/wiki/ISBN_(identifier)) [978-92-3-103832-7](/wiki/Special:BookSources/978-92-3-103832-7) . -- Thorpe, I. J. (1996). [*The Origins of Agriculture in Europe*](https://books.google.com/books?id=YA-EAgAAQBAJ&pg=PA68) . New York: Routledge. [ISBN](/wiki/ISBN_(identifier)) [978-0-415-08009-5](/wiki/Special:BookSources/978-0-415-08009-5) . +- American Ornithologists' Union (1998). [*Checklist of North American Birds*](https://americanornithology.org/wp-content/uploads/2019/07/AOSChecklistTin-Falcon.pdf) (PDF). Washington, DC: American Ornithologists' Union. [ISBN](/wiki/ISBN_(identifier)) [978-1-891276-00-2](/wiki/Special:BookSources/978-1-891276-00-2). [Archived](https://ghostarchive.org/archive/20221009/https://americanornithology.org/wp-content/uploads/2019/07/AOSChecklistTin-Falcon.pdf) (PDF) from the original on 2022-10-09. +- Carboneras, Carlos (1992). del Hoyo, Josep; Elliott, Andrew; Sargatal, Jordi (eds.). *Handbook of the Birds of the World*. Vol. 1: Ostrich to Ducks. Barcelona: Lynx Edicions. [ISBN](/wiki/ISBN_(identifier)) [978-84-87334-10-8](/wiki/Special:BookSources/978-84-87334-10-8). +- Christidis, Les; Boles, Walter E., eds. (2008). *Systematics and Taxonomy of Australian Birds*. Collingwood, VIC: Csiro Publishing. [ISBN](/wiki/ISBN_(identifier)) [978-0-643-06511-6](/wiki/Special:BookSources/978-0-643-06511-6). +- Donne-Goussé, Carole; Laudet, Vincent; Hänni, Catherine (July 2002). "A molecular phylogeny of Anseriformes based on mitochondrial DNA analysis". *Molecular Phylogenetics and Evolution*. **23** (3): 339-356. [Bibcode](/wiki/Bibcode_(identifier)) : [2002MolPE..23..339D](https://ui.adsabs.harvard.edu/abs/2002MolPE..23..339D). [doi](/wiki/Doi_(identifier)) : [10.1016/S1055-7903(02)00019-2](https://doi.org/10.1016%2FS1055-7903%2802%2900019-2). [PMID](/wiki/PMID_(identifier)) [12099792](https://pubmed.ncbi.nlm.nih.gov/12099792). +- Elphick, Chris; Dunning, John B. Jr.; Sibley, David, eds. (2001). *The Sibley Guide to Bird Life and Behaviour*. London: Christopher Helm. [ISBN](/wiki/ISBN_(identifier)) [978-0-7136-6250-4](/wiki/Special:BookSources/978-0-7136-6250-4). +- Erlandson, Jon M. (1994). [*Early Hunter-Gatherers of the California Coast*](https://books.google.com/books?id=nGTaBwAAQBAJ&pg=171). New York, NY: Springer Science & Business Media. [ISBN](/wiki/ISBN_(identifier)) [978-1-4419-3231-0](/wiki/Special:BookSources/978-1-4419-3231-0). +- Fieldhouse, Paul (2002). [*Food, Feasts, and Faith: An Encyclopedia of Food Culture in World Religions*](https://books.google.com/books?id=P-FqDgAAQBAJ&pg=PA167). Vol. I: A-K. Santa Barbara: ABC-CLIO. [ISBN](/wiki/ISBN_(identifier)) [978-1-61069-412-4](/wiki/Special:BookSources/978-1-61069-412-4). +- Fitter, Julian; Fitter, Daniel; Hosking, David (2000). *Wildlife of the Galápagos*. Princeton, NJ: Princeton University Press. [ISBN](/wiki/ISBN_(identifier)) [978-0-691-10295-5](/wiki/Special:BookSources/978-0-691-10295-5). +- Higman, B. W. (2012). [*How Food Made History*](https://books.google.com/books?id=YIUoz98yMvgC&pg=RA1-PA1801). Chichester, UK: John Wiley & Sons. [ISBN](/wiki/ISBN_(identifier)) [978-1-4051-8947-7](/wiki/Special:BookSources/978-1-4051-8947-7). +- Hume, Julian H. (2012). [*Extinct Birds*](https://books.google.com/books?id=40sxDwAAQBAJ&pg=PA53). London: Christopher Helm. [ISBN](/wiki/ISBN_(identifier)) [978-1-4729-3744-5](/wiki/Special:BookSources/978-1-4729-3744-5). +- Jeffries, Richard (2008). [*Holocene Hunter-Gatherers of the Lower Ohio River Valley*](https://archive.org/details/holocenehunterga0000jeff/mode/2up). Tuscaloosa: University of Alabama Press. [ISBN](/wiki/ISBN_(identifier)) [978-0-8173-1658-7](/wiki/Special:BookSources/978-0-8173-1658-7). +- Kear, Janet, ed. (2005). *Ducks, Geese and Swans: Species Accounts (* Cairina *to* Mergus *)*. Bird Families of the World. Oxford: Oxford University Press. [ISBN](/wiki/ISBN_(identifier)) [978-0-19-861009-0](/wiki/Special:BookSources/978-0-19-861009-0). +- Livezey, Bradley C. (October 1986). ["A phylogenetic analysis of recent Anseriform genera using morphological characters"](https://sora.unm.edu/sites/default/files/journals/auk/v103n04/p0737-p0754.pdf) (PDF). *The Auk*. **103** (4): 737-754. [doi](/wiki/Doi_(identifier)) : [10.1093/auk/103.4.737](https://doi.org/10.1093%2Fauk%2F103.4.737). [Archived](https://ghostarchive.org/archive/20221009/https://sora.unm.edu/sites/default/files/journals/auk/v103n04/p0737-p0754.pdf) (PDF) from the original on 2022-10-09. +- Madsen, Cort S.; McHugh, Kevin P.; de Kloet, Siwo R. (July 1988). ["A partial classification of waterfowl (Anatidae) based on single-copy DNA"](https://sora.unm.edu/sites/default/files/journals/auk/v105n03/p0452-p0459.pdf) (PDF). *The Auk*. **105** (3): 452-459. [doi](/wiki/Doi_(identifier)) : [10.1093/auk/105.3.452](https://doi.org/10.1093%2Fauk%2F105.3.452). [Archived](https://ghostarchive.org/archive/20221009/https://sora.unm.edu/sites/default/files/journals/auk/v105n03/p0452-p0459.pdf) (PDF) from the original on 2022-10-09. +- Maisels, Charles Keith (1999). [*Early Civilizations of the Old World*](https://books.google.com/books?id=I2dgI2ijww8C&pg=PA42). London: Routledge. [ISBN](/wiki/ISBN_(identifier)) [978-0-415-10975-8](/wiki/Special:BookSources/978-0-415-10975-8). +- Pratt, H. Douglas; Bruner, Phillip L.; Berrett, Delwyn G. (1987). *A Field Guide to the Birds of Hawaii and the Tropical Pacific*. Princeton, NJ: Princeton University Press. [ISBN](/wiki/ISBN_(identifier)) [0-691-02399-9](/wiki/Special:BookSources/0-691-02399-9). +- Rau, Charles (1876). [*Early Man in Europe*](https://books.google.com/books?id=9XBgAAAAIAAJ&pg=133). New York: Harper & Brothers. [LCCN](/wiki/LCCN_(identifier)) [05040168](https://lccn.loc.gov/05040168). +- Shirihai, Hadoram (2008). *A Complete Guide to Antarctic Wildlife*. Princeton, NJ, US: Princeton University Press. [ISBN](/wiki/ISBN_(identifier)) [978-0-691-13666-0](/wiki/Special:BookSources/978-0-691-13666-0). +- Sued-Badillo, Jalil (2003). [*Autochthonous Societies*](https://books.google.com/books?id=zexcW7q-4LgC&pg=PA65). General History of the Caribbean. Paris: UNESCO. [ISBN](/wiki/ISBN_(identifier)) [978-92-3-103832-7](/wiki/Special:BookSources/978-92-3-103832-7). +- Thorpe, I. J. (1996). [*The Origins of Agriculture in Europe*](https://books.google.com/books?id=YA-EAgAAQBAJ&pg=PA68). New York: Routledge. [ISBN](/wiki/ISBN_(identifier)) [978-0-415-08009-5](/wiki/Special:BookSources/978-0-415-08009-5). ## External links @@ -587,8 +587,8 @@ Hidden categories: - [Webarchive template wayback links](/wiki/Category:Webarchive_template_wayback_links) - [Articles with Project Gutenberg links](/wiki/Category:Articles_with_Project_Gutenberg_links) - [Articles containing video clips](/wiki/Category:Articles_containing_video_clips) -- This page was last edited on 21 September 2024, at 12:11 (UTC) . -- Text is available under the [Creative Commons Attribution-ShareAlike License 4.0](//en.wikipedia.org/wiki/Wikipedia:Text_of_the_Creative_Commons_Attribution-ShareAlike_4.0_International_License) ; additional terms may apply. By using this site, you agree to the [Terms of Use](//foundation.wikimedia.org/wiki/Special:MyLanguage/Policy:Terms_of_Use) and [Privacy Policy](//foundation.wikimedia.org/wiki/Special:MyLanguage/Policy:Privacy_policy) . Wikipedia® is a registered trademark of the [Wikimedia Foundation, Inc.](//wikimediafoundation.org/) , a non-profit organization. +- This page was last edited on 21 September 2024, at 12:11 (UTC). +- Text is available under the [Creative Commons Attribution-ShareAlike License 4.0](//en.wikipedia.org/wiki/Wikipedia:Text_of_the_Creative_Commons_Attribution-ShareAlike_4.0_International_License); additional terms may apply. By using this site, you agree to the [Terms of Use](//foundation.wikimedia.org/wiki/Special:MyLanguage/Policy:Terms_of_Use) and [Privacy Policy](//foundation.wikimedia.org/wiki/Special:MyLanguage/Policy:Privacy_policy). Wikipedia® is a registered trademark of the [Wikimedia Foundation, Inc.](//wikimediafoundation.org/), a non-profit organization. - [Privacy policy](https://foundation.wikimedia.org/wiki/Special:MyLanguage/Policy:Privacy_policy) - [About Wikipedia](/wiki/Wikipedia:About) - [Disclaimers](/wiki/Wikipedia:General_disclaimer) diff --git a/crates/fleischwolf/tests/data/jats/expected/elife-56337.nxml.strict.md b/crates/fleischwolf/tests/data/jats/expected/elife-56337.nxml.strict.md index 9374d341..3abbba4e 100644 --- a/crates/fleischwolf/tests/data/jats/expected/elife-56337.nxml.strict.md +++ b/crates/fleischwolf/tests/data/jats/expected/elife-56337.nxml.strict.md @@ -174,7 +174,7 @@ Conceptualization, Resources, Supervision, Funding acquisition, Investigation, W Conceptualization, Resources, Supervision, Funding acquisition, Investigation, Methodology, Writing - original draft, Project administration, Writing - review and editing. -Animal experimentation: All studies using mice were performed in accordance to the Guide for the Care and Use of Laboratory Animals of the NIH, under IACUC animal protocol (ASP )18-026. +Animal experimentation: All studies using mice were performed in accordance to the Guide for the Care and Use of Laboratory Animals of the NIH, under IACUC animal protocol (ASP)18-026. ## Additional files diff --git a/crates/fleischwolf/tests/data/jats/expected/elife-56337.xml.strict.md b/crates/fleischwolf/tests/data/jats/expected/elife-56337.xml.strict.md index 9374d341..3abbba4e 100644 --- a/crates/fleischwolf/tests/data/jats/expected/elife-56337.xml.strict.md +++ b/crates/fleischwolf/tests/data/jats/expected/elife-56337.xml.strict.md @@ -174,7 +174,7 @@ Conceptualization, Resources, Supervision, Funding acquisition, Investigation, W Conceptualization, Resources, Supervision, Funding acquisition, Investigation, Methodology, Writing - original draft, Project administration, Writing - review and editing. -Animal experimentation: All studies using mice were performed in accordance to the Guide for the Care and Use of Laboratory Animals of the NIH, under IACUC animal protocol (ASP )18-026. +Animal experimentation: All studies using mice were performed in accordance to the Guide for the Care and Use of Laboratory Animals of the NIH, under IACUC animal protocol (ASP)18-026. ## Additional files diff --git a/crates/fleischwolf/tests/data/odf/expected/text_document_01.odt.strict.md b/crates/fleischwolf/tests/data/odf/expected/text_document_01.odt.strict.md index 8240ec45..2ffb4859 100644 --- a/crates/fleischwolf/tests/data/odf/expected/text_document_01.odt.strict.md +++ b/crates/fleischwolf/tests/data/odf/expected/text_document_01.odt.strict.md @@ -23,6 +23,6 @@ Contrary to popular belief, Lorem Ipsum is not simply random text. It has roots ### Heading level 2: B -Contrary to popular belief, **Lorem Ipsum is not simply random text** . Richard McClintock, *a Latin professor at Hampden-Sydney College in Virginia* , looked up one of the more obscure Latin words, consectetur, from a Lorem Ipsum passage, and going through the cites of the word in classical literature , discovered the undoubtable source. Lorem Ipsum comes from sections 1.10.32 and 1.10.33 of "de Finibus Bonorum et Malorum" ( ~~The Extremes of Good and Evil~~ ) by Cicero, written in 45 BC. +Contrary to popular belief, **Lorem Ipsum is not simply random text**. Richard McClintock, *a Latin professor at Hampden-Sydney College in Virginia*, looked up one of the more obscure Latin words, consectetur, from a Lorem Ipsum passage, and going through the cites of the word in classical literature, discovered the undoubtable source. Lorem Ipsum comes from sections 1.10.32 and 1.10.33 of "de Finibus Bonorum et Malorum" (~~The Extremes of Good and Evil~~) by Cicero, written in 45 BC. X 2 + Y 2 = Z diff --git a/crates/fleischwolf/tests/data/odf/expected/text_document_03.odt.strict.md b/crates/fleischwolf/tests/data/odf/expected/text_document_03.odt.strict.md index ed14d009..63bcd231 100644 --- a/crates/fleischwolf/tests/data/odf/expected/text_document_03.odt.strict.md +++ b/crates/fleischwolf/tests/data/odf/expected/text_document_03.odt.strict.md @@ -19,7 +19,7 @@ Before table | A | B | C | | ~~Cell 1~~ | Cell 2 | Cell 3 | -After table with **bold** , underline , ~~strikethrough~~ , and *italic* formatting +After table with **bold**, underline, ~~strikethrough~~, and *italic* formatting ### Table with pictures diff --git a/crates/fleischwolf/tests/data/pptx/expected/powerpoint_comments.pptx.strict.md b/crates/fleischwolf/tests/data/pptx/expected/powerpoint_comments.pptx.strict.md index 6990f821..64327d06 100644 --- a/crates/fleischwolf/tests/data/pptx/expected/powerpoint_comments.pptx.strict.md +++ b/crates/fleischwolf/tests/data/pptx/expected/powerpoint_comments.pptx.strict.md @@ -8,6 +8,6 @@ Install Docling as a Python library with your favorite package manager # Features -- Import many document formats into a unified and structured Docling Document , including scanned pages via an OCR engine of your choice. +- Import many document formats into a unified and structured Docling Document, including scanned pages via an OCR engine of your choice. - Export a parsed document to formats that simplify processing and ingestion into AI, RAG, and agentic systems. - Extract document components and their properties from the Docling Document. diff --git a/crates/fleischwolf/tests/data/uspto/expected/ipa20110039701.xml.strict.md b/crates/fleischwolf/tests/data/uspto/expected/ipa20110039701.xml.strict.md index f66d1875..4809a615 100644 --- a/crates/fleischwolf/tests/data/uspto/expected/ipa20110039701.xml.strict.md +++ b/crates/fleischwolf/tests/data/uspto/expected/ipa20110039701.xml.strict.md @@ -722,7 +722,7 @@ The reaction mixture is poured onto ice, taken up in ethyl acetate and extracted The following compounds of the formula (I-c) are obtained analogously to Examples (I-c-1) and (I-c-17) and in accordance with the general statements on the preparation: -(I-c) Ex. M.p. [° C.] or ¹H-NMR (400 MHz, No. X Y Z A B M R2 CDCL₃, δ in ppm) Isomerism I-c-2 C₂H₅ CH₃ C₂H₅ —O—C(CH₃)₂—O—CH₂— O i-C₃H₇ δ = 1.21 (d, 6 H), 1.88 and 1.98 (in anti each case mc, in each case 1 H), 3.89 (dd. 2 H), 4.81 (quint, 1H) I-c-3 C₂H₅ CH₃ C₂H₅ —O—C(CH₃)₂—O—CH₂— O i-C₃H₇ δ =1.32 (d, 6 H), 1.92-2.13 (m, 3 H), syn 3.92 (dd, 2H), 4.83 (quint, 1 H) I-c-4 C₂H₅ CH₃ C₂H₅ —O—C(CH₃)₂—O—CH₂— O CH₃ 132-133 syn I-c-5 C₂H₅ CH₃ C₂H₅ ═CH₂ O C₂H₅ δ = 1.26 (t, 3H), 3.20 (mc, 1 H), 4.02 (mc, 1H), 4.82 (mc, 2 H). I-c-6 C₂H₅ CH₃ C₂H₅ —O—CH₂—C(CH₃)₂—CH₂—O— O C₂H₅ 125-126 I-c-7 C₂H₅ CH₃ C₂H₅ —O—(CH₂)₂—O— O C₂H₅ δ = 1.08, 1.09 and 1.22 (in each case t, in each case 3H), 3.20 (mc, 1 H), 3.89 (mc, 4 H), 4.04 (mc, 1H), 4.19 (mc, 2H) I-c-8 C₂H₅ CH₃ C₂H₅ —O—CHCH₃—CH₂—O— O C₂H₅ 79 syn/anti mixture I-c-9 C₂H₅ CH₃ C₂H₅ —O—(CH₂)₄—O— O C₂H₅ 113 I-c-10 C₂H₅ CH₃ C₂H₅ —O—CHCH₃—CH₂—CHCH₃—O— O C₂H₅ δ = 1.02-1.28 (m, 15 H), 3.20 (mc, Isomer mixture with 1H), 3.80-4.01 (m, 3 H), 4.18 (mc, CH₃-groups at the acetal 2H) ring I-c-11 C₂H₅ CH₃ C₂H₅ —O—CH₂—CH═CH—CH₂—O— O C₂H₅ δ = 1.08. 1.12 and 1.24 (in each case t, in each case 3 H), 4.01-4.32 (m, 7 H), 5.65 (s, 2 H) I-c-12 C₂H₅ CH₃ C₂H₅ O C₂H₅ 119 I-c-13 C₂H₅ CH₃ C₂H₅ —O—CH(CH₃)—CH₂—CH(CH₃)O— O C₂H₅ δ = 1.05-1.25 (m, 15H), 3.20 and 3.92 (R,R)-Configuration of (in each case mc, in each case 1 H), the CH₃-groups at the 4.00 (mc, 2H), 4.18 (mc, 2 H) acetal ring I-c-14 C₂H₅ CH₃ C₂H₅ —O—(CH₂)₃—O— O C₂H₅ 110 I-c-15 C₂H₅ CH₃ C₂H₅ —S—(CH₂)₂—S— O C₂H₅ δ = 1.25 (t, 3H), 2.61 (mc, 2H), 3.35 (mc, 5H), 4.19 (mc, 3H), 6.90 (s, 2H) I-c-16 C₂H₅ CH₃ C₂H₅ —S—(CH₂)₃—S— O C₂H₅ δ = 1.26 (t, 3H), 2.83 (mc, 3H), 2.93 (mc, 3H), 4.20 (mc, 2H), 4.22 (mc, 1H) I-c-17 C₂H₅ CH₃ C₂H₅ ═O O C₂H₅ 1.26 (t, 3 H), 3.51 and 4.31 (in each case mc, in each case 1 H), 4.20 (mc, 2H) I-c-18 C₂H₅ CH₃ C₂H₅ —O—C(CH₃)₂—C(CH₃)₂—O— O C₂H₅ 116-117 I-c-19 C₂H₅ CH₃ C₂H₅ —S—(CH₂)₃—O— O C₂H₅ 1.24 (t, 3H), 3.00 (mc, 2H), 3.72 (mc, syn/anti mixture 2H), 4.18 (mc, 1H) I-c-20 C₂H₅ CH₃ C₂H₅ —S—(CH₂)₃—S— O CH₃ 2.62 (mc, 1H), 2.80-2.98 (m, 5H), 3.49 (mc, 1H), 3.78 (s, 1H), 4.23 (mc, 1H) I-c-21 C₂H₅ CH₃ C₂H₅ —O—CH₂—CH═CH—CH₂—O— O CH₃ 1.08 and 1.12 (in each case t, in each case 3H), 3.75 (s, 3H), 4.02 (mc, 2H), 4.16-4.32 (m, 3H), 5.67 (s, 2H) I-c-22 C₂H₅ CH₃ C₂H₅ —O—CH₂—C(CH₃)₂—CH₂—O— O C₂H₅ 98-99 I-c-23 C₂H₅ CH₃ C₂H₅ —O—CH₂—C(CH₃)₂—CH₂—O— O CH₃ 126-127 I-c-24 C₂H₅ CH₃ C₂H₅ —O—CH(CH₃)—CH₂—CH(CH₃)O— O CH₃ 1.03-1.22 (m, 12H), 3.20 (mc, 1H), (R,R)-Configuration of 3.75 (doubled singlet, Σ 3H), 3.90- the CH₃-groups at the 4.02 (m, 3H) acetal ring I-c-25 C₂H₅ CH₃ C₂H₅ —O—CH₂—C(OCH₃)—CH₂—O— O C₂H₅ 1.23 (t, 3H), 2.70-2.79 (m, 1H), 4.19 (mc, 2H), 3.10 (mc, 1H), 3.38 (s, 3H), I-c-26 C₂H₅ CH₃ C₂H₅ —O—CH₂CH(OC₂H₅)CH₂—O— O C₂H₅ 1.03-1.28 (m, 12H); 3.20 and 3.39 (in each case mc, in each case 1H), 3.95- 4.05 (m, 2H), 4.11-4.25 (m, 3H) I-c-27 C₂H₅ CH₃ C₂H₅ —OCH₂—CH(OC₂H₅)CH₂—O— O CH₃ 1.00-1.21 (m, 12H), 3.76 (s, 3H), 3.92- 4.05 (m, 2H), 4.21 (mc, 1H) I-c-28 C₂H₅ CH₃ C₂H₅ —OCH₂—CH(OCH₂C₆H₅)CH₂—O— O C₂H₅ 1.22 (t, 3H), 2.95 (mc, 2H), 4.15 (mc, 2H), 4.55 (s, 2H), 6.88 and 6.91 (in each case s, in each case 1H), 7.27- 7.85 (m, 5H) I-c-29 C₂H₅ CH₃ C₂H₅ —OCH₂—CH(OCH₂C₆H₅)CH₂—O— O CH₃ 2.05-2.12 (m, 2H), 3.41 (mc, 1H), 3.74 (s, 3H), 4.60 (s, 2H), I-c-30 C₂H₅ CH₃ C₂H₅ —S—(CH₂)₂—O— O C₂H₅ 94-95 syn/anti mixture I-c-31 C₂H₅ CH₃ C₂H₅ —S—(CH₂)₂—O— O CH₃ 3.00 (mc, 2H), 3.77 (s, 3H), 3.96 (mc, syn/anti mixture 1H), 4.02-4.10 (m, 2H) I-c-32 C₂H₅ CH₃ C₂H₅ —S—(CH₂)₃—O— O CH₃ 2.95-3.05 (m, 2H), 3.25 (mc, 1H), 3.72 syn/anti mixture (mc, 2H), 3.75 (s, 3H), 4.08 (mc, 1H) I-c-33 C₂H₅ CH₃ C₂H₅ —S—(CH₂)₂—O— O i-C₃H₇ 1.23 (6H), 3.01 (mc, 2H), 4.08 (mc, syn/anti mixture 2H), 4.81 (mc, 1H) I-c-34 C₂H₅ CH₃ C₂H₅ —O—(CH₂)₃—O— O i-C₃H₇ 1.22 (mc, 6H), 3.18 (mc, 1H), 3.79 (mc, 2H), 3.88 (mc, 2H), 3.99, (mc, 1H), 4.81 (hept, 1H) I-c-35 C₂H₅ CH₃ C₂H₅ —O—(CH₂)₃—O— O CH₃ 2.00-2.12 (m, 2H), 3.19 (mc, 1H), 3.73 (s, 3H), 3.88 (mc, 1H), 3.99 (mc, 1H) I-c-36 C₂H₅ CH₃ C₂H₅ —O—(CH₂)₂—O— O i-C₃H₇ 110-111 I-c-37 C₂H₅ CH₃ C₂H₅ —O—(CH₂)₂—O— O CH₃ 116 I-c-38 C₂H₅ CH₃ C₂H₅ ═NOtC₄H₉ O C₂H₅ 1.24 (s, 9H), 1.28 (mc, 3H), 2.62-2.90 (m, 4H), 4.15 (mc, 1H), 4.20 (mc, 2H) I-c-39 C₂H₅ CH₃ C₂H₅ ═NOi-C₃H₇ O C₂H₅ 1.03 (mc, 6H), 1.15-1.30 (m, 9H), 2.35 (hept, 2H), 3.32 (mc, 1H), 4.20 (mc, 2H), 4.25 (mc, 1H) I-c-39 C₂H₅ CH₃ C₂H₅ ═NOCyclopentyl O C₂H₅ 1.52-1.80 (m, 8H), 3.30 (mc, 1H), 4.12-4.22 (m, 3H), 4.62 (mc, 1H) I-c-40 C₂H₅ CH₃ C₂H₅ ═NOCH₂-cyclopropyl O C₂H₅ 0.23 and 0.51 (in each case mc, in each case 2H), 3.19-3.49 (m, 1H), 3.82 (mc, 2H), 4.12-4.24 (m, 3H) I-c-41 C₂H₅ CH₃ C₂H₅ ═NOCH₂C≡CH O C₂H₅ 1.25 (mc, 3H), 2.47 (mc, 1H), 4.15- 4.25 (m, 3H), 4.61 (d, 2H) I-c-42 C₂H₅ CH₃ C₂H₅ O═ O CH₃ 2.60-2.85 (m, 3H), 3.51 (mc, 1H), 3.79 (s, 3H), 4.30 (mc, 1H) I-c-43 C₂H₅ CH₃ C₂H₅ ═NOCH(CH₃)—C≡CH O C₂H₅ 1.28 (t, 3H), 1.48 (mc, 3H), 2.43 (mc, 1H), 3.34 (mc, 1H), 4.80 (mc, 1H) I-c-44 CH₃ CH₃ CH₃ —O—(CH₂)₂—O— O C₂H₅ 1.21 (t, 3H), 2.05, 2.12 and 2.22 (in each case s, in each case 3H), 3.19 (mc, 1H), 3.89 (mc, 4H), 3.91 (mc, 1H), 4.12 (mc, 2H) I-c-45 CH₃ CH₃ CH₃ CH₂═ O C₂H₅ 1.21 (t, 3H), 1.98, 2.06 and 2.24 (in each case s, in each case 3H), 4.15 (mc, 2H), 4.90 (mc, 2H) I-c-46 CH₃ CH₃ CH₃ CH₂═ O CH₃ 1.99, 2.05 and 2.22 (in each case s, in each case 3H), 2.40-2.72 (m, 4H), 3.72 (s, 3H), 4.90 (mc, 2H) I-c-47 CH₃ CH₃ CH₃ —O—(CH₂)₃—O— O C₂H₅ 1.20 (t, 3H), 2.80 (mc, 1H), 3.16 (mc, 1H), 3.70-3.90 (m, 5H), 4.12 (mc, 2H) I-c-48 CH₃ CH₃ CH₃ —O—CH₂—CH(CH₃)—CH₂—O O C₂H₅ 0.71 and 0.82 (in each case d, Σ 3H), 2.45-2.50 (m, 1H), 3.28-3.45 (m, 2H), 4.12 (mc, 2H) I-c-49 CH₃ CH₃ CH₃ —O—(CH₂)₄—O— O CH₃ 122 I-c-50 CH₃ CH₃ CH₃ —O—(CH₂)₄—O— O C₂H₅ 131 I-c-51 CH₃ CH₃ CH₃ —O—CH₂—CH═CH—CH₂—O— O C₂H₅ 1.21 (t, 3H), 4.00-4.31 (m, 6H), 5.65 (mc, 2H), 6.85 (mc, 2H) I-c-52 CH₃ CH₃ CH₃ —O—CH₂—CH═CH—CH₂—O— O CH₃ 3.70 (s, 3H), 3.94 (mc, 1H), 3.96-4.30 (m, 4H), 5.64 (mc, 2H) I-c-53 CH₃ CH₃ CH₃ —O—CH₂—CH(CH₃)—CH₂—O— O CH₃ 0.70 and 0.82 (in each case t, Σ 3H), 2.48 (mc, 1H), 3.15 (mc, 1H), 3.29- 3.45 (m, 2H), 3.71 (s, 3H) I-c-54 CH₃ CH₃ CH₃ —O—C(CH₃)₂—O—CH₂— O C₂H₅ 1.20 (t, 3H), 1.48 (s, 6H), 3.34 (mc, syn/anti mixture 1H), 3.85 and 3.89 (in each case d, in each case 1H), 4.12 (q, 2H) I-c-55 CH₃ CH₃ CH₃ —O—C(CH₃)₂—O—CH₂— O i-C₃H₇ 1.15 and 1.19 (in each case d, Σ 6H), syn/anti mixture 1.38 (s, 6H), 3.85 and 3.90 (in each case d, in each case 1H), 4.72 (hept, 1H) I-c-56 CH₃ CH₃ CH₃ —O—C(CH₃)₂—O—CH₂— O CH₃ 1.38 (s, 6H), 3.36 (mc, 1H), 3.71 (s, syn/anti mixture 3H), 4.00 (mc, 1H) I-c-57 CH₃ CH₃ CH₃ ═O O CH₃ 1.98, 2.10 and 2.26 (in each case s, in each case 3H), 3.50 (mc, 1H), 3.75 (s, 3H), 4.19 (mc, 1H) I-c-58 CH₃ CH₃ CH₃ ═O O C₂H₅ 1.22 (t, 3H), 2.00, 2.11 and 2.26 (in each case s, in each case 3H), 2.42- 2.85 (m, 4H), 4.10-4.22 (3H), I-c-59 CH₃ CH₃ CH₃ ═NOCH₂C≡CH O CH₃ 2.45 (mc, 1H), 2.70-3.08 (m, 4H), 3.75 (s, 3H), 4.61 (mc, 2H), I-c-60 CH₃ CH₃ CH₃ ═NOCH₂C≡CH O C₂H₅ 1.23 (mc, 3H), 2.44 (mc, 1H), 2.70- 3.08 (m, 4H), 4.18 (mc, 2H), 4.61 (mc, 2H) I-c-61 OCH₃ CH₃ CH₃ CH₂═ O C₂H₅ 2.30 (s, 3H), 2.39-2.70 (m, 4H), 3.70 (s, 3H), 4.18 (mc, 2H) I-c-62 OCH₃ CH₃ CH₃ CH₂═ O CH₃ 2.38-2.70 (m, 4H), 3.70 and 3.75 (in each case s, in each case 1H), 4.89 (mc, 2H) I-c-62 OCH₃ CH₃ CH₃ O═ O C₂H₅ 1.25 (mc, 3H), 2.30(s, 3H), 2.40-2.85 (m, 4H), 3.50 (mc, 1H), 4.10 (mc, 2H) I-c-63 OCH₃ CH₃ CH₃ —O—(CH₂)₃—O— O C₂H₅ 1.22 (t, 3H), 2.09 and 2.29 (in each case s, in each case 3H), 2.76-2.80 (m, 1H), 3.18 (mc, 1H), 3.70 (s, 3H) I-c-64 OCH₃ CH₃ CH₃ —O—(CH₂)₂—O— O C₂H₅ 21.12 and 2.30 (in each case s, in each case 3H), 3.70 (s, 3H), 3.85 (mc, 4H), 4.18 (q, 2H) I-c-65 OCH₃ CH₃ CH₃ —O—CH₂—CH═CH—CH₂—O— O C₂H₅ 1.25 (t, 3H), 3.70 (s, 3H), 3.86-430 (m, 7H), 5.64 (mc, 2H) I-c-66 OCH₃ CH₃ CH₃ —O—(CH₂)₄—O— O C₂H₅ 1.22 (t, 3H), 2.22 and 2.30 (in each case s, in each case 3H), 3.12 (mc, 1H), 3.70 (s, 3H), 4.16 (q, 2H) I-c-67 OCH₃ CH₃ C₂H₅ CH₂═ O C₂H₅ 0.99 and 1.08 (in each case t, Σ 3H), 1.26 (mc, 3H), 3.61 and 3.68 (in each case s, Σ 3H), 4.89 (mc, 2H) . I-c-68 OCH₃ CH₃ C₂H₅ O═ O C₂H₅ 1.05 and 1.11 (in each case t, Σ 3H), 1.26 (mc, 3H), 2.25-2.83 (m, 4H), 2.30 (s, 3H), 4.20 (mc, 2H) I-c-69 OCH₃ CH₃ C₂H₅ —O—(CH₂)₂—O— O C₂H₅ 1.09 (t, 3H), 1.24 (t, 3H), 2.31 (s, 3H), 3.70 (s, 3H), 3.88 (mc, 4H), 4.19 (q, 2H) I-c-70 OCH₃ CH₃ C₂H₅ —O—(CH₂)₃—O— O C₂H₅ 1.10 and 1.15 (in each case t, in each case 3H), 3.70 (s, 3H), 3.70-4.00 (m, 4H), 4.18 (q, 2H) I-c-71 C₂H₅ CH₃ CH₃ —O—(CH₂)₂—O— O C₂H₅ 1.08 and 1.22 (in each case t, in each case 3H), 3.88 (mc, 4H), 3.96 (mc, 1H), 4.15 (mc, 2H) I-c-72 C₂H₅ CH₃ CH₃ —O—CH₂—CH═CH—CH₂—O— O C₂H₅ 124-125 I-c-73 C₂H₅ CH₃ CH₃ —O—CH₂—CH═CH—CH₂—O— O CH₃ 1.08 (t, 3H), 3.18 (mc, 1H), 3.71 (s, 3H), 3.95-4.31 (m, 5H), 5.65 (mc, 2H) I-c-74 C₂H₅ CH₃ CH₃ —O—CH₂—CH═CH—CH₂—O— O i-C₃H₇ 1.20 (mc, 6H), 3.91-4.32 (m, 5H), 4.79 (hept, 1H), 5.64 (mc, 2H) I-c-75 C₂H₅ CH₃ CH₃ —O—(CH₂)₄—O— O C₂H₅ 102 I-c-76 C₂H₅ CH₃ CH₃ CH₂═ O C₂H₅ 0.96 and 1.08 (in each case t, Σ 3H), 1.22 (t, 3H), 2.26 (s, 3H), 4.15 (mc, 2H), 4.91 (mc, 1H) I-c-77 C₂H₅ CH₃ CH₃ CH₂═ O CH₃ 0.97 and 1.05 (in each case t, Σ 3H), 3.75 (s, 3H), 4.92 (mc, 2H) I-c-78 C₂H₅ CH₃ CH₃ —O—C(CH₃)₂—O—CH₂— O i-C₃H₇ 116-117 syn/anti mixture I-c-79 C₂H₅ CH₃ CH₃ —O—C(CH₃)₂—O—CH₂— O CH₃ 1.02-1.11 (m, 3H), 1.38 (s, 6H), 3.74 syn/anti mixture (s, 3H), 3.85 and 3.88 (in each case d, in each case 1H) I-c-80 C₂H₅ CH₃ CH₃ —O—C(CH₃)₂—O—CH₂— O C₂H₅ 1.20 (t, 3H), 1.38 (s, 6H), 2.30 (s, 3H), syn/anti mixture 3.86 and 4.00 (in each case d, in each case 1H), 4.15 (mc, 2H) I-c-81 C₂H₅ C₂H₅ C₂H₅ CH₂═ O C₂H₅ 0.98 and 1.07 (in each case t, in each case 3H), 1.22-1.29 (m, 6H), 4.20 (mc, 2H), 4.92 (mc, 2H) I-c-82 C₂H₅ C₂H₅ C₂H₅ O═ O C₂H₅ 103 I-c-83 C₂H₅ C₂H₅ C₂H₅ —O—(CH₂)₃—O— O C₂H₅ 122 I-c-84 C₂H₅ C₂H₅ C₂H₅ —O—(CH₂)₂—O— O C₂H₅ 98 I-c-85 C₂H₅ CH₃ H —O—(CH₂)₃—O— O C₂H₅ I-c-86 C₂H₅ CH₃ H —O—(CH₂)₃—O— O CH₃ 1-c-87 C₂H₅ CH₃ H —O—(CH₂)₄—O— O C₂H₅ I-c-88 C₂H₅ CH₃ H —O—(CH₂)₂—O— O C₂H₅ I-c-89 C₂H₅ CH₃ C₂H₅ —O—CH(CH₃)—CH₂—CH₂—O— O C₂H₅ 1.05-1.28 (m, 12H), 2.32 (s, 3H), 3.70- syn/anti mixture 4.05 (m, 4H), 4.18 (mc, 2H) +(I-c) Ex. M.p. [° C.] or ¹H-NMR (400 MHz, No. X Y Z A B M R2 CDCL₃, δ in ppm) Isomerism I-c-2 C₂H₅ CH₃ C₂H₅ —O—C(CH₃)₂—O—CH₂— O i-C₃H₇ δ = 1.21 (d, 6 H), 1.88 and 1.98 (in anti each case mc, in each case 1 H), 3.89 (dd. 2 H), 4.81 (quint, 1H) I-c-3 C₂H₅ CH₃ C₂H₅ —O—C(CH₃)₂—O—CH₂— O i-C₃H₇ δ =1.32 (d, 6 H), 1.92-2.13 (m, 3 H), syn 3.92 (dd, 2H), 4.83 (quint, 1 H) I-c-4 C₂H₅ CH₃ C₂H₅ —O—C(CH₃)₂—O—CH₂— O CH₃ 132-133 syn I-c-5 C₂H₅ CH₃ C₂H₅ ═CH₂ O C₂H₅ δ = 1.26 (t, 3H), 3.20 (mc, 1 H), 4.02 (mc, 1H), 4.82 (mc, 2 H). I-c-6 C₂H₅ CH₃ C₂H₅ —O—CH₂—C(CH₃)₂—CH₂—O— O C₂H₅ 125-126 I-c-7 C₂H₅ CH₃ C₂H₅ —O—(CH₂)₂—O— O C₂H₅ δ = 1.08, 1.09 and 1.22 (in each case t, in each case 3H), 3.20 (mc, 1 H), 3.89 (mc, 4 H), 4.04 (mc, 1H), 4.19 (mc, 2H) I-c-8 C₂H₅ CH₃ C₂H₅ —O—CHCH₃—CH₂—O— O C₂H₅ 79 syn/anti mixture I-c-9 C₂H₅ CH₃ C₂H₅ —O—(CH₂)₄—O— O C₂H₅ 113 I-c-10 C₂H₅ CH₃ C₂H₅ —O—CHCH₃—CH₂—CHCH₃—O— O C₂H₅ δ = 1.02-1.28 (m, 15 H), 3.20 (mc, Isomer mixture with 1H), 3.80-4.01 (m, 3 H), 4.18 (mc, CH₃-groups at the acetal 2H) ring I-c-11 C₂H₅ CH₃ C₂H₅ —O—CH₂—CH═CH—CH₂—O— O C₂H₅ δ = 1.08. 1.12 and 1.24 (in each case t, in each case 3 H), 4.01-4.32 (m, 7 H), 5.65 (s, 2 H) I-c-12 C₂H₅ CH₃ C₂H₅ O C₂H₅ 119 I-c-13 C₂H₅ CH₃ C₂H₅ —O—CH(CH₃)—CH₂—CH(CH₃)O— O C₂H₅ δ = 1.05-1.25 (m, 15H), 3.20 and 3.92 (R,R)-Configuration of (in each case mc, in each case 1 H), the CH₃-groups at the 4.00 (mc, 2H), 4.18 (mc, 2 H) acetal ring I-c-14 C₂H₅ CH₃ C₂H₅ —O—(CH₂)₃—O— O C₂H₅ 110 I-c-15 C₂H₅ CH₃ C₂H₅ —S—(CH₂)₂—S— O C₂H₅ δ = 1.25 (t, 3H), 2.61 (mc, 2H), 3.35 (mc, 5H), 4.19 (mc, 3H), 6.90 (s, 2H) I-c-16 C₂H₅ CH₃ C₂H₅ —S—(CH₂)₃—S— O C₂H₅ δ = 1.26 (t, 3H), 2.83 (mc, 3H), 2.93 (mc, 3H), 4.20 (mc, 2H), 4.22 (mc, 1H) I-c-17 C₂H₅ CH₃ C₂H₅ ═O O C₂H₅ 1.26 (t, 3 H), 3.51 and 4.31 (in each case mc, in each case 1 H), 4.20 (mc, 2H) I-c-18 C₂H₅ CH₃ C₂H₅ —O—C(CH₃)₂—C(CH₃)₂—O— O C₂H₅ 116-117 I-c-19 C₂H₅ CH₃ C₂H₅ —S—(CH₂)₃—O— O C₂H₅ 1.24 (t, 3H), 3.00 (mc, 2H), 3.72 (mc, syn/anti mixture 2H), 4.18 (mc, 1H) I-c-20 C₂H₅ CH₃ C₂H₅ —S—(CH₂)₃—S— O CH₃ 2.62 (mc, 1H), 2.80-2.98 (m, 5H), 3.49 (mc, 1H), 3.78 (s, 1H), 4.23 (mc, 1H) I-c-21 C₂H₅ CH₃ C₂H₅ —O—CH₂—CH═CH—CH₂—O— O CH₃ 1.08 and 1.12 (in each case t, in each case 3H), 3.75 (s, 3H), 4.02 (mc, 2H), 4.16-4.32 (m, 3H), 5.67 (s, 2H) I-c-22 C₂H₅ CH₃ C₂H₅ —O—CH₂—C(CH₃)₂—CH₂—O— O C₂H₅ 98-99 I-c-23 C₂H₅ CH₃ C₂H₅ —O—CH₂—C(CH₃)₂—CH₂—O— O CH₃ 126-127 I-c-24 C₂H₅ CH₃ C₂H₅ —O—CH(CH₃)—CH₂—CH(CH₃)O— O CH₃ 1.03-1.22 (m, 12H), 3.20 (mc, 1H), (R,R)-Configuration of 3.75 (doubled singlet, Σ 3H), 3.90- the CH₃-groups at the 4.02 (m, 3H) acetal ring I-c-25 C₂H₅ CH₃ C₂H₅ —O—CH₂—C(OCH₃)—CH₂—O— O C₂H₅ 1.23 (t, 3H), 2.70-2.79 (m, 1H), 4.19 (mc, 2H), 3.10 (mc, 1H), 3.38 (s, 3H), I-c-26 C₂H₅ CH₃ C₂H₅ —O—CH₂CH(OC₂H₅)CH₂—O— O C₂H₅ 1.03-1.28 (m, 12H); 3.20 and 3.39 (in each case mc, in each case 1H), 3.95- 4.05 (m, 2H), 4.11-4.25 (m, 3H) I-c-27 C₂H₅ CH₃ C₂H₅ —OCH₂—CH(OC₂H₅)CH₂—O— O CH₃ 1.00-1.21 (m, 12H), 3.76 (s, 3H), 3.92- 4.05 (m, 2H), 4.21 (mc, 1H) I-c-28 C₂H₅ CH₃ C₂H₅ —OCH₂—CH(OCH₂C₆H₅)CH₂—O— O C₂H₅ 1.22 (t, 3H), 2.95 (mc, 2H), 4.15 (mc, 2H), 4.55 (s, 2H), 6.88 and 6.91 (in each case s, in each case 1H), 7.27- 7.85 (m, 5H) I-c-29 C₂H₅ CH₃ C₂H₅ —OCH₂—CH(OCH₂C₆H₅)CH₂—O— O CH₃ 2.05-2.12 (m, 2H), 3.41 (mc, 1H), 3.74 (s, 3H), 4.60 (s, 2H), I-c-30 C₂H₅ CH₃ C₂H₅ —S—(CH₂)₂—O— O C₂H₅ 94-95 syn/anti mixture I-c-31 C₂H₅ CH₃ C₂H₅ —S—(CH₂)₂—O— O CH₃ 3.00 (mc, 2H), 3.77 (s, 3H), 3.96 (mc, syn/anti mixture 1H), 4.02-4.10 (m, 2H) I-c-32 C₂H₅ CH₃ C₂H₅ —S—(CH₂)₃—O— O CH₃ 2.95-3.05 (m, 2H), 3.25 (mc, 1H), 3.72 syn/anti mixture (mc, 2H), 3.75 (s, 3H), 4.08 (mc, 1H) I-c-33 C₂H₅ CH₃ C₂H₅ —S—(CH₂)₂—O— O i-C₃H₇ 1.23 (6H), 3.01 (mc, 2H), 4.08 (mc, syn/anti mixture 2H), 4.81 (mc, 1H) I-c-34 C₂H₅ CH₃ C₂H₅ —O—(CH₂)₃—O— O i-C₃H₇ 1.22 (mc, 6H), 3.18 (mc, 1H), 3.79 (mc, 2H), 3.88 (mc, 2H), 3.99, (mc, 1H), 4.81 (hept, 1H) I-c-35 C₂H₅ CH₃ C₂H₅ —O—(CH₂)₃—O— O CH₃ 2.00-2.12 (m, 2H), 3.19 (mc, 1H), 3.73 (s, 3H), 3.88 (mc, 1H), 3.99 (mc, 1H) I-c-36 C₂H₅ CH₃ C₂H₅ —O—(CH₂)₂—O— O i-C₃H₇ 110-111 I-c-37 C₂H₅ CH₃ C₂H₅ —O—(CH₂)₂—O— O CH₃ 116 I-c-38 C₂H₅ CH₃ C₂H₅ ═NOtC₄H₉ O C₂H₅ 1.24 (s, 9H), 1.28 (mc, 3H), 2.62-2.90 (m, 4H), 4.15 (mc, 1H), 4.20 (mc, 2H) I-c-39 C₂H₅ CH₃ C₂H₅ ═NOi-C₃H₇ O C₂H₅ 1.03 (mc, 6H), 1.15-1.30 (m, 9H), 2.35 (hept, 2H), 3.32 (mc, 1H), 4.20 (mc, 2H), 4.25 (mc, 1H) I-c-39 C₂H₅ CH₃ C₂H₅ ═NOCyclopentyl O C₂H₅ 1.52-1.80 (m, 8H), 3.30 (mc, 1H), 4.12-4.22 (m, 3H), 4.62 (mc, 1H) I-c-40 C₂H₅ CH₃ C₂H₅ ═NOCH₂-cyclopropyl O C₂H₅ 0.23 and 0.51 (in each case mc, in each case 2H), 3.19-3.49 (m, 1H), 3.82 (mc, 2H), 4.12-4.24 (m, 3H) I-c-41 C₂H₅ CH₃ C₂H₅ ═NOCH₂C≡CH O C₂H₅ 1.25 (mc, 3H), 2.47 (mc, 1H), 4.15- 4.25 (m, 3H), 4.61 (d, 2H) I-c-42 C₂H₅ CH₃ C₂H₅ O═ O CH₃ 2.60-2.85 (m, 3H), 3.51 (mc, 1H), 3.79 (s, 3H), 4.30 (mc, 1H) I-c-43 C₂H₅ CH₃ C₂H₅ ═NOCH(CH₃)—C≡CH O C₂H₅ 1.28 (t, 3H), 1.48 (mc, 3H), 2.43 (mc, 1H), 3.34 (mc, 1H), 4.80 (mc, 1H) I-c-44 CH₃ CH₃ CH₃ —O—(CH₂)₂—O— O C₂H₅ 1.21 (t, 3H), 2.05, 2.12 and 2.22 (in each case s, in each case 3H), 3.19 (mc, 1H), 3.89 (mc, 4H), 3.91 (mc, 1H), 4.12 (mc, 2H) I-c-45 CH₃ CH₃ CH₃ CH₂═ O C₂H₅ 1.21 (t, 3H), 1.98, 2.06 and 2.24 (in each case s, in each case 3H), 4.15 (mc, 2H), 4.90 (mc, 2H) I-c-46 CH₃ CH₃ CH₃ CH₂═ O CH₃ 1.99, 2.05 and 2.22 (in each case s, in each case 3H), 2.40-2.72 (m, 4H), 3.72 (s, 3H), 4.90 (mc, 2H) I-c-47 CH₃ CH₃ CH₃ —O—(CH₂)₃—O— O C₂H₅ 1.20 (t, 3H), 2.80 (mc, 1H), 3.16 (mc, 1H), 3.70-3.90 (m, 5H), 4.12 (mc, 2H) I-c-48 CH₃ CH₃ CH₃ —O—CH₂—CH(CH₃)—CH₂—O O C₂H₅ 0.71 and 0.82 (in each case d, Σ 3H), 2.45-2.50 (m, 1H), 3.28-3.45 (m, 2H), 4.12 (mc, 2H) I-c-49 CH₃ CH₃ CH₃ —O—(CH₂)₄—O— O CH₃ 122 I-c-50 CH₃ CH₃ CH₃ —O—(CH₂)₄—O— O C₂H₅ 131 I-c-51 CH₃ CH₃ CH₃ —O—CH₂—CH═CH—CH₂—O— O C₂H₅ 1.21 (t, 3H), 4.00-4.31 (m, 6H), 5.65 (mc, 2H), 6.85 (mc, 2H) I-c-52 CH₃ CH₃ CH₃ —O—CH₂—CH═CH—CH₂—O— O CH₃ 3.70 (s, 3H), 3.94 (mc, 1H), 3.96-4.30 (m, 4H), 5.64 (mc, 2H) I-c-53 CH₃ CH₃ CH₃ —O—CH₂—CH(CH₃)—CH₂—O— O CH₃ 0.70 and 0.82 (in each case t, Σ 3H), 2.48 (mc, 1H), 3.15 (mc, 1H), 3.29- 3.45 (m, 2H), 3.71 (s, 3H) I-c-54 CH₃ CH₃ CH₃ —O—C(CH₃)₂—O—CH₂— O C₂H₅ 1.20 (t, 3H), 1.48 (s, 6H), 3.34 (mc, syn/anti mixture 1H), 3.85 and 3.89 (in each case d, in each case 1H), 4.12 (q, 2H) I-c-55 CH₃ CH₃ CH₃ —O—C(CH₃)₂—O—CH₂— O i-C₃H₇ 1.15 and 1.19 (in each case d, Σ 6H), syn/anti mixture 1.38 (s, 6H), 3.85 and 3.90 (in each case d, in each case 1H), 4.72 (hept, 1H) I-c-56 CH₃ CH₃ CH₃ —O—C(CH₃)₂—O—CH₂— O CH₃ 1.38 (s, 6H), 3.36 (mc, 1H), 3.71 (s, syn/anti mixture 3H), 4.00 (mc, 1H) I-c-57 CH₃ CH₃ CH₃ ═O O CH₃ 1.98, 2.10 and 2.26 (in each case s, in each case 3H), 3.50 (mc, 1H), 3.75 (s, 3H), 4.19 (mc, 1H) I-c-58 CH₃ CH₃ CH₃ ═O O C₂H₅ 1.22 (t, 3H), 2.00, 2.11 and 2.26 (in each case s, in each case 3H), 2.42- 2.85 (m, 4H), 4.10-4.22 (3H), I-c-59 CH₃ CH₃ CH₃ ═NOCH₂C≡CH O CH₃ 2.45 (mc, 1H), 2.70-3.08 (m, 4H), 3.75 (s, 3H), 4.61 (mc, 2H), I-c-60 CH₃ CH₃ CH₃ ═NOCH₂C≡CH O C₂H₅ 1.23 (mc, 3H), 2.44 (mc, 1H), 2.70- 3.08 (m, 4H), 4.18 (mc, 2H), 4.61 (mc, 2H) I-c-61 OCH₃ CH₃ CH₃ CH₂═ O C₂H₅ 2.30 (s, 3H), 2.39-2.70 (m, 4H), 3.70 (s, 3H), 4.18 (mc, 2H) I-c-62 OCH₃ CH₃ CH₃ CH₂═ O CH₃ 2.38-2.70 (m, 4H), 3.70 and 3.75 (in each case s, in each case 1H), 4.89 (mc, 2H) I-c-62 OCH₃ CH₃ CH₃ O═ O C₂H₅ 1.25 (mc, 3H), 2.30(s, 3H), 2.40-2.85 (m, 4H), 3.50 (mc, 1H), 4.10 (mc, 2H) I-c-63 OCH₃ CH₃ CH₃ —O—(CH₂)₃—O— O C₂H₅ 1.22 (t, 3H), 2.09 and 2.29 (in each case s, in each case 3H), 2.76-2.80 (m, 1H), 3.18 (mc, 1H), 3.70 (s, 3H) I-c-64 OCH₃ CH₃ CH₃ —O—(CH₂)₂—O— O C₂H₅ 21.12 and 2.30 (in each case s, in each case 3H), 3.70 (s, 3H), 3.85 (mc, 4H), 4.18 (q, 2H) I-c-65 OCH₃ CH₃ CH₃ —O—CH₂—CH═CH—CH₂—O— O C₂H₅ 1.25 (t, 3H), 3.70 (s, 3H), 3.86-430 (m, 7H), 5.64 (mc, 2H) I-c-66 OCH₃ CH₃ CH₃ —O—(CH₂)₄—O— O C₂H₅ 1.22 (t, 3H), 2.22 and 2.30 (in each case s, in each case 3H), 3.12 (mc, 1H), 3.70 (s, 3H), 4.16 (q, 2H) I-c-67 OCH₃ CH₃ C₂H₅ CH₂═ O C₂H₅ 0.99 and 1.08 (in each case t, Σ 3H), 1.26 (mc, 3H), 3.61 and 3.68 (in each case s, Σ 3H), 4.89 (mc, 2H). I-c-68 OCH₃ CH₃ C₂H₅ O═ O C₂H₅ 1.05 and 1.11 (in each case t, Σ 3H), 1.26 (mc, 3H), 2.25-2.83 (m, 4H), 2.30 (s, 3H), 4.20 (mc, 2H) I-c-69 OCH₃ CH₃ C₂H₅ —O—(CH₂)₂—O— O C₂H₅ 1.09 (t, 3H), 1.24 (t, 3H), 2.31 (s, 3H), 3.70 (s, 3H), 3.88 (mc, 4H), 4.19 (q, 2H) I-c-70 OCH₃ CH₃ C₂H₅ —O—(CH₂)₃—O— O C₂H₅ 1.10 and 1.15 (in each case t, in each case 3H), 3.70 (s, 3H), 3.70-4.00 (m, 4H), 4.18 (q, 2H) I-c-71 C₂H₅ CH₃ CH₃ —O—(CH₂)₂—O— O C₂H₅ 1.08 and 1.22 (in each case t, in each case 3H), 3.88 (mc, 4H), 3.96 (mc, 1H), 4.15 (mc, 2H) I-c-72 C₂H₅ CH₃ CH₃ —O—CH₂—CH═CH—CH₂—O— O C₂H₅ 124-125 I-c-73 C₂H₅ CH₃ CH₃ —O—CH₂—CH═CH—CH₂—O— O CH₃ 1.08 (t, 3H), 3.18 (mc, 1H), 3.71 (s, 3H), 3.95-4.31 (m, 5H), 5.65 (mc, 2H) I-c-74 C₂H₅ CH₃ CH₃ —O—CH₂—CH═CH—CH₂—O— O i-C₃H₇ 1.20 (mc, 6H), 3.91-4.32 (m, 5H), 4.79 (hept, 1H), 5.64 (mc, 2H) I-c-75 C₂H₅ CH₃ CH₃ —O—(CH₂)₄—O— O C₂H₅ 102 I-c-76 C₂H₅ CH₃ CH₃ CH₂═ O C₂H₅ 0.96 and 1.08 (in each case t, Σ 3H), 1.22 (t, 3H), 2.26 (s, 3H), 4.15 (mc, 2H), 4.91 (mc, 1H) I-c-77 C₂H₅ CH₃ CH₃ CH₂═ O CH₃ 0.97 and 1.05 (in each case t, Σ 3H), 3.75 (s, 3H), 4.92 (mc, 2H) I-c-78 C₂H₅ CH₃ CH₃ —O—C(CH₃)₂—O—CH₂— O i-C₃H₇ 116-117 syn/anti mixture I-c-79 C₂H₅ CH₃ CH₃ —O—C(CH₃)₂—O—CH₂— O CH₃ 1.02-1.11 (m, 3H), 1.38 (s, 6H), 3.74 syn/anti mixture (s, 3H), 3.85 and 3.88 (in each case d, in each case 1H) I-c-80 C₂H₅ CH₃ CH₃ —O—C(CH₃)₂—O—CH₂— O C₂H₅ 1.20 (t, 3H), 1.38 (s, 6H), 2.30 (s, 3H), syn/anti mixture 3.86 and 4.00 (in each case d, in each case 1H), 4.15 (mc, 2H) I-c-81 C₂H₅ C₂H₅ C₂H₅ CH₂═ O C₂H₅ 0.98 and 1.07 (in each case t, in each case 3H), 1.22-1.29 (m, 6H), 4.20 (mc, 2H), 4.92 (mc, 2H) I-c-82 C₂H₅ C₂H₅ C₂H₅ O═ O C₂H₅ 103 I-c-83 C₂H₅ C₂H₅ C₂H₅ —O—(CH₂)₃—O— O C₂H₅ 122 I-c-84 C₂H₅ C₂H₅ C₂H₅ —O—(CH₂)₂—O— O C₂H₅ 98 I-c-85 C₂H₅ CH₃ H —O—(CH₂)₃—O— O C₂H₅ I-c-86 C₂H₅ CH₃ H —O—(CH₂)₃—O— O CH₃ 1-c-87 C₂H₅ CH₃ H —O—(CH₂)₄—O— O C₂H₅ I-c-88 C₂H₅ CH₃ H —O—(CH₂)₂—O— O C₂H₅ I-c-89 C₂H₅ CH₃ C₂H₅ —O—CH(CH₃)—CH₂—CH₂—O— O C₂H₅ 1.05-1.28 (m, 12H), 2.32 (s, 3H), 3.70- syn/anti mixture 4.05 (m, 4H), 4.18 (mc, 2H) ### Example I-d-1 diff --git a/crates/fleischwolf/tests/data/uspto/expected/ipg07997973.xml.strict.md b/crates/fleischwolf/tests/data/uspto/expected/ipg07997973.xml.strict.md index e92e1766..7f5c7aed 100644 --- a/crates/fleischwolf/tests/data/uspto/expected/ipg07997973.xml.strict.md +++ b/crates/fleischwolf/tests/data/uspto/expected/ipg07997973.xml.strict.md @@ -70,7 +70,7 @@ The term “e.g.” and like terms mean “for example”, and thus does not lim The term “i.e.” and like terms mean “that is”, and thus limits the term or phrase it explains. For example, in the sentence “the computer sends data (i.e., instructions) over the Internet”, the term “i.e.” explains that “instructions” are the “data” that the computer sends over the Internet. -Any given numerical range shall include whole and fractions of numbers within the range. For example, the range “1 to 10” shall be interpreted to specifically include whole numbers between 1 and 10 (e.g., 1, 2, 3, 4, . . . 9) and non-whole numbers (e.g., 1.1, 1.2, . . . 1.9). +Any given numerical range shall include whole and fractions of numbers within the range. For example, the range “1 to 10” shall be interpreted to specifically include whole numbers between 1 and 10 (e.g., 1, 2, 3, 4,... 9) and non-whole numbers (e.g., 1.1, 1.2,... 1.9). ### II. Determining diff --git a/crates/fleischwolf/tests/data/webvtt/expected/webvtt_example_02.vtt.strict.md b/crates/fleischwolf/tests/data/webvtt/expected/webvtt_example_02.vtt.strict.md index 14117d7b..fbd874f5 100644 --- a/crates/fleischwolf/tests/data/webvtt/expected/webvtt_example_02.vtt.strict.md +++ b/crates/fleischwolf/tests/data/webvtt/expected/webvtt_example_02.vtt.strict.md @@ -6,4 +6,4 @@ Hee! *laughter* That’s awesome! -Sur les *playground* , ici à Montpellier +Sur les *playground*, ici à Montpellier diff --git a/crates/fleischwolf/tests/data/xbrl/expected/mlac-20251231.xml.strict.md b/crates/fleischwolf/tests/data/xbrl/expected/mlac-20251231.xml.strict.md index 37557e73..127afdab 100644 --- a/crates/fleischwolf/tests/data/xbrl/expected/mlac-20251231.xml.strict.md +++ b/crates/fleischwolf/tests/data/xbrl/expected/mlac-20251231.xml.strict.md @@ -436,7 +436,7 @@ NOTE 9 - SEGMENT INFORMATION ASC Topic 280, "Segment Reporting," establishes standards for companies to report in their financial statement information about operating segments, products, services, geographic areas, and major customers. Operating segments are defined as components of an enterprise for which separate financial information is available that is regularly evaluated by the Company's chief operating officer decision maker ("CODM"), or group, in deciding how to allocate resources and assess performance. -The Company's CODM has been identified as the Chief Executive Officer , who reviews the operating results for the Company as a whole to make decisions about allocating resources and assessing financial performance. Accordingly, management has determined that the Company only has one reportable segment. +The Company's CODM has been identified as the Chief Executive Officer, who reviews the operating results for the Company as a whole to make decisions about allocating resources and assessing financial performance. Accordingly, management has determined that the Company only has one reportable segment. The CODM assesses performance for the single segment and decides how to allocate resources based on net income that also is reported on the statements of operations as net income. The measure of segment assets is reported on the balance sheets as total assets. When evaluating the Company's performance and making key decisions regarding resource allocation, the CODM reviews several key metrics, which include the following: @@ -474,4 +474,4 @@ NOTE 10 - SUBSEQUENT EVENTS The Company evaluated subsequent events and transactions that occurred after the balance sheet date up to the date that the financial statements were issued. Based upon this review, the Company did not identify any subsequent events that would have required adjustment or disclosure in the financial statements. -On January 13, 2026, Mountain Lake Acquisition Corp. entered into Amendment No. 1 to the Business Combination Agreement ( the "Business Combination Agreement Amendment"), effective as of October 1, 2025, which, among other things, added Astral Horizon, L.P. and certain Seller affiliates as parties to the agreement, modified the allocation and form of merger consideration, revised the parties making seller representations and warranties, and replaced Exhibit E to the Business Combination Agreement. +On January 13, 2026, Mountain Lake Acquisition Corp. entered into Amendment No. 1 to the Business Combination Agreement (the "Business Combination Agreement Amendment"), effective as of October 1, 2025, which, among other things, added Astral Horizon, L.P. and certain Seller affiliates as parties to the agreement, modified the allocation and form of merger consideration, revised the parties making seller representations and warranties, and replaced Exhibit E to the Business Combination Agreement. From 54031da7b5e8366c035451962d4cc974ff80cedc Mon Sep 17 00:00:00 2001 From: artiz Date: Sun, 28 Jun 2026 12:44:21 +0200 Subject: [PATCH 04/42] docs(pdf): add PDF conformance roadmap + groundtruth measurement script MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit PDF_CONFORMANCE.md records the current per-PDF byte-conformance vs the docling groundtruth (1/14 exact after this PR) and scopes the remaining blockers: - Text-stream extraction — why raw chars / GetBoundedText both fail, and that the real fix needs an FPDFText_GetText per-char-range binding that pdfium-render 0.8.37 doesn't expose (upstream/FFI work). - TableFormer — weights, the autoregressive OTSL decoding loop, cell matching, span-aware serialization (six table PDFs blocked on it). - RTL/bidi, duplicate glyphs, code fencing. scripts/pdf_groundtruth.sh measures it (no docling install needed). Co-Authored-By: Claude Opus 4.8 (1M context) --- PDF_CONFORMANCE.md | 144 +++++++++++++++++++++++++++++++++++++ scripts/pdf_groundtruth.sh | 42 +++++++++++ 2 files changed, 186 insertions(+) create mode 100644 PDF_CONFORMANCE.md create mode 100755 scripts/pdf_groundtruth.sh diff --git a/PDF_CONFORMANCE.md b/PDF_CONFORMANCE.md new file mode 100644 index 00000000..788518a7 --- /dev/null +++ b/PDF_CONFORMANCE.md @@ -0,0 +1,144 @@ +# PDF conformance roadmap + +How close the Rust PDF pipeline gets to docling's **default** Markdown, measured +byte-for-byte against the committed groundtruth (`tests/data/pdf/groundtruth/*.md`), +and what it would take to close the remaining gap. + +> Measure locally with `scripts/pdf_groundtruth.sh` (no docling install needed — +> it diffs against the checked-in reference). The numbers below are the current +> state. + +## Current state + +**1 / 14 groundtruth PDFs are byte-for-byte exact** (`picture_classification`); +the rest are blocked on one of the categories below. Diff = changed lines vs the +groundtruth (one changed line counts as 2). + +| PDF | diff | dominant blocker | +|---|---:|---| +| picture_classification | **exact** | — | +| right_to_left_01 | 4 | RTL/bidi | +| code_and_formula | 6 | inter-run spacing + code fencing | +| right_to_left_02 | 8 | RTL/bidi | +| amt_handbook_sample | 12 | double-spaces, duplicate glyphs, fractions | +| 2305.03393v1-pg9 | 25 | table structure | +| right_to_left_03 | 74 | RTL/bidi | +| multi_page | 76 | inter-run spacing + line-wrap hyphens | +| normal_4pages | 108 | reading order (CJK) | +| 2305.03393v1 | 152 | table structure | +| table_mislabeled_as_picture | 151 | table structure | +| 2203.01017v2 | 346 | table structure (+ inter-run spacing) | +| 2206.01062 | 321 | table structure | +| redp5110_sampled | 342 | table structure | + +Shipped in this PR (no regressions; `pdf_conformance` stays 76/76): +de-hyphenation + typography normalization, ``, +caption-before-image pairing, and (strict-mode only) punctuation tightening. + +Reaching ~50% exact requires the two big items below: **text-stream extraction** +(unlocks the spacing-bound PDFs) and **TableFormer** (unlocks the six +table-bound PDFs). + +--- + +## Blocker 1 — inter-run text spacing (a.k.a. "text-stream extraction") + +**Symptom.** pdfium splits a visual line into multiple style *segments* (a +citation's superscripts, a code line's tokens, mixed fonts). We emit one cell +per segment and join them with single spaces, so the real inter-run spacing is +lost: `[ 37 , 36 ]` instead of `[37, 36]`, `function add ( a , b )` instead of +`function add(a, b)`. docling reads text via pypdfium2's `get_text_range` +(`FPDFText_GetText`), which inserts spaces from each glyph's *advance* and so +reproduces the PDF's real spacing. + +**What was tried in this PR and why each failed** (all reverted): + +1. **Raw char API** (`PdfPageText::chars()` → `unicode_char()` + `loose_bounds()`, + concatenated per line). pdfium's per-char list is *unreliable*: some lines + come back with no space characters at all (`Thiscontentisextremelyvaluablefor`) + and the char order is occasionally scrambled. Net regression. +2. **`inside_rect()`** (`FPDFText_GetBoundedText`) over a whole line's bounding + box. `GetBoundedText` ≠ `GetText`: it *drops* inter-run spaces on + multi-segment lines (`{ahn,nli,mly,taa}@zurich` vs docling's + `{ ahn,nli,mly,taa } @zurich`) and *bleeds* glyphs from vertically adjacent + lines (`nevertheless exLines of different…`). Net regression. +3. **Hybrid** (segment text for single-segment lines, `inside_rect` only for + multi-segment lines). Same `GetBoundedText` divergence on exactly the lines + that need fixing. + +**Root cause / the real fix.** `segment.text()` is itself `inside_rect(segment. +bounds())` — i.e. the *only* reliable text unit pdfium-render exposes is a single +style run. What docling uses, `FPDFText_GetText(textpage, start_index, count, …)` +for an arbitrary **character range**, is *not* wrapped by `pdfium-render` +0.8.37. The path forward is to get that call: + +- add a thin binding for `FPDFText_GetText` over a char range (upstream PR to + `pdfium-render`, or call it through the crate's `PdfiumLibraryBindings` handle + directly), then +- group segments into lines (by vertical band, splitting at column gutters — the + clustering already prototyped in this PR), map each line to its `[start, count]` + char range, and read the whole line with `GetText`. + +This is the single highest-leverage change for default-mode conformance: it +fixes citations, inline code, fractions, and the justified-text double spaces, +and unblocks `multi_page` and `code_and_formula` (the latter also needs code +regions rendered as fenced blocks). **Stopgap shipped:** `--strict` tightens the +citation/parenthetical spacing at serialization time, so strict Markdown already +reads cleanly even though default mode still mirrors the segment spacing. + +Also needed alongside it: +- **Line-wrap de-hyphenation for real hyphens.** We already drop the U+0002 soft + hyphen; `multi_page` wraps words with a real `-` (`professi-`/`onal`), which + needs line-end-hyphen detection during the line join. +- **Double-space preservation.** docling keeps the PDF's wide justified spacing + (`the stainless steel nuts`); `clean_text` currently collapses runs of + whitespace. With `GetText` per line, stop collapsing intra-line spacing. + +## Blocker 2 — table structure (TableFormer) + +**Symptom.** Six PDFs (`2206.01062`, `2305.03393v1[-pg9]`, `redp5110_sampled`, +`table_mislabeled_as_picture`, and the table on `2203.01017v2`) are dominated by +table differences. We reconstruct grids *geometrically* (cluster cells into +rows/columns); docling runs **TableFormer**, an autoregressive transformer that +predicts the table structure as an OTSL/HTML tag sequence plus per-cell bounding +boxes, which recovers spanning headers and merged cells we cannot. + +**Scope of a port** (large — own PR, likely staged over several): + +1. **Weights.** TableFormer ships in `docling-ibm-models` (`TableModel04_rs`, + "accurate"/"fast" variants). Export the encoder + the two decoders to ONNX + from the published checkpoint; confirm the license permits redistribution of + a converted model. +2. **Inference loop.** Unlike the layout/OCR models (single `Session::run`), + TableFormer is **autoregressive**: encode the table-crop image once, then step + the structure decoder to emit OTSL tokens until ``, feeding each token + back in. The cell-bbox decoder runs per predicted cell. This is a real + decoding loop in `fleischwolf-pdf`, not a one-shot call — budget for KV-cache + handling and a token vocabulary/OTSL grammar. +3. **Cell content.** Map predicted cell bounding boxes back onto the PDF text + cells (we have these) to fill cell text — the same matching docling does for + "PDF" tables (it does not OCR programmatic tables). +4. **Serialization.** Convert the predicted OTSL grid (with row/col spans) to the + `Table` node; the Markdown table serializer already exists but assumes a plain + grid, so spans need representing. + +A cheaper interim improvement (not docling-exact, but closes some diff): better +geometric reconstruction — detect header rows, merge obvious spanning cells, and +handle the multi-line header cells that currently shatter into many columns. + +## Blocker 3 — RTL / bidi (Arabic) + +`right_to_left_01/02/03`. Two compounding issues: (a) reading order — Latin runs +embedded in RTL text and the overall right-to-left flow are emitted left-to-right +(`Python و ة R` vs `R و Python`); we'd need Unicode bidi reordering of each line. +(b) Arabic shaping — pdfium returns presentation-form / decomposed sequences that +differ from docling's (`اإل` vs `الإ`), needing NFC-ish normalization of the +Arabic block. Both are self-contained but specialized; lower priority than 1–2. + +## Smaller items + +- **Duplicate glyphs** (`amt_handbook`: `T he`, `F Figure 7-26 6`). pdfium emits + doubled glyphs for some bold/overlapping text; needs de-duplication of + overlapping cells. +- **Code regions** → fenced ```` ``` ```` blocks with the caption *after* (code + captions trail; figure captions lead). Pairs with Blocker 1 for the code text. diff --git a/scripts/pdf_groundtruth.sh b/scripts/pdf_groundtruth.sh new file mode 100755 index 00000000..5f9ce6cb --- /dev/null +++ b/scripts/pdf_groundtruth.sh @@ -0,0 +1,42 @@ +#!/usr/bin/env bash +# +# Per-PDF byte-conformance of the Rust pipeline vs the committed docling +# groundtruth (tests/data/pdf/groundtruth/*.md). Unlike conformance.sh this needs +# no docling install — it diffs against the checked-in reference. Use it to track +# how many groundtruth PDFs are byte-for-byte exact (see PDF_CONFORMANCE.md). +# +# Usage: scripts/pdf_groundtruth.sh + +set -euo pipefail +cd "$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)/.." + +export PDFIUM_DYNAMIC_LIB_PATH="${PDFIUM_DYNAMIC_LIB_PATH:-$(pwd)/.pdfium/lib}" +export DOCLING_LAYOUT_ONNX="${DOCLING_LAYOUT_ONNX:-$(pwd)/models/layout_heron.onnx}" +export DOCLING_OCR_REC_ONNX="${DOCLING_OCR_REC_ONNX:-$(pwd)/models/ocr_rec.onnx}" +export DOCLING_OCR_DICT="${DOCLING_OCR_DICT:-$(pwd)/models/ppocr_keys_v1.txt}" + +cargo build --release --quiet -p fleischwolf-cli +BIN=./target/release/fleischwolf + +exact=0 +total=0 +printf "%-34s %12s\n" "PDF" "DIFF-LINES" +printf "%-34s %12s\n" "---" "----------" +for gt in tests/data/pdf/groundtruth/*.md; do + stem="$(basename "$gt" .md)" + src="tests/data/pdf/sources/$stem.pdf" + [[ -f "$src" ]] || continue + total=$((total + 1)) + out="$("$BIN" "$src" 2>/dev/null || echo '')" + # Compare trailing-newline-insensitively; one changed line counts as 2. + d="$(diff <(printf '%s' "$out") <(printf '%s' "$(cat "$gt")") | grep -cE '^[<>]' || true)" + if [[ "$d" -eq 0 ]]; then + exact=$((exact + 1)) + mark="EXACT" + else + mark="$d" + fi + printf "%-34s %12s\n" "$stem" "$mark" +done +echo +echo "Fully conformant: $exact / $total" From 898f526a199d3d7689bb2ea677c8286a5a9a263d Mon Sep 17 00:00:00 2001 From: artiz Date: Sun, 28 Jun 2026 18:19:43 +0200 Subject: [PATCH 05/42] docs(pdf): record FPDFText_GetText investigation (8 approaches, all reverted) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Updates the text-stream blocker with the full finding: FPDFText_GetText IS reachable via the public PdfiumLibraryBindings trait (a second raw-FFI handle on the same bytes drives GetText directly, no pdfium-render fork, stays publishable), and it reads citations correctly in isolation. But none of the eight reconstruction strategies tried (raw chars, gap-based, GetBoundedText, real GetText with char-detected or segment-defined lines, ± U+FFFE de-hyphenation, ± single-segment override) beats pdfium's native style segments on the aggregate — the hard part is reconstructing docling's exact line and character ranges (docling-parse territory), not the GetText call itself. Segment extraction stays in production; --strict remains the citation-spacing stopgap. Co-Authored-By: Claude Opus 4.8 (1M context) --- PDF_CONFORMANCE.md | 81 ++++++++++++++++++++++++++++------------------ 1 file changed, 49 insertions(+), 32 deletions(-) diff --git a/PDF_CONFORMANCE.md b/PDF_CONFORMANCE.md index 788518a7..3e32f916 100644 --- a/PDF_CONFORMANCE.md +++ b/PDF_CONFORMANCE.md @@ -51,40 +51,57 @@ lost: `[ 37 , 36 ]` instead of `[37, 36]`, `function add ( a , b )` instead of (`FPDFText_GetText`), which inserts spaces from each glyph's *advance* and so reproduces the PDF's real spacing. -**What was tried in this PR and why each failed** (all reverted): +**Eight approaches were tried in this PR; all regressed the aggregate and were +reverted.** The headline finding: real `FPDFText_GetText` **is reachable and +works in isolation**, but no whole-line reconstruction built on top of it beats +pdfium's native style segments. 1. **Raw char API** (`PdfPageText::chars()` → `unicode_char()` + `loose_bounds()`, - concatenated per line). pdfium's per-char list is *unreliable*: some lines - come back with no space characters at all (`Thiscontentisextremelyvaluablefor`) - and the char order is occasionally scrambled. Net regression. -2. **`inside_rect()`** (`FPDFText_GetBoundedText`) over a whole line's bounding - box. `GetBoundedText` ≠ `GetText`: it *drops* inter-run spaces on - multi-segment lines (`{ahn,nli,mly,taa}@zurich` vs docling's - `{ ahn,nli,mly,taa } @zurich`) and *bleeds* glyphs from vertically adjacent - lines (`nevertheless exLines of different…`). Net regression. -3. **Hybrid** (segment text for single-segment lines, `inside_rect` only for - multi-segment lines). Same `GetBoundedText` divergence on exactly the lines - that need fixing. - -**Root cause / the real fix.** `segment.text()` is itself `inside_rect(segment. -bounds())` — i.e. the *only* reliable text unit pdfium-render exposes is a single -style run. What docling uses, `FPDFText_GetText(textpage, start_index, count, …)` -for an arbitrary **character range**, is *not* wrapped by `pdfium-render` -0.8.37. The path forward is to get that call: - -- add a thin binding for `FPDFText_GetText` over a char range (upstream PR to - `pdfium-render`, or call it through the crate's `PdfiumLibraryBindings` handle - directly), then -- group segments into lines (by vertical band, splitting at column gutters — the - clustering already prototyped in this PR), map each line to its `[start, count]` - char range, and read the whole line with `GetText`. - -This is the single highest-leverage change for default-mode conformance: it -fixes citations, inline code, fractions, and the justified-text double spaces, -and unblocks `multi_page` and `code_and_formula` (the latter also needs code -regions rendered as fenced blocks). **Stopgap shipped:** `--strict` tightens the -citation/parenthetical spacing at serialization time, so strict Markdown already -reads cleanly even though default mode still mirrors the segment spacing. + concatenated per line). pdfium's per-char list is *unreliable*: some lines come + back with no space characters at all (`Thiscontentisextremelyvaluablefor`) and + the char order is occasionally scrambled. +2. **Raw char API + gap-based spacing** (drop pdfium's spaces, re-insert from + glyph gaps). Fixes code perfectly but garbles prose (band mis-sort merges + glyphs from adjacent lines: `valuablefor`, stray glyphs). +3. **`inside_rect()`** (`FPDFText_GetBoundedText`) over a whole line's bbox. + `GetBoundedText` ≠ `GetText`: it *drops* inter-run spaces on multi-segment + lines (`{ahn,nli,mly,taa}@zurich` vs docling's `{ ahn,nli,mly,taa } @zurich`) + and *bleeds* glyphs from vertically adjacent lines. +4. **`inside_rect` hybrid** (segment text for single-segment lines only). Same + `GetBoundedText` divergence on the lines that need fixing. +5. **Real `FPDFText_GetText`, char-detected lines.** Reached the raw call via the + public `PdfiumLibraryBindings` trait — `bindings()` on the `Pdfium`/`PdfPage` + exposes `FPDF_LoadMemDocument`/`FPDF_LoadPage`/`FPDFText_LoadPage`/`CountChars`/ + `GetCharBox`/`GetText`, so a *second raw-FFI handle on the same bytes* drives + `GetText` directly (no fork, stays publishable). **Citations read correctly in + isolation** (`[37, 36, 18, 20]`). But my char-box line detection diverges from + pdfium's line structure, and `GetText` inserts letter-tracking spaces into + display text (`Fi gures` for a tracked title) — net regression. +6. **+ U+FFFE de-hyphenation.** `GetText` encodes the wrap hyphen as **U+FFFE** + (not the segment path's U+0002); handling it recovered most prose, but the + title-tracking and line-detection issues remained. +7. **+ single-segment override** (replace a GetText line with segment text when + one segment covers it). Helped marginally; line boundaries still diverged. +8. **Segment-defined lines + `GetText` per multi-segment range** (group *segments* + into lines so the structure matches docling, `GetText` only the multi-segment + lines via a bbox→char-index range). Preserved the exact PDFs and improved + `multi_page`, but the bbox→range mapping mis-selects characters on dense + two-column pages, so citation lines on `2203`/`2206` came back wrong and + `normal_4pages` regressed (108→152). + +**Conclusion.** The blocker is *not* the missing binding — `GetText` is callable +and correct. It is that **reconstructing docling's exact line + character ranges +from glyph/segment geometry is itself the hard problem** (docling uses +`docling-parse`, a purpose-built PDF text reconstructor, not raw `GetText`). +pdfium's own style segments are a better-structured unit than anything rebuilt on +top of them here, so the segment path stays in production. A real fix needs a +faithful line/cell reconstructor (port `docling-parse`, or use pdfium's +`FPDFText_GetTextObject`/structured APIs to get true line boundaries before +`GetText`), not just the call this PR proved reachable. + +**Stopgap shipped:** `--strict` tightens the citation/parenthetical spacing at +serialization time, so strict Markdown reads cleanly even though default mode +mirrors the segment spacing. Also needed alongside it: - **Line-wrap de-hyphenation for real hyphens.** We already drop the U+0002 soft From b3b9d69ab872e4ea0cfd6fdff549819116e41cdf Mon Sep 17 00:00:00 2001 From: artiz Date: Sun, 28 Jun 2026 21:24:39 +0200 Subject: [PATCH 06/42] =?UTF-8?q?feat(pdf):=20docling-parse-grade=20text?= =?UTF-8?q?=20reconstruction=20(chars=E2=86=92words=E2=86=92lines)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Replaces the style-segment text path with a glyph-level reconstruction that mirrors docling's docling-parse, so default-mode spacing matches the groundtruth much more closely. Driven through the raw PdfiumLibraryBindings FFI on a second handle to the same bytes (FPDF_LoadMemDocument/LoadPage/FPDFText_LoadPage/ GetUnicode/GetCharBox) — pdfium-render's safe API only exposes whole style runs, which lose inter-run spacing; no fork, stays publishable. Segments remain the fallback when the raw text page can't be loaded. The grouping, derived empirically from docling-parse's own word/line cells: - words: a space glyph (pdfium emits them on most lines) pins the split; the fallback is a horizontal gap wider than ~0.25× the *line's* font height, so a small superscript citation isn't split at its tight digit gaps and a tracked display title isn't shattered (`Figures`, not `Fi gures`); - lines: baseline drop *and* leftward x-reset (a descending comma can't fake a break; a rising superscript stays on its line); - gluing: closing punctuation, opening punctuation, intra-number digits, and a period that runs into a digit/lowercase (`i.e.`, `98.5`) attach with no space. Plus: em/en-dash → `-`, and list bullets are stripped (the serializer adds `- `). Measured vs the docling groundtruth this beats the segment path on most PDFs (2203 346→341, 2206 321→317, 2305v1-pg9 25→19, multi_page 76→54) and keeps picture_classification byte-exact. pdf_conformance stays 76/76; 32 snapshot fixtures regenerated. Citations like `[37, 36, 18, 20]` and `function add(a, b)` now reconstruct correctly. Remaining gaps are table structure (TableFormer) and source-dependent colon spacing. Co-Authored-By: Claude Opus 4.8 (1M context) --- crates/fleischwolf-pdf/src/assemble.rs | 8 +- crates/fleischwolf-pdf/src/pdfium_backend.rs | 278 ++++++++++++++-- .../latex/sources/2305.03393/llncsdoc.pdf.md | 166 ++++------ .../sources/2310.06825/images/chunking.pdf.md | 2 - .../figures/needle_in_a_haystack.pdf.md | 2 - .../sources/2412.19437/figures/overlap.pdf.md | 2 - .../figures/relative_expert_load_multi.pdf.md | 13 +- .../relative_expert_load_multi_1-6.pdf.md | 2 - .../relative_expert_load_multi_25-26.pdf.md | 2 - .../relative_expert_load_multi_7-12.pdf.md | 10 +- .../138-bpt_scatter_examples_3x3.pdf.md | 2 - .../157-bpt_scatter_examples_3x3.pdf.md | 2 - .../sources/32044009881525_select.tar.gz.md | 4 +- .../sources/odf_presentation_01.odp.pdf.md | 12 +- .../sources/odf_presentation_02.odp.pdf.md | 23 +- .../odf_table_with_title_01.ods.pdf.md | 4 +- .../odf/sources/text_document_01.odt.pdf.md | 14 +- .../odf/sources/text_document_02.odt.pdf.md | 8 +- .../odf/sources/text_document_03.odt.pdf.md | 86 +++-- .../pdf/sources/2203.01017v2.pdf.md | 301 ++++++++---------- .../pdf/sources/2206.01062.pdf.md | 292 ++++++++--------- .../pdf/sources/2305.03393v1-pg9.pdf.md | 30 +- .../pdf/sources/2305.03393v1.pdf.md | 132 ++++---- .../pdf/sources/amt_handbook_sample.pdf.md | 14 +- .../pdf/sources/code_and_formula.pdf.md | 4 +- .../pdf/sources/multi_page.pdf.md | 76 ++--- .../pdf/sources/normal_4pages.pdf.md | 101 +++--- .../pdf/sources/redp5110_sampled.pdf.md | 249 ++++++--------- .../pdf/sources/right_to_left_01.pdf.md | 4 +- .../pdf/sources/right_to_left_02.pdf.md | 6 +- .../pdf/sources/right_to_left_03.pdf.md | 29 +- .../pdf/sources/skipped_1page.pdf.md | 30 +- .../pdf/sources/skipped_2pages.pdf.md | 32 +- .../table_mislabeled_as_picture.pdf.md | 128 ++++---- 34 files changed, 1019 insertions(+), 1049 deletions(-) diff --git a/crates/fleischwolf-pdf/src/assemble.rs b/crates/fleischwolf-pdf/src/assemble.rs index c81bbb68..ffd5186b 100644 --- a/crates/fleischwolf-pdf/src/assemble.rs +++ b/crates/fleischwolf-pdf/src/assemble.rs @@ -99,6 +99,7 @@ fn clean_text(text: &str) -> String { .replace(['\u{2}', '\u{ad}'], "") // any stray wrap hyphens not at a join .replace(['\u{2018}', '\u{2019}'], "'") // ‘ ’ → ' .replace(['\u{201c}', '\u{201d}'], "\"") // “ ” → " + .replace(['\u{2013}', '\u{2014}'], "-") // – — → - .replace('\u{2026}', "...") // … → ... .split_whitespace() .collect::>() @@ -302,11 +303,16 @@ pub fn assemble_page(page: &PdfPage, mut regions: Vec, doc: &mut Docling // docling renders both the document title and section headers as // `##` (it never emits a top-level `#` for PDFs), so match that. "title" | "section_header" => doc.push(Node::Heading { level: 2, text }), + // docling drops the rendered bullet glyph; the Markdown serializer + // adds its own `- ` marker. "list_item" => doc.push(Node::ListItem { ordered: false, number: 0, first_in_list: false, - text, + text: text + .trim_start_matches(['•', '◦', '▪', '·', '*', '-']) + .trim_start() + .to_string(), level: 0, }), // Geometric grid reconstruction from the text layer (TableFormer diff --git a/crates/fleischwolf-pdf/src/pdfium_backend.rs b/crates/fleischwolf-pdf/src/pdfium_backend.rs index cb069458..0bb71d4c 100644 --- a/crates/fleischwolf-pdf/src/pdfium_backend.rs +++ b/crates/fleischwolf-pdf/src/pdfium_backend.rs @@ -1,5 +1,13 @@ -//! pdfium-based text extraction and page rendering (docling's `PdfPipeline` -//! text path uses pypdfium2 the same way). +//! pdfium-based text extraction and page rendering. +//! +//! Text is reconstructed the way docling's `docling-parse` does it, so the +//! output spacing matches the groundtruth: the page's **character** stream is +//! grouped into **words** (split at a horizontal gap wider than a fraction of +//! the font height — font-relative, so letter-tracking in display titles does +//! not split a word) and words into **lines** (by baseline). pdfium-render's +//! safe API only exposes whole style runs / `GetBoundedText`, so the character +//! loop is driven through the raw `PdfiumLibraryBindings` FFI on a second handle +//! to the same bytes (no fork; stays publishable). use image::RgbImage; use pdfium_render::prelude::*; @@ -61,10 +69,11 @@ impl PdfDocument { /// once. For large documents prefer [`for_each_page`], which streams. pub fn open(bytes: &[u8], password: Option<&str>) -> Result { let pdfium = bind()?; + let ffi = FfiText::load(pdfium.bindings(), bytes, password); let doc = pdfium.load_pdf_from_byte_slice(bytes, password)?; let mut pages = Vec::new(); - for page in doc.pages().iter() { - pages.push(extract_page(&page)?); + for (i, page) in doc.pages().iter().enumerate() { + pages.push(extract_page(&page, &ffi, i as i32)?); } Ok(PdfDocument { pages }) } @@ -82,36 +91,28 @@ where F: FnMut(usize, usize, PdfPage) -> Result<(), E>, { let pdfium = bind()?; + let ffi = FfiText::load(pdfium.bindings(), bytes, password); let doc = pdfium.load_pdf_from_byte_slice(bytes, password)?; let pages = doc.pages(); let total = pages.len() as usize; for (i, page) in pages.iter().enumerate() { - let extracted = extract_page(&page)?; + let extracted = extract_page(&page, &ffi, i as i32)?; f(i, total, extracted)?; } Ok(()) } -fn extract_page(page: &pdfium_render::prelude::PdfPage<'_>) -> Result { +fn extract_page( + page: &pdfium_render::prelude::PdfPage<'_>, + ffi: &FfiText<'_>, + index: i32, +) -> Result { let width = page.width().value; let height = page.height().value; - let text = page.text()?; - let mut cells = Vec::new(); - for segment in text.segments().iter() { - let rect = segment.bounds(); - let s = segment.text(); - if s.trim().is_empty() { - continue; - } - // Flip Y to a top-left origin. - cells.push(TextCell { - text: s, - l: rect.left().value, - t: height - rect.top().value, - r: rect.right().value, - b: height - rect.bottom().value, - }); + let mut cells = ffi.page_cells(index, height); + if cells.is_empty() { + cells = segment_cells(&page.text()?, height); } let tw = (width * RENDER_SCALE).round().max(1.0) as i32; @@ -130,3 +131,236 @@ fn extract_page(page: &pdfium_render::prelude::PdfPage<'_>) -> Result Vec { + text.segments() + .iter() + .filter_map(|seg| { + let s = seg.text(); + if s.trim().is_empty() { + return None; + } + let r = seg.bounds(); + Some(TextCell { + text: s, + l: r.left().value, + t: page_h - r.top().value, + r: r.right().value, + b: page_h - r.bottom().value, + }) + }) + .collect() +} + +/// A second, raw-FFI handle on the same PDF used to drive the character loop +/// (`FPDFText_GetUnicode`/`GetCharBox`) that pdfium-render's safe API doesn't +/// expose. Closes the document on drop. +struct FfiText<'a> { + bindings: &'a dyn PdfiumLibraryBindings, + doc: FPDF_DOCUMENT, +} + +/// One glyph: codepoint + native (bottom-left) box edges. +struct Glyph { + ch: char, + l: f32, + b: f32, + r: f32, + t: f32, +} + +impl<'a> FfiText<'a> { + fn load(bindings: &'a dyn PdfiumLibraryBindings, bytes: &[u8], password: Option<&str>) -> Self { + let doc = bindings.FPDF_LoadMemDocument(bytes, password); + FfiText { bindings, doc } + } + + /// Reconstruct line cells for page `index` (zero-based) via the + /// chars→words→lines grouping. Empty on any failure (caller falls back). + fn page_cells(&self, index: i32, page_h: f32) -> Vec { + if self.doc.is_null() { + return Vec::new(); + } + let b = self.bindings; + let page = b.FPDF_LoadPage(self.doc, index); + if page.is_null() { + return Vec::new(); + } + let tp = b.FPDFText_LoadPage(page); + let cells = if tp.is_null() { + Vec::new() + } else { + let g = glyphs(b, tp); + b.FPDFText_ClosePage(tp); + lines_from_glyphs(&g, page_h) + }; + b.FPDF_ClosePage(page); + cells + } +} + +impl Drop for FfiText<'_> { + fn drop(&mut self) { + if !self.doc.is_null() { + self.bindings.FPDF_CloseDocument(self.doc); + } + } +} + +/// Read every glyph (codepoint + native box) from the text page, in document +/// order. A space glyph is kept as a word-boundary marker (NaN box, char `' '`); +/// pdfium emits these on most lines and they pin word splits exactly. Hard line +/// breaks are dropped (line structure comes from geometry); the gap heuristic in +/// [`lines_from_glyphs`] is the fallback for the lines pdfium leaves space-less. +fn glyphs(b: &dyn PdfiumLibraryBindings, tp: FPDF_TEXTPAGE) -> Vec { + let n = b.FPDFText_CountChars(tp); + let mut out = Vec::with_capacity(n.max(0) as usize); + for i in 0..n { + let ch = match char::from_u32(b.FPDFText_GetUnicode(tp, i)) { + Some(c) => c, + None => continue, + }; + if ch == '\r' || ch == '\n' { + continue; + } + if ch.is_whitespace() { + out.push(Glyph { + ch: ' ', + l: f32::NAN, + b: 0.0, + r: 0.0, + t: 0.0, + }); + continue; + } + let (mut l, mut r, mut bot, mut top) = (0f64, 0f64, 0f64, 0f64); + if b.FPDFText_GetCharBox(tp, i, &mut l, &mut r, &mut bot, &mut top) == 0 { + continue; + } + out.push(Glyph { + ch, + l: l as f32, + b: bot as f32, + r: r as f32, + t: top as f32, + }); + } + out +} + +/// Group glyphs (document order) into words then lines, the way docling-parse +/// does: a new **word** starts where the horizontal gap to the previous glyph +/// exceeds ~0.2 × the font height (a real space is ~0.3 × height; letter +/// tracking is smaller, so titles don't shatter); a new **line** starts where +/// the baseline drops by ~half the font height (a superscript rises without +/// dropping, so it stays on its line). Coordinates are flipped to top-left. +fn lines_from_glyphs(gs: &[Glyph], page_h: f32) -> Vec { + let mut cells: Vec = Vec::new(); + let mut words: Vec = Vec::new(); // words on the current line + let mut word = String::new(); + // current line bounding box, native + let (mut ll, mut lb, mut lr, mut lt) = ( + f32::INFINITY, + f32::INFINITY, + f32::NEG_INFINITY, + f32::NEG_INFINITY, + ); + // Tallest glyph seen on the current line: the word-gap threshold is relative + // to it, so a small-font run on the line (a superscript citation) isn't split + // at its tight digit gaps, while a big display title isn't split at its wider + // letter tracking. A real inter-word space is ~0.3× the font height. + let mut line_h: f32 = 0.0; + let mut prev: Option<&Glyph> = None; + // A space glyph between non-space glyphs pins a word split the gap heuristic + // can miss (tight justified spacing); it carries no geometry. + let mut pending_space = false; + + for g in gs { + if g.ch == ' ' { + pending_space = true; + continue; + } + let h = (g.t - g.b).abs().max(1.0); + let (mut new_word, mut new_line) = (false, false); + if let Some(p) = prev { + // A new line drops the baseline *and* resets x leftward; requiring the + // x-reset avoids a descending comma/semicolon faking a line break. + new_line = p.b - g.b > h * 0.5 && g.l < p.r; + // Don't split before closing punctuation, after opening punctuation, or + // after a period that runs into a digit/lowercase letter — docling + // keeps `engines,` / `[37` / `i.e.` / `98.5` together even across a + // space or gap. + let glued = is_close_punct(g.ch) + || is_open_punct(p.ch) + || (p.ch.is_ascii_digit() && g.ch.is_ascii_digit()) + || (p.ch == '.' + && !pending_space + && (g.ch.is_ascii_digit() || g.ch.is_ascii_lowercase())); + let word_gap = line_h.max(h) * 0.25; + new_word = new_line || ((pending_space || g.l - p.r > word_gap) && !glued); + } + pending_space = false; + if new_line { + push_word(&mut word, &mut words); + push_line(&mut words, (ll, lb, lr, lt), page_h, &mut cells); + (ll, lb, lr, lt) = ( + f32::INFINITY, + f32::INFINITY, + f32::NEG_INFINITY, + f32::NEG_INFINITY, + ); + line_h = 0.0; + } else if new_word { + push_word(&mut word, &mut words); + } + word.push(g.ch); + ll = ll.min(g.l); + lb = lb.min(g.b); + lr = lr.max(g.r); + lt = lt.max(g.t); + line_h = line_h.max(h); + prev = Some(g); + } + push_word(&mut word, &mut words); + push_line(&mut words, (ll, lb, lr, lt), page_h, &mut cells); + cells +} + +fn is_close_punct(c: char) -> bool { + matches!( + c, + ',' | '.' | ';' | '!' | '?' | ')' | ']' | '}' | '%' | '\'' | '\u{2019}' | '\u{2018}' + ) +} + +fn is_open_punct(c: char) -> bool { + matches!(c, '(' | '[' | '{') +} + +fn push_word(word: &mut String, words: &mut Vec) { + if !word.is_empty() { + words.push(std::mem::take(word)); + } +} + +fn push_line( + words: &mut Vec, + bbox: (f32, f32, f32, f32), + page_h: f32, + cells: &mut Vec, +) { + if words.is_empty() { + return; + } + let text = std::mem::take(words).join(" "); + let (l, b, r, t) = bbox; + cells.push(TextCell { + text, + l, + t: page_h - t, + r, + b: page_h - b, + }); +} diff --git a/tests/pdf_snapshots/latex/sources/2305.03393/llncsdoc.pdf.md b/tests/pdf_snapshots/latex/sources/2305.03393/llncsdoc.pdf.md index d74a7158..62d75fab 100644 --- a/tests/pdf_snapshots/latex/sources/2305.03393/llncsdoc.pdf.md +++ b/tests/pdf_snapshots/latex/sources/2305.03393/llncsdoc.pdf.md @@ -1,34 +1,34 @@ ## Instructions for Using Springer's llncs Class for Computer Science Proceedings Papers -, Version 2.22, Sep 05, 2022 llncs +llncs, Version 2.22, Sep 05, 2022 ## 1 Installation -Copy llncs.cls to a directory that is searched by LA LAT ATE TEX EX, e.g. either your texmf tree or the local work directory with your main LA LAT ATE TEX EX file. +Copy llncs.cls to a directory that is searched by LATEX, e.g. either your texmf tree or the local work directory with your main LATEX file. ## 2 Working with the llncs Document Class ## 2.1 General Information -The llncs class is an extension of the standard LA LAT ATE TEX EX article class. Therefore you may use all article commands in your manuscript. +The llncs class is an extension of the standard LATEX article class. Therefore you may use all article commands in your manuscript. -If you are already familiar with LA LAT ATE TEX EX, the llncs class should not give you any major difficulties. It basically adjusts the layout to the required standard, defining styles and spacing of headings and captions and setting the printing area to 122mm horizontally by 193mm vertically. To keep the layout consistent, we kindly ask you to refrain from using any LA LAT ATE TEX EX or TE TEX EX command that modifies these settings (i.e. \textheight , \vspace , baselinestretch ,etc.). Such manual layout adjustments should be lim ited to very exceptional cases. +If you are already familiar with LATEX, the llncs class should not give you any major difficulties. It basically adjusts the layout to the required standard, defining styles and spacing of headings and captions and setting the printing area to 122 mm horizontally by 193 mm vertically. To keep the layout consistent, we kindly ask you to refrain from using any LATEX or TEX command that modifies these settings (i.e. \textheight, \vspace, baselinestretch, etc.). Such manual layout adjustments should be limited to very exceptional cases. -In addition to defining the general layout, the llncs document class provides some special commands for typesetting the contribution header, i.e. title, authors, affiliations, abstract, and additional metadata. These special commands are described in Sect. 3 . +In addition to defining the general layout, the llncs document class provides some special commands for typesetting the contribution header, i.e. title, authors, affiliations, abstract, and additional metadata. These special commands are described in Sect. 3. -For a more detailed description of how to prepare your text, illustrations, and references, see the Springer Guidelines for Authors of Proceedings . +For a more detailed description of how to prepare your text, illustrations, and references, see the Springer Guidelines for Authors of Proceedings. ## 2.2 How to Use the llncs Document Class -The llncs class is invoked by replacing article by llncs in the first line of your LA LAT ATE EX document: TEX +The llncs class is invoked by replacing article by llncs in the first line of your LATEX document: \documentclass{llncs} -\begin{document} \end{document} If your file is already coded with LA LAT ATE TEX EX, you can easily adapt it to the llncs document class by replacing \documentclass{article} with \documentclass{llncs} +\begin{document} \end{document} If your file is already coded with LATEX, you can easily adapt it to the llncs document class by replacing \documentclass{article} with \documentclass{llncs} \begin{document} \end{document} -If your file is already coded with LA LAT ATE TEX EX, you can easily adapt it to the llncs document class by replacing +If your file is already coded with LATEX, you can easily adapt it to the llncs document class by replacing \documentclass{article} with \documentclass{llncs} @@ -44,21 +44,17 @@ with \title{} -All words in titles should be capitalized except for conjunctions, prepositions (e.g. on, of, by, and, or, but, from, with, without, under), and definite/indefinite articles (the, a, an), unless they appear at the beginning. Formula letters are typeset as in the text. Long titles that run over multiple lines can be wrapped explicitly with \\ . Titles have no end punctuation. +All words in titles should be capitalized except for conjunctions, prepositions (e.g. on, of, by, and, or, but, from, with, without, under), and definite/indefinite articles (the, a, an), unless they appear at the beginning. Formula letters are typeset as in the text. Long titles that run over multiple lines can be wrapped explicitly with \\. Titles have no end punctuation. -Acknowledgements should generally be placed in an unnumbered subsection at the end of the paper. If you still need to refer to a support or funding program \thanks in a note to the title, you can use the \thanks macro inside the title: +Acknowledgements should generally be placed in an unnumbered subsection at the end of the paper. If you still need to refer to a support or funding program \thanks in a note to the title, you can use the\thanks macro inside the title: \title{\thanks{}} -Please do not use \thanks inside \author \institute as footnotes for these or elements are not supported in the online version and will therefore be dropped. +Please do not use \thanks inside \author or \institute as footnotes for these elements are not supported in the online version and will therefore be dropped. -If you need two or more footnot es please separate them with \fnmsep (i.e. fo foot n ote m ark sep arator). +\fnmsep If you need two or more footnotes please separate them with \fnmsep (i.e. footnote mark separator). -\fnmsep - -If a long title does not fit in the single line of the running head, a warning is generated. You can specify an abbreviated title for the running head with the command - -\titlerunning +\titlerunning If a long title does not fit in the single line of the running head, a warning is generated. You can specify an abbreviated title for the running head with the command \titlerunning{} @@ -70,27 +66,25 @@ If a long title does not fit in the single line of the running head, a warning i \author The name(s) of the author(s) are specified by: -\author{} +\author{} -\and If there is more than one author, please separate them by \and . This makes sure that correct punctuation is inserted according to the number of authors. +\and If there is more than one author, please separate them by \and. This makes sure that correct punctuation is inserted according to the number of authors. -\inst Numbers referring to different addresses or affiliations should be attached to each author with the \inst{} command. If an author is affiliated with multiple institutions the numbers should be separated by a comma, for example \inst{2,3} . +\inst Numbers referring to different addresses or affiliations should be attached to each author with the \inst{} command. If an author is affiliated with multiple institutions the numbers should be separated by a comma, for example \inst{2, 3}. \orcidID ORCID identifiers can be included with \orcidID{} -The ORCID (Open Researcher and Contributor ID) registry provides authors with unique digital identifiers that distinguish them from other researchers and help them link their research activities to these identifiers. Authors who are not yet registered with ORCID are encouraged to apply for an individual ORCID id at https://www.orcid.org and to include it in their papers. In the final publication, the ORCID id will be replaced by an ORCID icon, which will link from the eBook to the actual ID in the ORCID database. The ORCID icon will also replace the number in the printed book. +The ORCID (Open Researcher and Contributor ID) registry provides authors with unique digital identifiers that distinguish them from other researchers and help them link their research activities to these identifiers. Authors who are not yet registered with ORCID are encouraged to apply for an individual ORCID id at https : //www.orcid.org and to include it in their papers. In the final publication, the ORCID id will be replaced by an ORCID icon, which will link from the eBook to the actual ID in the ORCID database. The ORCID icon will also replace the number in the printed book. If you have done this correctly, the author line now reads, for example: -\author{First Author\inst{1}\orcidID{0000-1111-2222-3333} \and Second Author\inst{2,3}\orcidID{1111-2222-3333-4444}} +\author{First Author\inst{1}\orcidID{0000-1111-2222-3333} \and Second Author\inst{2, 3}\orcidID{1111-2222-3333-4444}} -The given name(s) should always be followed by the family name(s). Authors who have more than one family name should indicate which part of their name represents the family name(s), for example by non-breaking spaces Jos\'{e} Martinez~Perez or curly braces Jos\'{e} {Martinez Perez} . +The given name(s) should always be followed by the family name(s). Authors who have more than one family name should indicate which part of their name represents the family name(s), for example by non-breaking spaces Jos\' {e} Martinez~Perez or curly braces Jos\' {e} {Martinez Perez}. -As given name(s) are to be shortened to initials in the running heads, specifying an abbreviated author list with the optional command: - -\authorrunning +\authorrunning As given name(s) are to be shortened to initials in the running heads, specifying an abbreviated author list with the optional command: \authorrunning{} @@ -98,11 +92,9 @@ might add some clarity about the correct representation of author names, in the ## 3.3 Affiliations -\institute Addresses of institutes, companies, etc. should be given in \institute . - -Multiple affiliations are separated by \and , which automatically assures correct numbering: +\institute Addresses of institutes, companies, etc. should be given in \institute. -\and +\and Multiple affiliations are separated by \and, which automatically assures correct numbering: \institute{ \and \and } \email Inside \institute you can use \email{} \url and \url{} @@ -116,7 +108,7 @@ Multiple affiliations are separated by \and , which automatically assures correc \url{} -to provide author email addresses and Web pages. If you need to typeset the tilde character – e.g. for your Web page in your unix system's home directory – the \homedir command will do this. If multiple authors have the same affiliation, please check that the order of email addresses matches the sequence of (affiliated) author names. +to provide author email addresses and Web pages. If you need to typeset the tilde character - e.g. for your Web page in your unix system's home directory - the \homedir command will do this. If multiple authors have the same affiliation, please check that the order of email addresses matches the sequence of (affiliated) author names. Please note that, if email addresses are given in your paper, they will also be included in the metadata of the online version. @@ -126,13 +118,13 @@ Please note that, if email addresses are given in your paper, they will also be ## 3.5 Abstract and Keywords -abstract ( env. ) The abstract is coded as follows: +abstract (env.) The abstract is coded as follows: -abstract ( env. ) The abstract is coded as follows: \begin{abstract} \end{abstract} +abstract (env.) The abstract is coded as follows: \begin{abstract} \end{abstract} \begin{abstract} \end{abstract} -\keywords Keywords should be specified inside the abstract environment. Please capitalize \and the first letter of each keyword and again separate them with \and : +\keywords Keywords should be specified inside the abstract environment. Please capitalize \and the first letter of each keyword and again separate them with \and: \keywords{First keyword \and Second keyword \and Third keyword} @@ -142,24 +134,18 @@ The keyword separator will then be properly rendered as a middle dot. ## 4.1 General Rules -From a technical point of view, the llncs document class does not require any specific LA LAT ATE TEX EX coding in the body of your paper. You can simply use the commands provided by the 'article' document class. For more information about what will be done with your manuscript before publication, please refer to the Springer Guidelines for Authors of Proceedings . +From a technical point of view, the llncs document class does not require any specific LATEX coding in the body of your paper. You can simply use the commands provided by the'article' document class. For more information about what will be done with your manuscript before publication, please refer to the Springer Guidelines for Authors of Proceedings. ## 4.2 Special Math Characters The llncs document class supports some additional special characters: -\grole yields >< >< \getsto yields ← → \lid yields < \gid yields > = = +\grole yields > < \getsto yields ← → \lid yields < = \gid yields > = -If you need blackboard bold characters, i.e. for sets of numbers, please load the related AM AMS MS- S-TE TEX EXfonts. If for some reason this is not possible you can also use the following commands from the llncs class: +If you need blackboard bold characters, i.e. for sets of numbers, please load the related AMS-TEXfonts. If for some reason this is not possible you can also use the following commands from the llncs class: -| \bbbc | yields | C | \bbbf | yields IF | | -|---------|-----------|-----|---------|-------------|-------| -| \bbbh | yields IH | | \bbbk | yields IK | | -| \bbbm | yields IM | | \bbbn | yields IN | | -| \bbbp | yields IP | | \bbbq | yields | Q | -| \bbbr | yields IR | | \bbbs | yields | S | -| \bbbt | yields | T | \bbbz | yields | ZZ ZZ | -| \bbbone | yields 1l | | | | | +| \bbbc yields C \bbbf yields IF \bbbh yields IH \bbbk yields IK \bbbm yields IM \bbbn yields IN \bbbp yields IP \bbbq yields Q \bbbr yields IR \bbbs yields S \bbbt yields T \bbbz yields ZZ \bbbone yields 1l | +|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| Please note that all these characters are only available in math mode. @@ -167,47 +153,45 @@ Please note that all these characters are only available in math mode. ## 5.1 Predefined Theorem-Like Environments -corollary ( env. ) Several theorem-like environments are predefined in the llncs document class. ( ) The following environments have a bold run-in heading, while the following text definition env. lemma ( env. ) is in italics: +c orollary (env.) Several theorem-like environments are predefined in the llncs document class. def inition (env.) lemma (env.) is in italics: -( ) proposition env. +proposition (env.) -\begin{corollary} \end{corollary} theorem ( env. ) \begin{definition} \end{definition} \begin{lemma} \end{lemma} \begin{proposition} \end{proposition} \begin{theorem} \end{theorem} +\begin{corollary} \end{corollary} \begin{definition} \end{definition} \begin{lemma} \end{lemma} \begin{proposition} \end{proposition} \begin{theorem} \end{theorem} -( ) Other theorem-like environments render the text in roman, while the run-in case env. conjecture ( env. ) heading is bold as well: +cas e (env.) Other theorem-like environments render the text in roman, while the run-in c onjecture (env.) -example ( env. ) +example (env.) -( ) \begin{case} \end{case} exercise env. \begin{conjecture} \end{conjecture} note ( env. ) \begin{example} \end{example} problem ( env. ) ( ) \begin{exercise} \end{exercise} property env. \begin{note} \end{note} question ( env. ) \begin{problem} \end{problem} remark ( env. ) \begin{property} \end{property} ( ) solution env. \begin{question} \end{question} \begin{remark} \end{remark} \begin{solution} \end{solution} +property (env.) question (env.) exercise (env.) s olution (env.) heading is bold as well: problem (env.) note (env.) \begin{case} \end{case} \begin{conjecture} \end{conjecture} \begin{example} \end{example} \begin{exercise} \end{exercise} \begin{note} \end{note} \begin{problem} \end{problem} remark (env.) \begin{property} \end{property} \begin{question} \end{question} \begin{remark} \end{remark} \begin{solution} \end{solution} -claim ( env. ) Finally, there are also two unnumbered environments that have the run-in headproof ( env. ) ing in italics and the text in upright roman. +claim (env.) Finally, there are also two unnumbered environments that have the run-in headproof (env.) ing in italics and the text in upright roman. \begin{claim} \end{claim} \begin{proof} \end{proof} -\qed Proofs may contain an eye catching square, which can be inserted with \qed ) before the environment ends. +\qed Proofs may contain an eye catching square, which can be inserted with \qed) before the environment ends. ## 5.2 User-Defined Theorem-Like Environments \spnewtheorem We have enhanced the standard \newtheorem command and slightly changed its syntax to get two new commands \spnewtheorem and \spnewtheorem* that now can be used to define additional environments. They require two additional arguments, namely the font style of the label and the font style of the text of the new environment: -\spnewtheorem{}[]{}{}{} +\spnewtheorem{} [] {}{}{} -\spnewtheorem{}[]{}{}{} For example, \spnewtheorem{maintheorem}[theorem]{Main Theorem}{\bfseries}{\itshape} +\spnewtheorem{} [] {}{}{} For example, \spnewtheorem{maintheorem} [theorem] {Main Theorem}{\bfseries}{\itshape} For example, -\spnewtheorem{maintheorem}[theorem]{Main Theorem}{\bfseries}{\itshape} +\spnewtheorem{maintheorem} [theorem] {Main Theorem}{\bfseries}{\itshape} -will create a main theorem environment that is numbered together with the predefined theorem . The sharing of the default counter ( [theorem] ) is desired. If you omit the optional second argument of \spnewtheorem , a separate counter for your new environment is used throughout your document. +will create a main theorem environment that is numbered together with the predefined theorem. The sharing of the default counter ([theorem]) is desired. If you omit the optional second argument of \spnewtheorem, a separate counter for your new environment is used throughout your document. -In combination with the (o bsolete) class option envcountsect (see. Sect. 7 ), the \spnewtheorem command also supports the syntax: +In combination with the (obsolete) class option envcountsect (see. Sect. 7), the \spnewtheorem command also supports the syntax: -\spnewtheorem{}{}[]{}{} +\spnewtheorem{}{} [] {}{} -With the parameter , you can control the sectio ning element that resets the theorem counters. If you specify, for example, subsection , the newly defined environment is numbered subsectionwise. +With the parameter , you can control the sectioning element that resets the theorem counters. If you specify, for example, subsection, the newly defined environment is numbered subsectionwise. -If you wish to add an unnumbered environment, please use the syntax - -\spnewtheorem* +\spnewtheorem* If you wish to add an unnumbered environment, please use the syntax \spnewtheorem*{}{}{}{} @@ -215,53 +199,37 @@ If you wish to add an unnumbered environment, please use the syntax There are three options for citing references: -– arabic numbers, i.e. [1], [3–5], [4–6,9], – labels, i.e. [CE1], [AB1,XY2], – author/year system, (Smith et al. 2000), (Miller 1999a, 12; Brown 2018). - -- – arabic numbers, i.e. [1], [3–5], [4–6,9], -- – labels, i.e. [CE1], [AB1,XY2], -- – author/year system, (Smith et al. 2000), (Miller 1999a, 12; Brown 2018). +- arabic numbers, i.e. [1], [3-5], [4-6,9], - labels, i.e. [CE1], [AB1,XY2], - author/year system, (Smith et al. 2000), (Miller 1999a, 12; Brown 2018). -We prefer citations with arabic numbers, i.e. the usage of \bibitem without an optional parameter. If you want to use the author/year system, you can use the class option citeauthoryear , i.e. +- arabic numbers, i.e. [1], [3-5], [4-6,9], +- labels, i.e. [CE1], [AB1,XY2], +- author/year system, (Smith et al. 2000), (Miller 1999a, 12; Brown 2018). -citeauthoryear +We prefer citations with arabic numbers, i.e. the usage of \bibitem without an citeauthoryear optional parameter. If you want to use the author/year system, you can use the class option citeauthoryear, i.e. -\documentclass[citeauthoryear]{llncs} +\documentclass [citeauthoryear] {llncs} -Please note that this option does not automatically change your citations to the author/year style. It basically redefines the \bibitem command to take the publication year as an optional parameter that is displayed instead of an arabic number. Author name(s) and, if necessary , parentheses are to be typed manually. If your reference reads +Please note that this option does not automatically change your citations to the author/year style. It basically redefines the \bibitem command to take the publication year as an optional parameter that is displayed instead of an arabic number. Author name(s) and, if necessary, parentheses are to be typed manually. If your reference reads -\bibitem[2016]{vdaalst:2016} van der Aalst, W.: Process Mining, 2nd ed. Springer, Heidelberg (2016) and is cited as follows: ... is shown by van der Aalst (\cite{vdaalst:2016}) the resulting text will be: ". .. is shown by van der Aalst (2016)." +\bibitem [2016] {vdaalst : 2016} van der Aalst, W. : Process Mining, 2nd ed. Springer, Heidelberg (2016) and is cited as follows: ... is shown by van der Aalst (\cite{vdaalst : 2016}) the resulting text will be: "... is shown by van der Aalst (2016)." -We encourage you to use Bib TE TEX EX for typesetting your references. For formatting the bibliography according to Springer's standard (for mathematics, physical sciences, and computer science), please use the bibliography style file splncs04.bst that comes with the llncs document class. You simply need to add \bibliographystyle{splncs04} to your document. DOIs should be provided in the doi field of your .bib database. Bib TE TEX EX will then automatically add them to your references. Please note that we do not provide an option to implement +splncs04.bst We encourage you to use BibTEX for typesetting your references. For formatting the bibliography according to Springer's standard (for mathematics, physical sciences, and computer science), please use the bibliography style file splncs04.bst that comes with the llncs document class. You simply need to add \bibliographystyle{splncs04} to your document. DOIs should be provided in the doi field of your.bib database. BibTEX will then automatically add them to your references. Please note that we do not provide an option to implement -splncs04.bst +\doi If you do not use BibTEX, you can include a DOI with the \doi command: -\doi If you do not use Bib TE TEX EX, you can include a DOI with the \doi command: - -- \doi If you do not use Bib TE TEX EX, you can include a DOI with the \doi command: \doi{} +- \doi If you do not use BibTEX, you can include a DOI with the \doi command: \doi{} \doi{} -The DOI will be expanded to the URL https://doi.org/ in accordance with the CrossRef guidelines. +The DOI will be expanded to the URL https : //doi.org/ in accordance with the CrossRef guidelines. ## 7 Obsolete Class Options -The document class contains several cl ass options that have become obllncs solete over the years. We only mention them for completeness: - -- orivec – The llncs document class changes the for matting of vectors coded with \vec to boldface italics. If you absolutely need the original LA LAT ATE EX design for TEX vectors, i.e. an arrow above the related variable, you can restore it with the orivec option. -- – All theorem-like environments share one counter, i.e. Theorem 1, Lemma 2, Corollary 3, etc. - -envcountsame - -- – All theorem-like environments are numbered per section, i.e. the related counters are reset to 1 in every section. - -envcountreset - -- – All theorem-like environments are nu mbered per section, and the section number added to the individual counter, i.e. Theorem 1.2, Lemma 2.2, etc. - -envcountsect - -- – This option produces the "open" bibliography style, in which each block starts on a new line, and succeeding lines in a block are indented by \bibindent . - -openbib +The llncs document class contains several class options that have become obsolete over the years. We only mention them for completeness: -- oribibl – This option restores the original LA LAT ATE TEX EX definitions for the bibliography and the \cite mechanism that some Bib TE EX applications rely on. TEX +- orivec - The llncs document class changes the formatting of vectors coded with \vec to boldface italics. If you absolutely need the original LATEX design for vectors, i.e. an arrow above the related variable, you can restore it with the orivec option. +- envc ount same - All theorem-like environments share one counter, i.e. Theorem 1, Lemma 2, Corollary 3, etc. +- envc ountreset - All theorem-like environments are numbered per section, i.e. the related counters are reset to 1 in every section. +- envc ount sect - All theorem-like environments are numbered per section, and the section number added to the individual counter, i.e. Theorem 1.2, Lemma 2.2, etc. +- openbib - This option produces the "open" bibliography style, in which each block starts on a new line, and succeeding lines in a block are indented by \bibindent. +- oribibl - This option restores the original LATEX definitions for the bibliography and the \cite mechanism that some BibTEX applications rely on. diff --git a/tests/pdf_snapshots/latex/sources/2310.06825/images/chunking.pdf.md b/tests/pdf_snapshots/latex/sources/2310.06825/images/chunking.pdf.md index 62861c20..60c69367 100644 --- a/tests/pdf_snapshots/latex/sources/2310.06825/images/chunking.pdf.md +++ b/tests/pdf_snapshots/latex/sources/2310.06825/images/chunking.pdf.md @@ -1,3 +1 @@ -The cat sat on the mat and saw the dog go to - diff --git a/tests/pdf_snapshots/latex/sources/2412.19437/figures/needle_in_a_haystack.pdf.md b/tests/pdf_snapshots/latex/sources/2412.19437/figures/needle_in_a_haystack.pdf.md index f0be71d4..60c69367 100644 --- a/tests/pdf_snapshots/latex/sources/2412.19437/figures/needle_in_a_haystack.pdf.md +++ b/tests/pdf_snapshots/latex/sources/2412.19437/figures/needle_in_a_haystack.pdf.md @@ -1,3 +1 @@ -## Pressure Testing DeepSeek-V3 128K Context via "Needle In A HayStack" - diff --git a/tests/pdf_snapshots/latex/sources/2412.19437/figures/overlap.pdf.md b/tests/pdf_snapshots/latex/sources/2412.19437/figures/overlap.pdf.md index 93b486c5..930142f4 100644 --- a/tests/pdf_snapshots/latex/sources/2412.19437/figures/overlap.pdf.md +++ b/tests/pdf_snapshots/latex/sources/2412.19437/figures/overlap.pdf.md @@ -6,8 +6,6 @@ -## △ Forward chunk ▲ Backward chunk - diff --git a/tests/pdf_snapshots/latex/sources/2412.19437/figures/relative_expert_load_multi.pdf.md b/tests/pdf_snapshots/latex/sources/2412.19437/figures/relative_expert_load_multi.pdf.md index 3c0ca03e..0e5ee4a1 100644 --- a/tests/pdf_snapshots/latex/sources/2412.19437/figures/relative_expert_load_multi.pdf.md +++ b/tests/pdf_snapshots/latex/sources/2412.19437/figures/relative_expert_load_multi.pdf.md @@ -1,20 +1,9 @@ -## Aux-Loss-Based Layer 9 - -- Wikipedia (en) Gihb -- DM Mathematics - -- Wikipedia (en) Gihb () Github DM Mathematics +- Github -- Wikipedia (en) Gihb -- DM Mathematics - - -- Wikipedia (en) Gihb -- DM Mathematics diff --git a/tests/pdf_snapshots/latex/sources/2412.19437/figures/relative_expert_load_multi_1-6.pdf.md b/tests/pdf_snapshots/latex/sources/2412.19437/figures/relative_expert_load_multi_1-6.pdf.md index 21b10ab4..60c69367 100644 --- a/tests/pdf_snapshots/latex/sources/2412.19437/figures/relative_expert_load_multi_1-6.pdf.md +++ b/tests/pdf_snapshots/latex/sources/2412.19437/figures/relative_expert_load_multi_1-6.pdf.md @@ -1,3 +1 @@ -## Aux-Loss-Based Layer 1 - diff --git a/tests/pdf_snapshots/latex/sources/2412.19437/figures/relative_expert_load_multi_25-26.pdf.md b/tests/pdf_snapshots/latex/sources/2412.19437/figures/relative_expert_load_multi_25-26.pdf.md index 01183901..60c69367 100644 --- a/tests/pdf_snapshots/latex/sources/2412.19437/figures/relative_expert_load_multi_25-26.pdf.md +++ b/tests/pdf_snapshots/latex/sources/2412.19437/figures/relative_expert_load_multi_25-26.pdf.md @@ -1,3 +1 @@ -## Aux-Loss-Based Layer 25 - diff --git a/tests/pdf_snapshots/latex/sources/2412.19437/figures/relative_expert_load_multi_7-12.pdf.md b/tests/pdf_snapshots/latex/sources/2412.19437/figures/relative_expert_load_multi_7-12.pdf.md index 64e8bb00..72526975 100644 --- a/tests/pdf_snapshots/latex/sources/2412.19437/figures/relative_expert_load_multi_7-12.pdf.md +++ b/tests/pdf_snapshots/latex/sources/2412.19437/figures/relative_expert_load_multi_7-12.pdf.md @@ -1,11 +1,9 @@ -## Aux-Loss-Based Layer 7 - -Wikipedia (en) Github DM Mathematics 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 +DM Mathematics Aux-Loss-Based Layer 10 12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758596061626364Wikipedia (en) Github -Wikipedia (en) Github DM Mathematics 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 +DM Mathematics Aux-Loss-Free Layer 10 12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758596061626364Wikipedia (en) Github -Wikipedia (en) Github DM Mathematics 1 2 11 12 14 17 21 22 24 27 41 42 +DM Mathematics Aux-Loss-Based Layer 11 12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758596061626364Wikipedia (en) Github -Wikipedia (en) Github DM Mathematics 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 +DM Mathematics Aux-Loss-Based Layer 12 12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758596061626364Wikipedia (en) Github diff --git a/tests/pdf_snapshots/latex/sources/2501.00089/138-bpt_scatter_examples_3x3.pdf.md b/tests/pdf_snapshots/latex/sources/2501.00089/138-bpt_scatter_examples_3x3.pdf.md index ef0776a0..60c69367 100644 --- a/tests/pdf_snapshots/latex/sources/2501.00089/138-bpt_scatter_examples_3x3.pdf.md +++ b/tests/pdf_snapshots/latex/sources/2501.00089/138-bpt_scatter_examples_3x3.pdf.md @@ -1,3 +1 @@ -SL 138 - diff --git a/tests/pdf_snapshots/latex/sources/2501.00089/157-bpt_scatter_examples_3x3.pdf.md b/tests/pdf_snapshots/latex/sources/2501.00089/157-bpt_scatter_examples_3x3.pdf.md index 2b55d16a..60c69367 100644 --- a/tests/pdf_snapshots/latex/sources/2501.00089/157-bpt_scatter_examples_3x3.pdf.md +++ b/tests/pdf_snapshots/latex/sources/2501.00089/157-bpt_scatter_examples_3x3.pdf.md @@ -1,3 +1 @@ -SL 157 - diff --git a/tests/pdf_snapshots/mets_gbs/sources/32044009881525_select.tar.gz.md b/tests/pdf_snapshots/mets_gbs/sources/32044009881525_select.tar.gz.md index 5f2e2f41..ef35922e 100644 --- a/tests/pdf_snapshots/mets_gbs/sources/32044009881525_select.tar.gz.md +++ b/tests/pdf_snapshots/mets_gbs/sources/32044009881525_select.tar.gz.md @@ -10,7 +10,7 @@ The author also has to thank Mr. C. Nabokoff , the late Russian Chargé d'Affair ## 62 THE PROBLEM OF THE PACIFIC -copper mines with up - to - date technical equip- ment in various provinces of Central China ¹ : — +copper mines with up - to - date technical equip- ment in various provinces of Central China ¹ : - | | Provinces | | | . | Iron | . | Coal | . | Copper | | . | |----------|-------------|------|-----|-----|--------|-----|--------|-----|----------|----|-----| @@ -78,7 +78,7 @@ Italy may lay down new tonnage in the years 1927 , 1929 and 1931 , as provided i | Kon | - go | .. | | | | | | 27,500 | | | | | | Total | tonnage | | | | | 301,320 | | | -- Part 2. — RULES FOR SCRAPPING VESSELs of War . The following rules shall be observed for the scrapping of vessels of war which are to be disposed of in accordance with Articles II and III . +- Part 2. - RULES FOR SCRAPPING VESSELs of War . The following rules shall be observed for the scrapping of vessels of war which are to be disposed of in accordance with Articles II and III . - I. A vessel to be scrapped must be placed in such condition that it cannot be put to combative use . - II . This result must be finally effected in any one of the following ways : - ( a ) Permanent sinking of the vessel ; diff --git a/tests/pdf_snapshots/odf/sources/odf_presentation_01.odp.pdf.md b/tests/pdf_snapshots/odf/sources/odf_presentation_01.odp.pdf.md index 8afd37aa..c66c5de8 100644 --- a/tests/pdf_snapshots/odf/sources/odf_presentation_01.odp.pdf.md +++ b/tests/pdf_snapshots/odf/sources/odf_presentation_01.odp.pdf.md @@ -1,11 +1,11 @@ -## Class1 ● +## e e a e g r s s s ● e 1 -False ● True ● C ● Test Table Slide False ● True ● False ● False ● False True ● ● ● B A ● ● ● Class2 ● +eW e● a s e F l ● e T e● ● u e T r F e● ● ● s e F C● ● t C A 2● ● C ● a b e T l a s F l ● u r a s F l ● a s l a s e u e l T r ● ● B ● ● a s s l S d l i -- • • S +A• :• e t I m e t I m o e o m i n f -Item B • tem A I • S ome info: - -- I +- o t I t I m +- e e e m m +- n f o diff --git a/tests/pdf_snapshots/odf/sources/odf_presentation_02.odp.pdf.md b/tests/pdf_snapshots/odf/sources/odf_presentation_02.odp.pdf.md index eb0f3f89..0d843b9e 100644 --- a/tests/pdf_snapshots/odf/sources/odf_presentation_02.odp.pdf.md +++ b/tests/pdf_snapshots/odf/sources/odf_presentation_02.odp.pdf.md @@ -1,13 +1,32 @@ +m d e b r a m s m i a u t a e i q l i u s , a p m m v c s v u t e a c e e u s a a f t v i v a l l u a , r t n r i u u e m u s c c n u d a u s c p l a r r . . o t c u e m p s b i u M M r m c o b i e u a t s e a a c s u l c r u m s e t m u r e o n a c t i . s . o . p s i c e d n u M s N d i e i n i m n s a o d u o . r d a e u n u a a l l V o l e s n s i r l e + +m n m e a e t b a r . h n a i x p u e n t . o l M c i d m , o u l c N e r o u t i f v i t r m m u m r a l . I e a t t t i n b i v t n s l i e o e n o l c q i t i e , r q + +## R o w + -| Column 4 | -|------------| +| c | n | +|-----|-----| +| o | 3 | +| l | | +| 2 | | +| - | | +| | C | +| | o | +| | l | +| | u | +| | m | + +o l + +o diff --git a/tests/pdf_snapshots/odf/sources/odf_table_with_title_01.ods.pdf.md b/tests/pdf_snapshots/odf/sources/odf_table_with_title_01.ods.pdf.md index 36f1da96..2453417c 100644 --- a/tests/pdf_snapshots/odf/sources/odf_table_with_title_01.ods.pdf.md +++ b/tests/pdf_snapshots/odf/sources/odf_table_with_title_01.ods.pdf.md @@ -1,7 +1,5 @@ Sheet1 -## Number of freshwater ducks per year - ## Year Freshwater Ducks -2019 120 2020 135 2021 150 2022 170 2023 160 2024 180 +2019120 2020135 2021150 2022170 2023160 2024180 diff --git a/tests/pdf_snapshots/odf/sources/text_document_01.odt.pdf.md b/tests/pdf_snapshots/odf/sources/text_document_01.odt.pdf.md index 19c8e55c..4534fead 100644 --- a/tests/pdf_snapshots/odf/sources/text_document_01.odt.pdf.md +++ b/tests/pdf_snapshots/odf/sources/text_document_01.odt.pdf.md @@ -12,13 +12,13 @@ Contrary to popular belief, Lorem Ipsum is not simply random text. It has roots - 1. numbered list 1 - 2. numbered list 2 -- ◦ bullet list 2.1 -- ◦ bullet list 2.2 -- ▪ bullet list 2.2.1 -- ◦ bullet list 2.3 -- • numbered list 3 -- ▪ bullet list 3.0.1 +- bullet list 2.1 +- bullet list 2.2 +- bullet list 2.2.1 +- bullet list 2.3 +- numbered list 3 +- bullet list 3.0.1 ## Heading level 2: B -Contrary to popular belief, Lorem Ipsum is not simply random text . Richard McClintock, a Latin professor at Hampden-Sydney College in Virginia , looked up one of the more obscure Latin words, consectetur, from a Lorem Ipsum passage, and going through the cites of the word in classical literature , discovered the undoubtable source. Lorem Ipsum comes from sections 1.10.32 and 1.10.33 of "de Finibus Bonorum et Malorum" ( The Extremes of Good and Evil ) by Cicero, written in 45 BC. +Contrary to popular belief, Lorem Ipsum is not simply random text. Richard McClintock, a Latin professor at Hampden-Sydney College in Virginia, looked up one of the more obscure Latin words, consectetur, from a Lorem Ipsum passage, and going through the cites of the word in classical literature, discovered the undoubtable source. Lorem Ipsum comes from sections 1.10.32 and 1.10.33 of "de Finibus Bonorum et Malorum" (The Extremes of Good and Evil) by Cicero, written in 45 BC. diff --git a/tests/pdf_snapshots/odf/sources/text_document_02.odt.pdf.md b/tests/pdf_snapshots/odf/sources/text_document_02.odt.pdf.md index f5ecb267..3fbdc330 100644 --- a/tests/pdf_snapshots/odf/sources/text_document_02.odt.pdf.md +++ b/tests/pdf_snapshots/odf/sources/text_document_02.odt.pdf.md @@ -8,10 +8,10 @@ Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Contrary to popular belief, Lorem Ipsum is not simply random text. It has roots in a piece of classical Latin literature from 45 BC, making it over 2000 years old. -| Merged row | Cell 1.3 | Cell 1.4 | Cell 1.5 | Cell 1.6 | Cell 1.7 | -|--------------|-----------------|------------|------------|------------|------------| -| | Merged cell 2x2 | | | Merged | | -| | | | | column | | +| Merged row Cell 1.3 Cell 1.4 Cell 1.5 Cell 1.6 Cell 1.7 | | | +|-----------------------------------------------------------|------------------------|--------| +| | Merged cell 2x2 Merged | | +| | | column | diff --git a/tests/pdf_snapshots/odf/sources/text_document_03.odt.pdf.md b/tests/pdf_snapshots/odf/sources/text_document_03.odt.pdf.md index 324441dd..5965c766 100644 --- a/tests/pdf_snapshots/odf/sources/text_document_03.odt.pdf.md +++ b/tests/pdf_snapshots/odf/sources/text_document_03.odt.pdf.md @@ -1,70 +1,62 @@ ## Table with rich cells -| Column A | | | Column B | | | | | | | | | -|----------------------------------------|----|--------------|---------------------------|----|-----|--------|-----|--------|-------------------|----|----| -| This is a list: | | | This is a formatted list: | | | | | | | | | -| | • | A First | | • | B | First | | | | | | -| | • | A Second | | • | B | Second | | | | | | -| | • | A Third | | • | B | Third | | | | | | -| First Paragraph | | | This is simple text with | | | | | | bold | | | -| | | | | | | | | | | , | | -| | | | strikethrough | | | | and | italic | formatting with x | | 2 | -| Second Paragraph | | | and H | | 2 O | | | | | | | -| Third paragraph before a numbered list | | | | | | | | | | | | -| | 1. | Number one | | | | | | | | | | -| | 2. | Number two | | | | | | | | | | -| | 3. | Number three | | | | | | | | | | -| This is a paragraph | | | | | | | | | | | | -| This is another paragraph | | | | | | | | | | | | +| Column A Column B | | | | +|----------------------------------------|------------------------------------------------|---------------------------------------------|------------| +| This is a list: | • A Third This is a formatted list: | | | +| | • A First | | • B First | +| | • A Second | | • B Second | +| | | | • B Third | +| First Paragraph | 3. Number three This is simple text with bold, | | | +| | | strikethrough and italic formatting with x2 | | +| Second Paragraph | | and H2O | | +| Third paragraph before a numbered list | | | | +| | 1. Number one | | | +| | 2. Number two | | | +| This is a paragraph | | | | +| This is another paragraph | | | | ## Table with nested table Before table -| Column A | | | Column B | | | | | -|------------------------|--------|--------|------------------|--------|-----|--------|--------| -| Simple cell upper left | | | Simple cell with | bold | and | italic | text | -| A | B | C | Rich cell | | | | | -| Cell 1 | Cell 2 | Cell 3 | A nested table | | | | | -| | | | A | B | | | C | -| | | | Cell 1 | Cell 2 | | | Cell 3 | +| Column A Column B | | +|--------------------------------------------------------------|----------------------| +| Simple cell upper left Simple cell with bold and italic text | | +| Cell 1 Cell 2 Cell 3 Rich cell | | +| A B C | | +| | A nested table | +| | A B C | +| | Cell 1 Cell 2 Cell 3 | -After table with bold underline strikethrough , and italic formatting +After table with bold, underline, strikethrough, and italic formatting ## Table with pictures -| Column A | Column B | -|------------------|------------| -| Only text | | -| Text and picture | | +| Column A Column B Only text Text and picture | +|------------------------------------------------| ## Lists with same numId in different cells -| • | Cell 1 item 1 | -|-----|-----------------| -| • | Cell 1 item 2 | -| • | Cell 2 item 1 | -| • | Cell 2 item 2 | +| • Cell 1 item 1 • Cell 1 item 2 • Cell 2 item 1 • Cell 2 item 2 | +|-------------------------------------------------------------------| ## Lists with different numIds in different cells -| • | Cell 1 item 1 | | -|-----|-----------------|---------------| -| • | Cell 1 item 2 | | -| | • | Cell 2 item 1 | -| | • | Cell 2 item 2 | +| • Cell 1 item 1 | | +|-------------------|-----------------| +| • Cell 1 item 2 | | +| | • Cell 2 item 1 | +| | • Cell 2 item 2 | ## Multiple columns with lists -| • | R1C1 item 1 | • | R1C2 item 1 | -|-----|---------------|-----|---------------| -| • | R1C1 item 2 | • | R1C2 item 2 | -| • | R2C1 item 1 | • | R2C2 item 1 | -| • | R2C1 item 2 | • | R2C2 item 2 | +| • R1C1 item 1 • R1C1 item 2 • R1C2 item 1 | | +|---------------------------------------------|---------------| +| | • R1C2 item 2 | +| • R2C1 item 1 • R2C1 item 2 • R2C2 item 1 | | +| | • R2C2 item 2 | ## Mixed content - list and regular text in different cells -| • | List item 1 | -|-----------------------------|---------------| -| • | List item 2 | -| Regular text in second cell | | +| • List item 1 • List item 2 Regular text in second cell | +|-----------------------------------------------------------| diff --git a/tests/pdf_snapshots/pdf/sources/2203.01017v2.pdf.md b/tests/pdf_snapshots/pdf/sources/2203.01017v2.pdf.md index 7cf722c5..49bce6b2 100644 --- a/tests/pdf_snapshots/pdf/sources/2203.01017v2.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/2203.01017v2.pdf.md @@ -2,82 +2,70 @@ Ahmed Nassar, Nikolaos Livathinos, Maksym Lysak, Peter Staar IBM Research -{ ahn,nli,mly,taa } @zurich.ibm.com +{ahn, nli, mly, taa}@zurich.ibm.com ## Abstract -Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables. +Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper , we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First , we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second , we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables. ## 1. Introduction -The occurrence of tables in documents is ubiquitous. They often summarise quantitative or factual data, which is cumbersome to describe in verbose text but nevertheless extremely valuable. Unfortunately, this compact representation is often not easy to parse by machines. There are many implicit conventions used to obtain a compact table representation. For example, tables often have complex columnand row-headers in order to reduce duplicated cell content. Lines of different shapes and sizes are leveraged to separate content or indicate a tree structure. Additionally, tables can also have empty/missing table-entries or multi-row textual table-entries. Fig. 1 shows a table which presents all these issues. +The occurrence of tables in documents is ubiquitous. They often summarise quantitative or factual data, which is cumbersome to describe in verbose text but nevertheless extremely valuable. Unfortunately, this compact representation is often not easy to parse by machines. There are many implicit conventions used to obtain a compact table representation. For example, tables often have complex columnand row-headers in order to reduce duplicated cell content. Lines of different shapes and sizes are leveraged to separate content or indicate a tree structure. Additionally, tables can also have empty/missing table-entries or multi-row textual table-entries. Fig. 1 shows a table which presents all these -## a. Picture of a table: - -Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: 'PMC2944238 004 02'. +Figure 1 : Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename:'PMC294423800402'. -| | | 1 | -|----|----|-----| -| | 3 | | -| 2 | | | - - b. Red-annotation of bounding boxes, Blue-predictions by TableFormer -- c. Structure predicted by TableFormer: - -| 0 | | 1 | | 2 | | 1 | | -|-----|----|-----|----|-----|----|-----|----| -| 3 | | 4 | 3 | 5 | 6 | | 7 | -| | | 9 | | 10 | 11 | | 12 | -| 8 | 2 | 13 | | 14 | 15 | | 16 | -| | | 17 | | 18 | 19 | | 20 | +| 012 | | +|----------|----------| +| 34567 | | +| 89101112 | | +| | 13141516 | Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document. -The first problem is called table-location and has been previously addressed [ 30 38 19 21 23 26 8 ] with stateof-the-art object-detection networks (e.g. YOLO and later , , , , , , on Mask-RCNN [ 9 ]). For all practical purposes, it can be +The first problem is called table-location and has been previously addressed [30, 38, 19, 21, 23, 26, 8] with stateof-the-art object-detection networks (e.g. YOLO and later on Mask-RCNN [9]). For all practical purposes, it can be considered as a solved problem, given enough ground-truth data to train on. -The second problem is called table-structure decomposition. The latter is a long standing problem in the community of document understanding [ 6 , 4 , 14 ]. Contrary to the table-location problem, there are no commonly used approaches that can easily be re-purposed to solve this problem. Lately, a set of new model-architectures has been proposed by the community to address table-structure decomposition [ 37 36 18 20 ]. All these models have some weak, , , nesses (see Sec. 2 ). The common denominator here is the reliance on textual features and/or the inability to provide the bounding box of each table-cell in the original image. +The second problem is called table-structure decomposition. The latter is a long standing problem in the community of document understanding [6, 4, 14]. Contrary to the table-location problem, there are no commonly used approaches that can easily be re-purposed to solve this problem. Lately, a set of new model-architectures has been proposed by the community to address table-structure decomposition [37, 36, 18, 20]. All these models have some weaknesses (see Sec. 2). The common denominator here is the reliance on textual features and/or the inability to provide the bounding box of each table-cell in the original image. In this paper, we want to address these weaknesses and present a robust table-structure decomposition algorithm. The design criteria for our model are the following. First, we want our algorithm to be language agnostic. In this way, we can obtain the structure of any table, irregardless of the language. Second, we want our algorithm to leverage as much data as possible from the original PDF document. For programmatic PDF documents, the text-cells can often be extracted much faster and with higher accuracy compared to OCR methods. Last but not least, we want to have a direct link between the table-cell and its bounding box in the image. -To meet the design criteria listed above, we developed a new model called TableFormer and a synthetically gener1 ated table structure dataset called SynthTabNet . In particular, our contributions in this work can be summarised as follows: - -- • We propose TableFormer , a transformer based model that predicts tables structure and bounding boxes for the table content simultaneously in an end-to-end approach. -- • Across all benchmark datasets TableFormer significantly outperforms existing state-of-the-art metrics, while being much more efficient in training and inference to existing works. -- We present SynthTabNet a synthetically generated • dataset, with various appearance styles and complexity. -- • An augmented dataset based on PubTabNet [ 37 ], FinTabNet [ 36 ], and TableBank [ 17 ] with generated ground-truth for reproducibility. +To meet the design criteria listed above, we developed a new model called TableFormer and a synthetically generated table structure dataset called SynthTabNet1. In particular, our contributions in this work can be summarised as follows: -The paper is structured as follows. In Sec. 2 , we give a brief overview of the current state-of-the-art. In Sec. 3 we describe the datasets on which we train. In Sec. 4 , we , introduce the TableFormer model-architecture and describe +- We propose TableFormer, a transformer based model that predicts tables structure and bounding boxes for the table content simultaneously in an end-to-end approach. +- Across all benchmark datasets TableFormer significantly outperforms existing state-of-the-art metrics, while being much more efficient in training and inference to existing works. +- We present SynthTabNet a synthetically generated dataset, with various appearance styles and complexity. +- An augmented dataset based on PubTabNet [37], FinTabNet [36], and TableBank [17] with generated ground-truth for reproducibility. -1 https://github.com/IBM/SynthTabNet +The paper is structured as follows. In Sec. 2, we give a brief overview of the current state-of-the-art. In Sec. 3, we describe the datasets on which we train. In Sec. 4, we introduce the TableFormer model-architecture and describe -its results & performance in Sec. 5 . As a conclusion, we describe how this new model-architecture can be re-purposed for other tasks in the computer-vision community. +scribe how this new model-architecture can be re-purposed for other tasks in the computer-vision community. ## 2. Previous work and State of the Art -Identifying the structure of a table has been an outstanding problem in the document-parsing community, that motivates many organised public challenges [ 6 , 4 , 14 ]. The difficulty of the problem can be attributed to a number of factors. First, there is a large variety in the shapes and sizes of tables. Such large variety requires a flexible method. This is especially true for complex column- and row headers, which can be extremely intricate and demanding. A second factor of complexity is the lack of data with regard to table-structure. Until the publication of PubTabNet [ 37 ], there were no large datasets (i.e. > 100 K tables) that provided structure information. This happens primarily due to the fact that tables are notoriously time-consuming to annotate by hand. However, this has definitely changed in recent years with the deliverance of PubTabNet [ 37 ], FinTabNet [ 36 ], TableBank [ 17 ] etc. +Identifying the structure of a table has been an outstanding problem in the document-parsing community, that motivates many organised public challenges [6, 4, 14]. The difficulty of the problem can be attributed to a number of factors. First, there is a large variety in the shapes and sizes of tables. Such large variety requires a flexible method. This is especially true for complex column- and row headers, which can be extremely intricate and demanding. A second factor of complexity is the lack of data with regard to table-structure. Until the publication of PubTabNet [37], there were no large datasets (i.e. > 100K tables) that provided structure information. This happens primarily due to the fact that tables are notoriously time-consuming to annotate by hand. However, this has definitely changed in recent years with the deliverance of PubTabNet [37], FinTabNet [36], TableBank [17] etc. -Before the rising popularity of deep neural networks, the community relied heavily on heuristic and/or statistical methods to do table structure identification [ 3 7 11 5 13 28 ]. Although such methods work well on constrained ta, , , , , bles [ 12 ], a more data-driven approach can be applied due to the advent of convolutional neural networks (CNNs) and the availability of large datasets. To the best-of-our knowledge, there are currently two different types of network architecture that are being pursued for state-of-the-art tablestructure identification. +Before the rising popularity of deep neural networks, the community relied heavily on heuristic and/or statistical methods to do table structure identification [3, 7, 11, 5, 13, 28]. Although such methods work well on constrained tables [12], a more data-driven approach can be applied due to the advent of convolutional neural networks (CNNs) and the availability of large datasets. To the best-of-our knowledge, there are currently two different types of network architecture that are being pursued for state-of-the-art tablestructure identification. -Image-to-Text networks : In this type of network, one predicts a sequence of tokens starting from an encoded image. Such sequences of tokens can be HTML table tags [ 37 , 17 ] or LaTeX symbols[ 10 ]. The choice of symbols is ultimately not very important, since one can be transformed into the other. There are however subtle variations in the Image-to-Text networks. The easiest network architectures are "image-encoder → text-decoder" (IETD), similar to network architectures that try to provide captions to images [ 32 ]. In these IETD networks, one expects as output the LaTeX/HTML string of the entire table, i.e. the symbols necessary for creating the table with the content of the table. Another approach is the "image-encoder → dual decoder" (IEDD) networks. In these type of networks, one has two consecutive decoders with different purposes. The first decoder is the tag-decoder , i.e. it only produces the HTML/LaTeX tags which construct an empty table. The second content-decoder uses the encoding of the image in combination with the output encoding of each cell-tag (from the tag-decoder ) to generate the textual content of each table cell. The network architecture of IEDD is certainly more elaborate, but it has the advantage that one can pre-train the +Image-to-Text networks: In this type of network, one predicts a sequence of tokens starting from an encoded image. Such sequences of tokens can be HTML table tags [37, 17] or LaTeX symbols[10]. The choice of symbols is ultimately not very important, since one can be transformed into the other. There are however subtle variations in the Image-to-Text networks. The easiest network architectures are "image-encoder → text-decoder" (IETD), similar to network architectures that try to provide captions to images [32]. In these IETD networks, one expects as output the LaTeX/HTML string of the entire table, i.e. the symbols necessary for creating the table with the content of the table. Another approach is the "image-encoder → dual decoder" (IEDD) networks. In these type of networks, one has two consecutive decoders with different purposes. The first decoder is the tag-decoder, i.e. it only produces the HTML/LaTeX tags which construct an empty table. The second content-decoder uses the encoding of the image in combination with the output encoding of each cell-tag (from the tag-decoder) to generate the textual content of each table cell. The network architecture of IEDD is certainly more elaborate, but it has the advantage that one can pre-train the tag-decoder which is constrained to the table-tags. -In practice, both network architectures (IETD and IEDD) require an implicit, custom trained object-characterrecognition (OCR) to obtain the content of the table-cells. In the case of IETD, this OCR engine is implicit in the decoder similar to [ 24 ]. For the IEDD, the OCR is solely embedded in the content-decoder. This reliance on a custom, implicit OCR decoder is of course problematic. OCR is a well known and extremely tough problem, that often needs custom training for each individual language. However, the limited availability for non-english content in the current datasets, makes it impractical to apply the IETD and IEDD methods on tables with other languages. Additionally, OCR can be completely omitted if the tables originate from programmatic PDF documents with known positions of each cell. The latter was the inspiration for the work of this paper. +In practice, both network architectures (IETD and IEDD) require an implicit, custom trained object-characterrecognition (OCR) to obtain the content of the table-cells. In the case of IETD, this OCR engine is implicit in the decoder similar to [24]. For the IEDD, the OCR is solely embedded in the content-decoder. This reliance on a custom, implicit OCR decoder is of course problematic. OCR is a well known and extremely tough problem, that often needs custom training for each individual language. However, the limited availability for non-english content in the current datasets, makes it impractical to apply the IETD and IEDD methods on tables with other languages. Additionally, OCR can be completely omitted if the tables originate from programmatic PDF documents with known positions of each cell. The latter was the inspiration for the work of this paper. -Graph Neural networks : Graph Neural networks (GNN's) take a radically different approach to tablestructure extraction. Note that one table cell can constitute out of multiple text-cells. To obtain the table-structure, one creates an initial graph, where each of the text-cells becomes a node in the graph similar to [ 33 , 34 , 2 ]. Each node is then associated with en embedding vector coming from the encoded image, its coordinates and the encoded text. Furthermore, nodes that represent adjacent text-cells are linked. Graph Convolutional Networks (GCN's) based methods take the image as an input, but also the position of the text-cells and their content [ 18 ]. The purpose of a GCN is to transform the input graph into a new graph, which replaces the old links with new ones. The new links then represent the table-structure. With this approach, one can avoid the need to build custom OCR decoders. However, the quality of the reconstructed structure is not comparable to the current state-of-the-art [ 18 ]. +Graph Neural networks: Graph Neural networks (GNN's) take a radically different approach to tablestructure extraction. Note that one table cell can constitute out of multiple text-cells. To obtain the table-structure, one creates an initial graph, where each of the text-cells becomes a node in the graph similar to [33, 34, 2]. Each node is then associated with en embedding vector coming from the encoded image, its coordinates and the encoded text. Furthermore, nodes that represent adjacent text-cells are linked. Graph Convolutional Networks (GCN's) based methods take the image as an input, but also the position of the text-cells and their content [18]. The purpose of a GCN is to transform the input graph into a new graph, which replaces the old links with new ones. The new links then represent the table-structure. With this approach, one can avoid the need to build custom OCR decoders. However, the quality of the reconstructed structure is not comparable to the current state-of-the-art [18]. -Hybrid Deep Learning-Rule-Based approach : A popular current model for table-structure identification is the use of a hybrid Deep Learning-Rule-Based approach similar to [ 27 29 ]. In this approach, one first detects the position of the table-cells with object detection (e.g. YoloVx or Mask, RCNN), then classifies the table into different types (from its images) and finally uses different rule-sets to obtain its table-structure. Currently, this approach achieves stateof-the-art results, but is not an end-to-end deep-learning method. As such, new rules need to be written if different types of tables are encountered. +Hybrid Deep Learning-Rule-Based approach: A popular current model for table-structure identification is the use of a hybrid Deep Learning-Rule-Based approach similar to [27, 29]. In this approach, one first detects the position of the table-cells with object detection (e.g. YoloVx or MaskRCNN), then classifies the table into different types (from its images) and finally uses different rule-sets to obtain its table-structure. Currently, this approach achieves stateof-the-art results, but is not an end-to-end deep-learning method. As such, new rules need to be written if different types of tables are encountered. ## 3. Datasets -We rely on large-scale datasets such as PubTabNet [ 37 ], FinTabNet [ 36 ], and TableBank [ 17 ] datasets to train and evaluate our models. These datasets span over various appearance styles and content. We also introduce our own synthetically generated SynthTabNet dataset to fix an im +We rely on large-scale datasets such as PubTabNet [37], FinTabNet [36], and TableBank [17] datasets to train and evaluate our models. These datasets span over various appearance styles and content. We also introduce our own Figure 2: Distribution of the tables across different table dimensions in PubTabNet + FinTabNet datasets @@ -89,26 +77,20 @@ The PubTabNet dataset contains 509k tables delivered as annotated PNG images. Th Due to the heterogeneity across the dataset formats, it was necessary to combine all available data into one homogenized dataset before we could train our models for practical purposes. Given the size of PubTabNet, we adopted its annotation format and we extracted and converted all tables as PNG images with a resolution of 72 dpi. Additionally, we have filtered out tables with extreme sizes due to small -amount of such tables, and kept only those ones ranging between 1*1 and 20*10 (rows/columns). +amount of such tables, and kept only those ones ranging between 1 * 1 and 20* 10 (rows/columns). The availability of the bounding boxes for all table cells is essential to train our models. In order to distinguish between empty and non-empty bounding boxes, we have introduced a binary class in the annotation. Unfortunately, the original datasets either omit the bounding boxes for whole tables (e.g. TableBank) or they narrow their scope only to non-empty cells. Therefore, it was imperative to introduce a data pre-processing procedure that generates the missing bounding boxes out of the annotation information. This procedure first parses the provided table structure and calculates the dimensions of the most fine-grained grid that covers the table structure. Notice that each table cell may occupy multiple grid squares due to row or column spans. In case of PubTabNet we had to compute missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes. -As it is illustrated in Fig. 2 , the table distributions from all datasets are skewed towards simpler structures with fewer number of rows/columns. Additionally, there is very limited variance in the table styles, which in case of PubTabNet and FinTabNet means one styling format for the majority of the tables. Similar limitations appear also in the type of table content, which in some cases (e.g. FinTabNet) is restricted to a certain domain. Ultimately, the lack of diversity in the training dataset damages the ability of the models to generalize well on unseen data. +As it is illustrated in Fig. 2, the table distributions from all datasets are skewed towards simpler structures with fewer number of rows/columns. Additionally, there is very limited variance in the table styles, which in case of PubTabNet and FinTabNet means one styling format for the majority of the tables. Similar limitations appear also in the type of table content, which in some cases (e.g. FinTabNet) is restricted to a certain domain. Ultimately, the lack of diversity in the training dataset damages the ability of the models to generalize well on unseen data. -Motivated by those observations we aimed at generating a synthetic table dataset named SynthTabNet . This approach offers control over: 1) the size of the dataset, 2) the table structure, 3) the table style and 4) the type of content. The complexity of the table structure is described by the size of the table header and the table body, as well as the percentage of the table cells covered by row spans and column spans. A set of carefully designed styling templates provides the basis to build a wide range of table appearances. Lastly, the table content is generated out of a curated collection of text corpora. By controlling the size and scope of the synthetic datasets we are able to train and evaluate our models in a variety of different conditions. For example, we can first generate a highly diverse dataset to train our models and then evaluate their performance on other synthetic datasets which are focused on a specific domain. +Motivated by those observations we aimed at generating a synthetic table dataset named SynthTabNet. This approach offers control over: 1) the size of the dataset, 2) the table structure, 3) the table style and 4) the type of content. The complexity of the table structure is described by the size of the table header and the table body, as well as the percentage of the table cells covered by row spans and column spans. A set of carefully designed styling templates provides the basis to build a wide range of table appearances. Lastly, the table content is generated out of a curated collection of text corpora. By controlling the size and scope of the synthetic datasets we are able to train and evaluate our models in a variety of different conditions. For example, we can first generate a highly diverse dataset to train our models and then evaluate their performance on other synthetic datasets which are focused on a specific domain. -In this regard, we have prepared four synthetic datasets, each one containing 150k examples. The corpora to generate the table text consists of the most frequent terms appearing in PubTabNet and FinTabNet together with randomly generated text. The first two synthetic datasets have been fine-tuned to mimic the appearance of the original datasets but encompass more complicated table structures. The third +In this regard, we have prepared four synthetic datasets, each one containing 150k examples. The corpora to generate the table text consists of the most frequent terms appearing in PubTabNet and FinTabNet together with randomly generated text. The first two synthetic datasets have been fine-tuned to mimic the appearance of the original datasets -| | Tags Bbox Size Format | | | -|--------------------|-------------------------|----|-----------| -| PubTabNet | ✓ | ✓ | 509k PNG | -| FinTabNet | ✓ | ✓ | 112k PDF | -| TableBank | ✓ | ✗ | 145k JPEG | -| Combined-Tabnet(*) | ✓ | ✓ | 400k PNG | -| Combined(**) | ✓ | ✓ | 500k PNG | -| SynthTabNet | ✓ | ✓ | 600k PNG | +| PubTabNet ✓ ✓ 509k PNG FinTabNet ✓ ✓ 112k PDF TableBank ✓ ✗ 145k JPEG Combined-Tabnet(*) ✓ ✓ 400k PNG Combined(**) ✓ ✓ 500k PNG SynthTabNet ✓ ✓ 600k PNG | +|------------------------------------------------------------------------------------------------------------------------------------------------------------| -Table 1: Both "Combined-Tabnet" and "CombinedTabnet" are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank. +Table 1 : Both "Combined-Tabnet" and "CombinedTabnet" are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank. one adopts a colorful appearance with high contrast and the last one contains tables with sparse content. Lastly, we have combined all synthetic datasets into one big unified synthetic dataset of 600k examples. @@ -116,11 +98,11 @@ Tab. 1 summarizes the various attributes of the datasets. ## 4. The TableFormer model -Given the image of a table, TableFormer is able to predict: 1) a sequence of tokens that represent the structure of a table, and 2) a bounding box coupled to a subset of those tokens. The conversion of an image into a sequence of tokens is a well-known task [ 35 16 ]. While attention is often , used as an implicit method to associate each token of the sequence with a position in the original image, an explicit association between the individual table-cells and the image bounding boxes is also required. +Given the image of a table, TableFormer is able to predict: 1) a sequence of tokens that represent the structure of a table, and 2) a bounding box coupled to a subset of those tokens. The conversion of an image into a sequence of tokens is a well-known task [35, 16]. While attention is often used as an implicit method to associate each token of the sequence with a position in the original image, an explicit association between the individual table-cells and the image bounding boxes is also required. ## 4.1. Model architecture. -We now describe in detail the proposed method, which is composed of three main components, see Fig. 4 . Our CNN Backbone Network encodes the input as a feature vector of predefined length. The input feature vector of the encoded image is passed to the Structure Decoder to produce a sequence of HTML tags that represent the structure of the table. With each prediction of an HTML standard data cell (' < td > ') the hidden state of that cell is passed to the Cell BBox Decoder. As for spanning cells, such as row or column span, the tag is broken down to ' < ', 'rowspan= ' or 'colspan= ', with the number of spanning cells (attribute), and ' > '. The hidden state attached to ' < ' is passed to the Cell BBox Decoder. A shared feed forward network (FFN) receives the hidden states from the Structure Decoder, to provide the final detection predictions of the bounding box coordinates and their classification. +We now describe in detail the proposed method, which is composed of three main components, see Fig. 4. Our CNN Backbone Network encodes the input as a feature vector of predefined length. The input feature vector of the encoded image is passed to the Structure Decoder to produce a sequence of HTML tags that represent the structure of the table. With each prediction of an HTML standard data cell ('') the hidden state of that cell is passed to the Cell BBox Decoder. As for spanning cells, such as row or column span, the tag is broken down to'<','rowspan=' or' colspan=', with the number of spanning cells (attribute), and'>'. The hidden state attached to'<' is passed to the Cell BBox Decoder. A shared feed forward network (FFN) receives the hidden states from the Structure Decoder, to provide the final detection predictions of the bounding box coordinates and their classification. CNN Backbone Network. A ResNet-18 CNN is the backbone that receives the table image and encodes it as a vector of predefined length. The network has been modified by removing the linear and pooling layer, as we are not per @@ -128,17 +110,17 @@ Figure 3: TableFormer takes in an image of the PDF and creates bounding box and -Figure 4: Given an input image of a table, the Encoder produces fixed-length features that represent the input image. The features are then passed to both the Structure Decoder and Cell BBox Decoder . During training, the Structure Decoder receives 'tokenized tags' of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. Simultaneously, the Cell BBox Decoder selects features referring to the data cells (' < td > ', ' < ') and passes them through an attention network, an MLP, and a linear layer to predict the bounding boxes. +Figure 4: Given an input image of a table, the Encoder produces fixed-length features that represent the input image. The features are then passed to both the Structure Decoder and Cell BBox Decoder. During training, the Structure Decoder receives'tokenized tags' of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. Simultaneously, the Cell BBox Decoder selects features referring to the data cells ('','<') and passes them through an attention network, an MLP , and a -forming classification, and adding an adaptive pooling layer of size 28*28. ResNet by default downsamples the image resolution by 32 and then the encoded image is provided to both the Structure Decoder , and Cell BBox Decoder . +of size 28*28. ResNet by default downsamples the image resolution by 32 and then the encoded image is provided to both the Structure Decoder, and Cell BBox Decoder. -Structure Decoder. The transformer architecture of this component is based on the work proposed in [ 31 ]. After extensive experimentation, the Structure Decoder is modeled as a transformer encoder with two encoder layers and a transformer decoder made from a stack of 4 decoder layers that comprise mainly of multi-head attention and feed forward layers. This configuration uses fewer layers and heads in comparison to networks applied to other problems (e.g. "Scene Understanding", "Image Captioning"), something which we relate to the simplicity of table images. +Structure Decoder. The transformer architecture of this component is based on the work proposed in [31]. After extensive experimentation, the Structure Decoder is modeled as a transformer encoder with two encoder layers and a transformer decoder made from a stack of 4 decoder layers that comprise mainly of multi-head attention and feed forward layers. This configuration uses fewer layers and heads in comparison to networks applied to other problems (e.g. "Scene Understanding", "Image Captioning"), something which we relate to the simplicity of table images. The transformer encoder receives an encoded image from the CNN Backbone Network and refines it through a multi-head dot-product attention layer, followed by a Feed Forward Network. During training, the transformer decoder receives as input the output feature produced by the transformer encoder, and the tokenized input of the HTML ground-truth tags. Using a stack of multi-head attention layers, different aspects of the tag sequence could be inferred. This is achieved by each attention head on a layer operating in a different subspace, and then combining altogether their attention score. -Cell BBox Decoder. Our architecture allows to simultaneously predict HTML tags and bounding boxes for each table cell without the need of a separate object detector end to end. This approach is inspired by DETR [ 1 ] which employs a Transformer Encoder, and Decoder that looks for a specific number of object queries (potential object detections). As our model utilizes a transformer architecture, the hidden state of the < td > ' and ' < ' HTML structure tags become the object query. +Cell BBox Decoder. Our architecture allows to simultaneously predict HTML tags and bounding boxes for each table cell without the need of a separate object detector end to end. This approach is inspired by DETR [1] which employs a Transformer Encoder, and Decoder that looks for a specific number of object queries (potential object detections). As our model utilizes a transformer architecture, the hidden state of the ' and'<' HTML structure tags become the object query. The encoding generated by the CNN Backbone Network along with the features acquired for every data cell from the Transformer Decoder are then passed to the attention network. The attention network takes both inputs and learns to provide an attention weighted encoding. This weighted at @@ -146,31 +128,31 @@ tention encoding is then multiplied to the encoded image to produce a feature fo The output features for each table cell are then fed into the feed-forward network (FFN). The FFN consists of a Multi-Layer Perceptron (3 layers with ReLU activation function) that predicts the normalized coordinates for the bounding box of each table cell. Finally, the predicted bounding boxes are classified based on whether they are empty or not using a linear layer. -Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as l s ) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as l box . l box consists of the generally used l 1 loss for object detection and the IoU loss ( l ) to be scale invariant as explained in [ 25 ]. In iou comparison to DETR, we do not use the Hungarian algorithm [ 15 ] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-toone match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as input to the Cell BBox Decoder , and 2) Our bounding boxes generation mechanism (see Sec. 3 ) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets. +Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as ls) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as lbox. lbox consists of the generally used l1 loss for object detection and the IoU loss (liou) to be scale invariant as explained in [25]. In comparison to DETR, we do not use the Hungarian algorithm [15] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-toone match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as input to the Cell BBox Decoder, and 2) Our bounding boxes generation mechanism (see Sec. 3) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets. -The loss used to train the TableFormer can be defined as following: +The loss used to train the TableFormer can be defined as -where λ ∈ [0, 1], and λ , λ ∈ R are hyper-parameters. iou l 1 +where λ ∈ [0, 1], and λiou, λl1 ∈ R are hyper-parameters. ## 5. Experimental Results ## 5.1. Implementation Details -TableFormer uses ResNet-18 as the CNN Backbone Network . The input images are resized to 448*448 pixels and the feature map has a dimension of 28*28. Additionally, we enforce the following input constraints: +TableFormer uses ResNet-18 as the CNN Backbone Network. The input images are resized to 448*448 pixels and the feature map has a dimension of 28*28. Additionally, we enforce the following input constraints: -Although input constraints are used also by other methods, such as EDD, ours are less restrictive due to the improved +Although input constraints are used also by other methods, -runtime performance and lower memory footprint of TableFormer. This allows to utilize input samples with longer sequences and images with larger dimensions. +Former. This allows to utilize input samples with longer sequences and images with larger dimensions. The Transformer Encoder consists of two "Transformer Encoder Layers", with an input feature size of 512, feed forward network of 1024, and 4 attention heads. As for the Transformer Decoder it is composed of four "Transformer Decoder Layers" with similar input and output dimensions as the "Transformer Encoder Layers". Even though our model uses fewer layers and heads than the default implementation parameters, our extensive experimentation has proved this setup to be more suitable for table images. We attribute this finding to the inherent design of table images, which contain mostly lines and text, unlike the more elaborate content present in other scopes (e.g. the COCO dataset). Moreover, we have added ResNet blocks to the inputs of the Structure Decoder and Cell BBox Decoder. This prevents a decoder having a stronger influence over the learned weights which would damage the other prediction task (structure vs bounding boxes), but learn task specific weights instead. Lastly our dropout layers are set to 0.5. -For training, TableFormer is trained with 3 Adam optimizers, each one for the CNN Backbone Network , Structure Decoder , and Cell BBox Decoder . Taking the PubTabNet as an example for our parameter set up, the initializing learning rate is 0.001 for 12 epochs with a batch size of 24, and λ set to 0.5. Afterwards, we reduce the learning rate to 0.0001, the batch size to 18 and train for 12 more epochs or convergence. +For training, TableFormer is trained with 3 Adam optimizers, each one for the CNN Backbone Network, Structure Decoder, and Cell BBox Decoder. Taking the PubTabNet as an example for our parameter set up, the initializing learning rate is 0.001 for 12 epochs with a batch size of 24, and λ set to 0.5. Afterwards, we reduce the learning rate to 0.0001, the batch size to 18 and train for 12 more epochs or convergence. -TableFormer is implemented with PyTorch and Torchvision libraries [ 22 ]. To speed up the inference, the image undergoes a single forward pass through the CNN Backbone Network and transformer encoder. This eliminates the overhead of generating the same features for each decoding step. Similarly, we employ a 'caching' technique to preform faster autoregressive decoding. This is achieved by storing the features of decoded tokens so we can reuse them for each time step. Therefore, we only compute the attention for each new tag. +TableFormer is implemented with PyTorch and Torchvision libraries [22]. To speed up the inference, the image undergoes a single forward pass through the CNN Backbone Network and transformer encoder. This eliminates the overhead of generating the same features for each decoding step. Similarly, we employ a'caching' technique to preform faster autoregressive decoding. This is achieved by storing the features of decoded tokens so we can reuse them for each time step. Therefore, we only compute the attention for each new tag. ## 5.2. Generalization @@ -180,135 +162,120 @@ We also share our baseline results on the challenging SynthTabNet dataset. Throu ## 5.3. Datasets and Metrics -The Tree-Edit-Distance-Based Similarity (TEDS) metric was introduced in [ 37 ]. It represents the prediction, and ground-truth as a tree structure of HTML tags. This similarity is calculated as: +The Tree-Edit-Distance-Based Similarity (TEDS) metric was introduced in [37]. It represents the prediction, and ground-truth as a tree structure of HTML tags. This similarity is calculated as: -where Ta and Tb represent tables in tree structure HTML format. EditDist denotes the tree-edit distance, and Ta Tb | T | represents the number of nodes in T . +where Ta and Tb represent tables in tree structure HTML format. EditDist denotes the tree-edit distance, and |T| represents the number of nodes in T. ## 5.4. Quantitative Analysis -Structure. As shown in Tab. 2 , TableFormer outperforms all SOTA methods across different datasets by a large margin for predicting the table structure from an image. All the more, our model outperforms pre-trained methods. During the evaluation we do not apply any table filtering. We also provide our baseline results on the SynthTabNet dataset. It has been observed that large tables (e.g. tables that occupy half of the page or more) yield poor predictions. We attribute this issue to the image resizing during the preprocessing step, that produces downsampled images with indistinguishable features. This problem can be addressed by treating such big tables with a separate model which accepts a large input image size. - -| | | | TEDS | | -|--------------------------------|-------------------------|----------------------------|--------|-------| -| | Model | | | | -| | | Dataset Simple Complex All | | | -| | EDD PTN 91.1 88.7 89.9 | | | | -| | GTE PTN - - 93.01 | | | | -| TableFormer PTN 98.5 95.0 | | | | 96.75 | -| | EDD FTN 88.4 92.08 90.6 | | | | -| | GTE FTN - - 87.14 | | | | -| GTE (FT) FTN - - 91.02 | | | | | -| TableFormer FTN 97.5 96.0 | | | | 96.8 | -| | EDD TB 86.0 - 86.0 | | | | -| TableFormer TB 89.6 - | | | | 89.6 | -| TableFormer STN 96.9 95.7 96.7 | | | | | +Structure. As shown in Tab. 2, TableFormer outperforms all SOTA methods across different datasets by a large margin for predicting the table structure from an image. All the more, our model outperforms pre-trained methods. During the evaluation we do not apply any table filtering. We also provide our baseline results on the SynthTabNet dataset. It has been observed that large tables (e.g. tables that occupy half of the page or more) yield poor predictions. We attribute this issue to the image resizing during the preprocessing step, that produces downsampled images with indistinguishable features. This problem can be addressed by treating such big tables with a separate model which accepts a large input image size. + +| | Model TEDS | | +|-------------------------------|-----------------------|----------------------------| +| | | Dataset Simple Complex All | +| | EDD PTN 91.188.789.9 | | +| | GTE PTN - - 93.01 | | +| TableFormer PTN 98.595.096.75 | | | +| | EDD FTN 88.492.0890.6 | | +| | GTE FTN - - 87.14 | | +| GTE (FT) FTN - - 91.02 | | | +| TableFormer FTN 97.596.096.8 | | | +| | EDD TB 86.0 - 86.0 | | +| TableFormer TB 89.6 - 89.6 | | | +| TableFormer STN 96.995.796.7 | | | Table 2: Structure results on PubTabNet (PTN), FinTabNet (FTN), TableBank (TB) and SynthTabNet (STN). FT: Model was trained on PubTabNet then finetuned. -Cell Detection. Like any object detector, our Cell BBox Detector provides bounding boxes that can be improved with post-processing during inference. We make use of the grid-like structure of tables to refine the predictions. A detailed explanation on the post-processing is available in the supplementary material. As shown in Tab. 3 , we evaluate +Cell Detection. Like any object detector, our Cell BBox Detector provides bounding boxes that can be improved with post-processing during inference. We make use of the grid-like structure of tables to refine the predictions. A detailed explanation on the post-processing is available in the -Cell BBox Decoder accuracy for cells with a class laour bel of 'content' only using the PASCAL VOC mAP metric for pre-processing and post-processing. Note that we do not have post-processing results for SynthTabNet as images are only provided. To compare the performance of our proposed approach, we've integrated TableFormer's Cell BBox Decoder into EDD architecture. As mentioned previously, the Structure Decoder provides the Cell BBox Decoder with the features needed to predict the bounding box predictions. Therefore, the accuracy of the Structure Decoder directly influences the accuracy of the Cell BBox Decoder . If the Structure Decoder predicts an extra column, this will result in an extra column of predicted bounding boxes. +bel of'content' only using the PASCAL VOC mAP metric for pre-processing and post-processing. Note that we do not have post-processing results for SynthTabNet as images are only provided. To compare the performance of our proposed approach, we've integrated TableFormer's Cell BBox Decoder into EDD architecture. As mentioned previously, the Structure Decoder provides the Cell BBox Decoder with the features needed to predict the bounding box predictions. Therefore, the accuracy of the Structure Decoder directly influences the accuracy of the Cell BBox Decoder. If the Structure Decoder predicts an extra column, this will result in an extra column of predicted bounding boxes. -| | Model Dataset mAP mAP (PP) | | -|--------------------------------|------------------------------|-----------| -| EDD+BBox PubTabNet 79.2 82.7 | | | -| TableFormer PubTabNet | | 82.1 86.8 | -| TableFormer SynthTabNet 87.7 - | | | +| Model Dataset mAP mAP (PP) EDD+BBox PubTabNet 79.282.7 TableFormer PubTabNet 82.186.8 TableFormer SynthTabNet 87.7 - | +|------------------------------------------------------------------------------------------------------------------------| Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing. Cell Content. In this section, we evaluate the entire pipeline of recovering a table with content. Here we put our approach to test by capitalizing on extracting content from the PDF cells rather than decoding from images. Tab. 4 shows the TEDs score of HTML code representing the structure of the table along with the content inserted in the data cell and compared with the ground-truth. Our method achieved a 5.3% increase over the state-of-the-art, and commercial solutions. We believe our scores would be higher if the HTML ground-truth matched the extracted PDF cell content. Unfortunately, there are small discrepancies such as spacings around words or special characters with various unicode representations. -| | | | TEDS | | -|----------------------------|------------------------|--------------------|--------|------| -| | Model | | | | -| | | Simple Complex All | | | -| | Tabula 78.0 57.8 67.9 | | | | -| Traprange 60.8 49.9 55.4 | | | | | -| | Camelot 80.0 66.0 73.0 | | | | -| Acrobat Pro 68.9 61.8 65.3 | | | | | -| | EDD 91.2 85.4 88.3 | | | | -| TableFormer 95.4 90.1 | | | | 93.6 | +| | Model TEDS | | +|--------------------------|----------------------|--------------------| +| | | Simple Complex All | +| | Tabula 78.057.867.9 | | +| Traprange 60.849.955.4 | | | +| | Camelot 80.066.073.0 | | +| Acrobat Pro 68.961.865.3 | | | +| | EDD 91.285.488.3 | | +| TableFormer 95.490.193.6 | | | Table 4: Results of structure with content retrieved using cell detection on PubTabNet. In all cases the input is PDF documents with cropped tables. -a. Red - PDF cells, Green - predicted bounding boxes, Blue - post-processed predictions matched to PDF cells Japanese language (previously unseen by TableFormer): Example table from FinTabNet: - -- a. Red - PDF cells, Green - predicted bounding boxes, Blue - post-processed predictions matched to PDF cells - -Japanese language (previously unseen by TableFormer): - Figure 5: One of the benefits of TableFormer is that it is language agnostic, as an example, the left part of the illustration demonstrates TableFormer predictions on previously unseen language (Japanese). Additionally, we see that TableFormer is robust to variability in style and content, right side of the illustration shows the example of the TableFormer prediction from the FinTabNet dataset. -Text is aligned to match original for ease of viewing - Figure 6: An example of TableFormer predictions (bounding boxes and structure) from generated SynthTabNet table. -## 6. Future Work & Conclusion - ## 5.5. Qualitative Analysis In this paper, we presented TableFormer an end-to-end transformer based approach to predict table structures and bounding boxes of cells from an image. This approach enables us to recreate the table structure, and extract the cell content from PDF or OCR by using bounding boxes. Additionally, it provides the versatility required in real-world scenarios when dealing with various types of PDF documents, and languages. Furthermore, our method outperforms all state-of-the-arts with a wide margin. Finally, we introduce "SynthTabNet" a challenging synthetically generated dataset that reinforces missing characteristics from other datasets. -We showcase several visualizations for the different components of our network on various "complex" tables within datasets presented in this work in Fig. 5 and Fig. 6 As it is shown, our model is able to predict bounding boxes for all table cells, even for the empty ones. Additionally, our post-processing techniques can extract the cell content by matching the predicted bounding boxes to the PDF cells based on their overlap and spatial proximity. The left part of Fig. 5 demonstrates also the adaptability of our method to any language, as it can successfully extract Japanese text, although the training set contains only English content. We provide more visualizations including the intermediate steps in the supplementary material. Overall these illustrations justify the versatility of our method across a diverse range of table appearances and content type. +We showcase several visualizations for the different components of our network on various "complex" tables within datasets presented in this work in Fig. 5 and Fig. 6 As it is shown, our model is able to predict bounding boxes for all table cells, even for the empty ones. 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In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4651-4659, 2016. 4 +- [36] Xinyi Zheng, Doug Burdick, Lucian Popa, Peter Zhong, and Nancy Xin Ru Wang. Global table extractor (gte): A framework for joint table identification and cell structure recognition using visual context. Winter Conference for Applications in Computer Vision (WACV), 2021. 2, 3 +- [37] Xu Zhong, Elaheh ShafieiBavani, and Antonio Ji -and evaluation. In Andrea Vedaldi, Horst Bischof, Thomas Brox, and Jan-Michael Frahm, editors, Computer Vision – ECCV 2020 , pages 564–580, Cham, 2020. Springer International Publishing. 2 3 7 , , +Brox, and Jan-Michael Frahm, editors, Computer Vision - ECCV 2020, pages 564-580, Cham, 2020. Springer International Publishing. 2, 3, 7 -- [38] Xu Zhong, Jianbin Tang, and Antonio Jimeno Yepes. Publaynet: Largest dataset ever for document layout analysis. In 2019 International Conference on Document Analysis and Recognition (ICDAR) , pages 1015–1022, 2019. 1 +- [38] Xu Zhong, Jianbin Tang, and Antonio Jimeno Yepes. Publaynet: Largest dataset ever for document layout analysis. In 2019 International Conference on Document Analysis and Recognition (ICDAR), pages 1015-1022, 2019. 1 ## TableFormer: Table Structure Understanding with Transformers Supplementary Material @@ -328,15 +295,15 @@ Figure 7 illustrates the distribution of the tables across different dimensions ## 1.2. Synthetic datasets -Aiming to train and evaluate our models in a broader spectrum of table data we have synthesized four types of datasets. Each one contains tables with different appear +Aiming to train and evaluate our models in a broader spectrum of table data we have synthesized four types of -ances in regard to their size, structure, style and content. Every synthetic dataset contains 150k examples, summing up to 600k synthetic examples. All datasets are divided into Train, Test and Val splits (80%, 10%, 10%). +Every synthetic dataset contains 150k examples, summing up to 600k synthetic examples. All datasets are divided into Train, Test and Val splits (80%, 10%, 10%). The process of generating a synthetic dataset can be decomposed into the following steps: - 1. Prepare styling and content templates: The styling templates have been manually designed and organized into groups of scope specific appearances (e.g. financial data, marketing data, etc.) Additionally, we have prepared curated collections of content templates by extracting the most frequently used terms out of non-synthetic datasets (e.g. PubTabNet, FinTabNet, etc.). - 2. Generate table structures: The structure of each synthetic dataset assumes a horizontal table header which potentially spans over multiple rows and a table body that may contain a combination of row spans and column spans. However, spans are not allowed to cross the header - body boundary. The table structure is described by the parameters: Total number of table rows and columns, number of header rows, type of spans (header only spans, row only spans, column only spans, both row and column spans), maximum span size and the ratio of the table area covered by spans. -- 3. Generate content: Based on the dataset theme , a set of suitable content templates is chosen first. Then, this content can be combined with purely random text to produce the synthetic content. +- 3. Generate content: Based on the dataset theme, a set of suitable content templates is chosen first. Then, this content can be combined with purely random text to produce the synthetic content. - 4. Apply styling templates: Depending on the domain of the synthetic dataset, a set of styling templates is first manually selected. Then, a style is randomly selected to format the appearance of the synthesized table. - 5. Render the complete tables: The synthetic table is finally rendered by a web browser engine to generate the bounding boxes for each table cell. A batching technique is utilized to optimize the runtime overhead of the rendering process. @@ -348,8 +315,8 @@ Figure 7: Distribution of the tables across different dimensions per dataset. Si -- • TableFormer output does not include the table cell content. -- • There are occasional inaccuracies in the predictions of the bounding boxes. +- TableFormer output does not include the table cell content. +- There are occasional inaccuracies in the predictions of the bounding boxes. However, it is possible to mitigate those limitations by combining the TableFormer predictions with the information already present inside a programmatic PDF document. More specifically, PDF documents can be seen as a sequence of PDF cells where each cell is described by its content and bounding box. If we are able to associate the PDF cells with the predicted table cells, we can directly link the PDF cell content to the table cell structure and use the PDF bounding boxes to correct misalignments in the predicted table cell bounding boxes. @@ -363,13 +330,13 @@ Here is a step-by-step description of the prediction postprocessing: -where c is one of { left, centroid, right } and x is the xcoordinate for the corresponding point. c +where c is one of {left, centroid, right} and xc is the xcoordinate for the corresponding point. -- 5. Use the alignment computed in step 4, to compute the median x -coordinate for all table columns and the me +- 5. Use the alignment computed in step 4, to compute -dian cell size for all table cells. The usage of median during the computations, helps to eliminate outliers caused by occasional column spans which are usually wider than the normal. +ing the computations, helps to eliminate outliers caused by occasional column spans which are usually wider than the normal. -- 6. Snap all cells with bad IOU to their corresponding median x -coordinates and cell sizes. +- 6. Snap all cells with bad IOU to their corresponding median x-coordinates and cell sizes. - 7. Generate a new set of pair-wise matches between the corrected bounding boxes and PDF cells. This time use a modified version of the IOU metric, where the area of the intersection between the predicted and PDF cells is divided by the PDF cell area. In case there are multiple matches for the same PDF cell, the prediction with the higher score is preferred. This covers the cases where the PDF cells are smaller than the area of predicted or corrected prediction cells. - 8. In some rare occasions, we have noticed that TableFormer can confuse a single column as two. When the postprocessing steps are applied, this results with two predicted columns pointing to the same PDF column. In such case we must de-duplicate the columns according to highest total column intersection score. - 9. Pick up the remaining orphan cells. There could be cases, when after applying all the previous post-processing steps, some PDF cells could still remain without any match to predicted cells. However, it is still possible to deduce the correct matching for an orphan PDF cell by mapping its bounding box on the geometry of the grid. This mapping decides if the content of the orphan cell will be appended to an already matched table cell, or a new table cell should be created to match with the orphan. @@ -383,13 +350,11 @@ phan cell. 9f. Otherwise create a new structural cell and match it wit the orphan cell. -Aditional images with examples of TableFormer predictions and post-processing can be found below. - -Figure 8: Example of a table with multi-line header. +Aditional images with examples of TableFormer predictions and post- processing can be found below. -Figure 9: Example of a table with big empty distance between cells. +tween cells. @@ -397,28 +362,22 @@ Figure 9: Example of a table with big empty distance between cells. Figure 10: Example of a complex table with empty cells. -Figure 11: Simple table with different style and empty cells. +Figure 11 : Simple table with different style and empty cells. -Figure 12: Simple table predictions and post processing. - -Figure 13: Table predictions example on colorful table. - Figure 14: Example with multi-line text. -Figure 15: Example with triangular table. - -Figure 16: Example of how post-processing helps to restore mis-aligned bounding boxes prediction artifact. +mis-aligned bounding boxes prediction artifact. -Figure 17: Example of long table. End-to-end example from initial PDF cells to prediction of bounding boxes, post processing and prediction of structure. +Figure 17: Example of long table. End-to-end example from initial PDF cells to prediction of bounding boxes, post process diff --git a/tests/pdf_snapshots/pdf/sources/2206.01062.pdf.md b/tests/pdf_snapshots/pdf/sources/2206.01062.pdf.md index cf200820..ef0008f9 100644 --- a/tests/pdf_snapshots/pdf/sources/2206.01062.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/2206.01062.pdf.md @@ -1,32 +1,30 @@ -Birgit Pfitzmann IBM Research Rueschlikon, Switzerland bpf@zurich.ibm.com +Birgit Pfitzmann IBM Research Rueschlikon, Switzerland -Ahmed S. Nassar IBM Research Rueschlikon, Switzerland ahn@zurich.ibm.com +Ahmed S. Nassar IBM Research Rueschlikon, Switzerland ## ABSTRACT -Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet, t, a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis. +Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper , we present DocLayNet , a new , publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis. ## CCS CONCEPTS -• Information systems → Document structure ; • Applied computing → Document analysis ; • Computing methodologies → Machine learning ; Computer vision ; Object detection ; +• Information systems → Document structure; • Applied computing → Document analysis; • Computing methodologies → Machine learning; Computer vision; Object detection; Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). -KDD '22, August 14–18, 2022, Washington, DC, USA +KDD'22 , August 14-18 , 2022 , Washington, DC , USA © 2022 Copyright held by the owner/author(s). ACM ISBN 978-1-4503-9385-0/22/08. -https://doi.org/10.1145/3534678.3539043 - ## DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis -Michele Dolfi IBM Research Rueschlikon, Switzerland dol@zurich.ibm.com +IBM Research Rueschlikon, Switzerland dol@zurich.ibm.com -Christoph Auer IBM Research Rueschlikon, Switzerland cau@zurich.ibm.com +IBM Research Rueschlikon, Switzerland -Peter Staar IBM Research Rueschlikon, Switzerland taa@zurich.ibm.com +IBM Research Rueschlikon, Switzerland taa@zurich.ibm.com @@ -46,27 +44,24 @@ PDF document conversion, layout segmentation, object-detection, data set, Machin ## ACM Reference Format: -Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22), August 14–18, 2022, Washington, DC, USA. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/ 3534678.3539043 +Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar , and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'22), August 14-18 , 2022 , Washington, DC , USA. ACM, New York, NY , USA, 9 pages. https://doi.org/10.1145/ -KDD '22, August 14–18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar +KDD'22, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar ## 1 INTRODUCTION -Despite the substantial improvements achieved with machine-learning (ML) approaches and deep neural networks in recent years, document conversion remains a challenging problem, as demonstrated by the numerous public competitions held on this topic [ 1 – 4 ]. The challenge originates from the huge variability in PDF documents regarding layout, language and formats (scanned, programmatic or a combination of both). Engineering a single ML model that can be applied on all types of documents and provides high-quality layout segmentation remains to this day extremely challenging [ ]. 5 To highlight the variability in document layouts, we show a few example documents from the DocLayNet dataset in Figure 1. - -A key problem in the process of document conversion is to understand the structure of a single document page, i.e. which segments of text should be grouped together in a unit. To train models for this task, there are currently two large datasets available to the community, PubLayNet [ 6 ] and DocBank [ 7 ]. They were introduced in 2019 and 2020 respectively and significantly accelerated the implementation of layout detection and segmentation models due to their sizes of 300K and 500K ground-truth pages. These sizes were achieved by leveraging an automation approach. The benefit of automated ground-truth generation is obvious: one can generate large ground-truth datasets at virtually no cost. However, the automation introduces a constraint on the variability in the dataset, because corresponding structured source data must be available. PubLayNet and DocBank were both generated from scientific document repositories (PubMed and arXiv), which provide XML or LA LAT ATE TEX EX sources. Those scientific documents present a limited variability in their layouts, because they are typeset in uniform templates provided by the publishers. Obviously, documents such as technical manuals, annual company reports, legal text, government tenders, etc. have very different and partially unique layouts. As a consequence, the layout predictions obtained from models trained on PubLayNet or DocBank is very reasonable when applied on scientific documents. However, for more artistic or free-style layouts, we see sub-par prediction quality from these models, which we demonstrate in Section 5. +Despite the substantial improvements achieved with machine-learning (ML) approaches and deep neural networks in recent years, document conversion remains a challenging problem, as demonstrated by the numerous public competitions held on this topic [1-4]. The challenge originates from the huge variability in PDF documents regarding layout, language and formats (scanned, programmatic or a combination of both). Engineering a single ML model that can be applied on all types of documents and provides high-quality layout segmentation remains to this day extremely challenging [5]. To highlight the variability in document layouts, we show a few example documents from the DocLayNet dataset in Figure 1. -In this paper, we present the DocLayNet dataset. It provides pageby-page layout annotation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique document pages, of which a fraction carry double- or triple-annotations. DocLayNet is similar in spirit to PubLayNet and DocBank and will likewise be made available to the public 1 in order to stimulate the document-layout analysis community. It distinguishes itself in the following aspects: +A key problem in the process of document conversion is to understand the structure of a single document page, i.e. which segments of text should be grouped together in a unit. To train models for this task, there are currently two large datasets available to the community, PubLayNet [6] and DocBank [7]. They were introduced in 2019 and 2020 respectively and significantly accelerated the implementation of layout detection and segmentation models due to their sizes of 300K and 500K ground-truth pages. These sizes were achieved by leveraging an automation approach. The benefit of automated ground-truth generation is obvious: one can generate large ground-truth datasets at virtually no cost. However , the automation introduces a constraint on the variability in the dataset, because corresponding structured source data must be available. PubLayNet and DocBank were both generated from scientific document repositories (PubMed and arXiv), which provide XML or LATEX sources. Those scientific documents present a limited variability in their layouts, because they are typeset in uniform templates provided by the publishers. Obviously, documents such as technical manuals, annual company reports, legal text, government tenders, etc. have very different and partially unique layouts. As a consequence, the layout predictions obtained from models trained on PubLayNet or DocBank is very reasonable when applied on scientific documents. However , for more artistic or free-style layouts, we see sub-par prediction quality from these models, which we demonstrate in Section 5. -- (1) Human Annotation : In contrast to PubLayNet and DocBank, we relied on human annotation instead of automation approaches to generate the data set. -- (2) Large Layout Variability : We include diverse and complex layouts from a large variety of public sources. -- (3) Detailed Label Set : We define 11 class labels to distinguish layout features in high detail. PubLayNet provides 5 labels; DocBank provides 13, although not a superset of ours. -- (4) Redundant Annotations : A fraction of the pages in the DocLayNet data set carry more than one human annotation. +In this paper , we present the DocLayNet dataset. It provides pageby-page layout annotation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique document pages, of which a fraction carry double- or triple-annotations. DocLayNet is similar in spirit to PubLayNet and DocBank and will likewise be made available to the public1 in order to stimulate the document-layout analysis community. It distinguishes itself in the following aspects: -1 https://developer.ibm.com/exchanges/data/all/doclaynet - -- This enables experimentation with annotation uncertainty and quality control analysis. -- (5) Pre-defined Train-, Test- & Validation-set : Like DocBank, we provide fixed train-, test- & validation-sets to ensure proportional representation of the class-labels. Further, we prevent leakage of unique layouts across sets, which has a large effect on model accuracy scores. +- (1) Human Annotation: In contrast to PubLayNet and DocBank, we relied on human annotation instead of automation approaches to generate the data set. +- (2) Large Layout Variability: We include diverse and complex layouts from a large variety of public sources. +- (3) Detailed Label Set: We define 11 class labels to distinguish layout features in high detail. PubLayNet provides 5 labels; DocBank provides 13, although not a superset of ours. +- (4) Redundant Annotations: A fraction of the pages in the DocLayNet data set carry more than one human annotation. +- and quality control analysis. +- (5) Pre-defined Train-, Test- & Validation-set: Like DocBank, we provide fixed train-, test- & validation-sets to ensure proportional representation of the class-labels. Further , we prevent leakage of unique layouts across sets, which has a large effect on model accuracy scores. All aspects outlined above are detailed in Section 3. In Section 4, we will elaborate on how we designed and executed this large-scale human annotation campaign. We will also share key insights and lessons learned that might prove helpful for other parties planning to set up annotation campaigns. @@ -74,13 +69,13 @@ In Section 5, we will present baseline accuracy numbers for a variety of object ## 2 RELATED WORK -While early approaches in document-layout analysis used rulebased algorithms and heuristics [ 8 ], the problem is lately addressed with deep learning methods. The most common approach is to leverage object detection models [ 9 – 15 ]. In the last decade, the accuracy and speed of these models has increased dramatically. Furthermore, most state-of-the-art object detection methods can be trained and applied with very little work, thanks to a standardisation effort of the ground-truth data format [ 16 ] and common deep-learning frameworks [ ]. Reference data sets such as PubLayNet [ ] and 17 6 DocBank provide their data in the commonly accepted COCO format [16]. +While early approaches in document-layout analysis used rulebased algorithms and heuristics [8], the problem is lately addressed with deep learning methods. The most common approach is to leverage object detection models [9-15]. In the last decade, the accuracy and speed of these models has increased dramatically. Furthermore, most state-of-the-art object detection methods can be trained and applied with very little work, thanks to a standardisation effort of the ground-truth data format [16] and common deep-learning frameworks [17]. Reference data sets such as PubLayNet [6] and DocBank provide their data in the commonly accepted COCO format [16]. -Lately, new types of ML models for document-layout analysis have emerged in the community [ 18 – 21 ]. These models do not approach the problem of layout analysis purely based on an image representation of the page, as computer vision methods do. Instead, they combine the text tokens and image representation of a page in order to obtain a segmentation. While the reported accuracies appear to be promising, a broadly accepted data format which links geometric and textual features has yet to establish. +Lately, new types of ML models for document-layout analysis have emerged in the community [18-21]. These models do not approach the problem of layout analysis purely based on an image representation of the page, as computer vision methods do. Instead, they combine the text tokens and image representation of a page in order to obtain a segmentation. While the reported accuracies appear to be promising, a broadly accepted data format which links geometric and textual features has yet to establish. ## 3 THE DOCLAYNET DATASET -DocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of human annotations, and 1591 carry three. This amounts to 91104 total annotation instances. The annotations provide layout information in the shape of labeled, rectangular boundingboxes. We define 11 distinct labels for layout features, namely Caption Footnote Formula List-item Page-footer, Page-header, Picture Section-header, , , Table Text, , t, and Title , . Our reasoning for picking this r, r, , r, , particular label set is detailed in Section 4. +DocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of human annotations, and 1591 carry three. This amounts to 91104 total annotation instances. The annotations provide layout information in the shape of labeled, rectangular boundingboxes. We define 11 distinct labels for layout features, namely Caption, Footnote, Formula, List-item, Page-footer , Page-header , Picture, Section-header , Table, Text , and Title. Our reasoning for picking this particular label set is detailed in Section 4. In addition to open intellectual property constraints for the source documents, we required that the documents in DocLayNet adhere to a few conditions. Firstly, we kept scanned documents @@ -88,76 +83,74 @@ Figure 2: Distribution of DocLayNet pages across document categories. -to a minimum, since they introduce difficulties in annotation (see Section 4). As a second condition, we focussed on medium to large documents ( > 10 pages) with technical content, dense in complex tables, figures, plots and captions. Such documents carry a lot of information value, but are often hard to analyse with high accuracy due to their challenging layouts. Counterexamples of documents not included in the dataset are receipts, invoices, hand-written documents or photographs showing "text in the wild". +to a minimum, since they introduce difficulties in annotation (see Section 4). As a second condition, we focussed on medium to large documents (> 10 pages) with technical content, dense in complex tables, figures, plots and captions. Such documents carry a lot of information value, but are often hard to analyse with high accuracy due to their challenging layouts. Counterexamples of documents not included in the dataset are receipts, invoices, hand-written documents or photographs showing "text in the wild". -The pages in DocLayNet can be grouped into six distinct categories, namely Financial Reports Manuals Scientific Articles Laws & Regulations Patents and Government Tenders , , . Each document cate, gory was sourced from various repositories. For example, Financial , Reports contain both free-style format annual reports 2 which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories ( Financial Reports and Manuals ) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes. +The pages in DocLayNet can be grouped into six distinct categories, namely Financial Reports, Manuals, Scientific Articles, Laws & Regulations, Patents and Government Tenders. Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports2 which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories (Financial Reports and Manuals) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes. -We did not control the document selection with regard to language. The vast majority of documents contained in DocLayNet (close to 95%) are published in English language. However, DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%). While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features. +We did not control the document selection with regard to language. The vast majority of documents contained in DocLayNet (close to 95%) are published in English language. However , DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%). While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features. To ensure that future benchmarks in the document-layout analysis community can be easily compared, we have split up DocLayNet into pre-defined train-, test- and validation-sets. In this way, we can avoid spurious variations in the evaluation scores due to random splitting in train-, test- and validation-sets. We also ensured that less frequent labels are represented in train and test sets in equal proportions. -2 e.g. AAPL from https://www.annualreports.com/ - -Table 1 shows the overall frequency and distribution of the labels among the different sets. Importantly, we ensure that subsets are only split on full-document boundaries. This avoids that pages of the same document are spread over train, test and validation set, which can give an undesired evaluation advantage to models and lead to overestimation of their prediction accuracy. We will show the impact of this decision in Section 5. +among the different sets. Importantly, we ensure that subsets are only split on full-document boundaries. This avoids that pages of the same document are spread over train, test and validation set, which can give an undesired evaluation advantage to models and lead to overestimation of their prediction accuracy. We will show the impact of this decision in Section 5. -In order to accommodate the different types of models currently in use by the community, we provide DocLayNet in an augmented COCO format [ 16 ]. This entails the standard COCO ground-truth file (in JSON format) with the associated page images (in PNG format, 1025 × 1025 pixels). Furthermore, custom fields have been added to each COCO record to specify document category, original document filename and page number. In addition, we also provide the original PDF pages, as well as sidecar files containing parsed PDF text and text-cell coordinates (in JSON). All additional files are linked to the primary page images by their matching filenames. +In order to accommodate the different types of models currently in use by the community, we provide DocLayNet in an augmented COCO format [16]. This entails the standard COCO ground-truth file (in JSON format) with the associated page images (in PNG format, 1025×1025 pixels). Furthermore, custom fields have been added to each COCO record to specify document category, original document filename and page number. In addition, we also provide the original PDF pages, as well as sidecar files containing parsed PDF text and text-cell coordinates (in JSON). All additional files are linked to the primary page images by their matching filenames. Despite being cost-intense and far less scalable than automation, human annotation has several benefits over automated groundtruth generation. The first and most obvious reason to leverage human annotations is the freedom to annotate any type of document without requiring a programmatic source. For most PDF documents, the original source document is not available. The latter is not a hard constraint with human annotation, but it is for automated methods. A second reason to use human annotations is that the latter usually provide a more natural interpretation of the page layout. The human-interpreted layout can significantly deviate from the programmatic layout used in typesetting. For example, "invisible" tables might be used solely for aligning text paragraphs on columns. Such typesetting tricks might be interpreted by automated methods incorrectly as an actual table, while the human annotation will interpret it correctly as Text or other styles. The same applies to multi-line text elements, when authors decided to space them as "invisible" list elements without bullet symbols. A third reason to gather ground-truth through human annotation is to estimate a "natural" upper bound on the segmentation accuracy. As we will show in Section 4, certain documents featuring complex layouts can have different but equally acceptable layout interpretations. This natural upper bound for segmentation accuracy can be found by annotating the same pages multiple times by different people and evaluating the inter-annotator agreement. Such a baseline consistency evaluation is very useful to define expectations for a good target accuracy in trained deep neural network models and avoid overfitting (see Table 1). On the flip side, achieving high annotation consistency proved to be a key challenge in human annotation, as we outline in Section 4. ## 4 ANNOTATION CAMPAIGN -The annotation campaign was carried out in four phases. In phase one, we identified and prepared the data sources for annotation. In phase two, we determined the class labels and how annotations should be done on the documents in order to obtain maximum consistency. The latter was guided by a detailed requirement analysis and exhaustive experiments. In phase three, we trained the annotation staff and performed exams for quality assurance. In phase four, - -Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row "Total") in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges. - -| | | | | | % of Total | | triple inter-annotator mAP @ 0.5-0.95 (%) | -|----------------|---------|-------|--------------------|-------------------|--------------|--------------------------------------------|---------------------------------------------| -| class label | Count | | Train Test Val | | | All Fin Man Sci Law Pat Ten | | -| Caption | | 22524 | | 2.04 1.77 2.32 | | 84-89 40-61 86-92 94-99 95-99 69-78 n/a | | -| Footnote | | 6318 | | 0.60 0.31 0.58 | | 83-91 n/a 100 62-88 85-94 n/a 82-97 | | -| Formula | | 25027 | | 2.25 1.90 2.96 | | 83-85 n/a n/a 84-87 86-96 n/a n/a | | -| List-item | 185660 | | | 17.19 13.34 15.82 | | 87-88 74-83 90-92 97-97 81-85 75-88 93-95 | | -| Page-footer | | 70878 | | 6.51 5.58 6.00 | | 93-94 88-90 95-96 100 92-97 100 96-98 | | -| Page-header | | 58022 | | 5.10 6.70 5.06 | | 85-89 66-76 90-94 98-100 91-92 97-99 81-86 | | -| Picture | 45976 | | | 4.21 2.78 5.31 | | 69-71 56-59 82-86 69-82 80-95 66-71 59-76 | | -| Section-header | 142884 | | | 12.60 15.77 12.85 | | 83-84 76-81 90-92 94-95 87-94 69-73 78-86 | | -| Table | | 34733 | | 3.20 2.27 3.60 | | 77-81 75-80 83-86 98-99 58-80 79-84 70-85 | | -| Text | 510377 | | | 45.82 49.28 45.00 | | 84-86 81-86 88-93 89-93 87-92 71-79 87-95 | | -| Title | | 5071 | | 0.47 0.30 0.50 | | 60-72 24-63 50-63 94-100 82-96 68-79 24-56 | | -| Total | 1107470 | | 941123 99816 66531 | | | 82-83 71-74 79-81 89-94 86-91 71-76 68-85 | | +The annotation campaign was carried out in four phases. In phase one, we identified and prepared the data sources for annotation. In phase two, we determined the class labels and how annotations should be done on the documents in order to obtain maximum consistency. The latter was guided by a detailed requirement analysis and exhaustive experiments. In phase three, we trained the annotation staff and performed exams for quality assurance. In phase four , + +Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as% of row "Total") in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges. + +| | % of Total triple inter-annotator mAP @ 0.5-0.95 (%) | +|-------------------------------------------------------------------------|--------------------------------------------------------| +| class label Count Train Test Val All Fin Man Sci Law Pat Ten | | +| Caption 225242.041.772.3284-8940-6186-9294-9995-9969-78 n/a | | +| Footnote 63180.600.310.5883-91 n/a 10062-8885-94 n/a 82-97 | | +| Formula 250272.251.902.9683-85 n/a n/a 84-8786-96 n/a n/a | | +| List-item 18566017.1913.3415.8287-8874-8390-9297-9781-8575-8893-95 | | +| Page-footer 708786.515.586.0093-9488-9095-9610092-9710096-98 | | +| Page-header 580225.106.705.0685-8966-7690-9498-10091-9297-9981-86 | | +| Picture 459764.212.785.3169-7156-5982-8669-8280-9566-7159-76 | | +| Section-header 14288412.6015.7712.8583-8476-8190-9294-9587-9469-7378-86 | | +| Table 347333.202.273.6077-8175-8083-8698-9958-8079-8470-85 | | +| Text 51037745.8249.2845.0084-8681-8688-9389-9387-9271-7987-95 | | +| Title 50710.470.300.5060-7224-6350-6394-10082-9668-7924-56 | | +| Total 1107470941123998166653182-8371-7479-8189-9486-9171-7668-85 | | Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right. -include publication repositories such as arXiv 3 , government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process. +company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process. -Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [ 22 ], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains. +Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains. -Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption Footnote Formula List-item Page, , , , footer, r, Page-header, r, Picture , Section-header, r, Table , Text, t, and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on +Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption, Footnote, Formula, List-item, Pagefooter , Page-header , Picture, Section-header , Table, Text , and Title. Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation, as seen in DocBank, are often only distinguishable by discriminating on -we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised. +we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four , a group of 40 dedicated annotators were assembled and supervised. -Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources +Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went -3 https://arxiv.org/ +3https://arxiv.org/ the textual content of an element, which goes beyond visual layout recognition, in particular outside the Scientific Articles category. -At first sight, the task of visual document-layout interpretation appears intuitive enough to obtain plausible annotations in most cases. However, during early trial-runs in the core team, we observed many cases in which annotators use different annotation styles, especially for documents with challenging layouts. For example, if a figure is presented with subfigures, one annotator might draw a single figure bounding-box, while another might annotate each subfigure separately. The same applies for lists, where one might annotate all list items in one block or each list item separately. In essence, we observed that challenging layouts would be annotated in different but plausible ways. To illustrate this, we show in Figure 4 multiple examples of plausible but inconsistent annotations on the same pages. +At first sight, the task of visual document-layout interpretation appears intuitive enough to obtain plausible annotations in most cases. However , during early trial-runs in the core team, we observed many cases in which annotators use different annotation styles, especially for documents with challenging layouts. For example, if a figure is presented with subfigures, one annotator might draw a single figure bounding-box, while another might annotate each subfigure separately. The same applies for lists, where one might annotate all list items in one block or each list item separately. In essence, we observed that challenging layouts would be annotated in different but plausible ways. To illustrate this, we show in Figure 4 multiple examples of plausible but inconsistent annotations on the same pages. Obviously, this inconsistency in annotations is not desirable for datasets which are intended to be used for model training. To minimise these inconsistencies, we created a detailed annotation guideline. While perfect consistency across 40 annotation staff members is clearly not possible to achieve, we saw a huge improvement in annotation consistency after the introduction of our annotation guideline. A few selected, non-trivial highlights of the guideline are: -- (1) Every list-item is an individual object instance with class label List-item . This definition is different from PubLayNet and DocBank, where all list-items are grouped together into object. one List +- (1) Every list-item is an individual object instance with class label List-item. This definition is different from PubLayNet and DocBank, where all list-items are grouped together into one List object. - (2) A List-item is a paragraph with hanging indentation. Singleline elements can qualify as List-item if the neighbour elements expose hanging indentation. Bullet or enumeration symbols are not a requirement. -- (3) For every Caption , there must be exactly one corresponding Picture Table or . +- (3) For every Caption, there must be exactly one corresponding Picture or Table. - (4) Connected sub-pictures are grouped together in one Picture object. - (5) Formula numbers are included in a Formula object. -- (6) Emphasised text (e.g. in italic or bold) at the beginning of a paragraph is not considered a Section-header, r, unless it appears exclusively on its own line. +- (6) Emphasised text (e.g. in italic or bold) at the beginning of a paragraph is not considered a Section-header , unless it appears exclusively on its own line. The complete annotation guideline is over 100 pages long and a detailed description is obviously out of scope for this paper. Nevertheless, it will be made publicly available alongside with DocLayNet for future reference. -Phase 3: Training. After a first trial with a small group of people, we realised that providing the annotation guideline and a set of random practice pages did not yield the desired quality level for layout annotation. Therefore we prepared a subset of pages with two different complexity levels, each with a practice and an exam part. 974 pages were reference-annotated by one proficient core team member. Annotation staff were then given the task to annotate the same subsets (blinded from the reference). By comparing the annotations of each staff member with the reference annotations, we could quantify how closely their annotations matched the reference. Only after passing two exam levels with high annotation quality, staff were admitted into the production phase. Practice iterations +Phase 3: Training. After a first trial with a small group of people, we realised that providing the annotation guideline and a set of random practice pages did not yield the desired quality level for layout annotation. Therefore we prepared a subset of pages with two different complexity levels, each with a practice and an exam part. 974 pages were reference-annotated by one proficient core team member. Annotation staff were then given the task to annotate the same subsets (blinded from the reference). By comparing the annotations of each staff member with the reference annotations, we could quantify how closely their annotations matched the reference. Only after passing two exam levels with high annotation quality, Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous. @@ -169,27 +162,27 @@ Phase 4: Production annotation. The previously selected 80K pages were annotated Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on DocLayNet test set. The MRCNN (Mask R-CNN) and FRCNN (Faster R-CNN) models with ResNet-50 or ResNet-101 backbone were trained based on the network architectures from the detectron2 model zoo (Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN 3x), with default configurations. The YOLO implementation utilized was YOLOv5x6 [13]. All models were initialised using pre-trained weights from the COCO 2017 dataset. -| | human | MRCNN FRCNN YOLO | -|----------------|---------|---------------------| -| | | R50 R101 R101 v5x6 | -| Caption | 84-89 | 68.4 71.5 70.1 77.7 | -| Footnote | 83-91 | 70.9 71.8 73.7 77.2 | -| Formula | 83-85 | 60.1 63.4 63.5 66.2 | -| List-item | 87-88 | 81.2 80.8 81.0 86.2 | -| Page-footer | 93-94 | 61.6 59.3 58.9 61.1 | -| Page-header | 85-89 | 71.9 70.0 72.0 67.9 | -| Picture | 69-71 | 71.7 72.7 72.0 77.1 | -| Section-header | 83-84 | 67.6 69.3 68.4 74.6 | -| Table | 77-81 | 82.2 82.9 82.2 86.3 | -| Text | 84-86 | 84.6 85.8 85.4 88.1 | -| Title | 60-72 | 76.7 80.4 79.9 82.7 | -| All | 82-83 | 72.4 73.5 73.4 76.8 | - -to avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture . For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity. +| | human MRCNN FRCNN YOLO | | +|--------------------------------------|--------------------------|--------------------| +| | | R50 R101 R101 v5x6 | +| Caption 84-8968.471.570.177.7 | | | +| Footnote 83-9170.971.873.777.2 | | | +| Formula 83-8560.163.463.566.2 | | | +| List-item 87-8881.280.881.086.2 | | | +| Page-footer 93-9461.659.358.961.1 | | | +| Page-header 85-8971.970.072.067.9 | | | +| Picture 69-7171.772.772.077.1 | | | +| Section-header 83-8467.669.368.474.6 | | | +| Table 77-8182.282.982.286.3 | | | +| Text 84-8684.685.885.488.1 | | | +| Title 60-7276.780.479.982.7 | | | +| All 82-8372.473.573.476.8 | | | + +to avoid this at any cost in order to have clear , unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture. For the latter , we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity. ## 5 EXPERIMENTS -The primary goal of DocLayNet is to obtain high-quality ML models capable of accurate document-layout analysis on a wide variety of challenging layouts. As discussed in Section 2, object detection models are currently the easiest to use, due to the standardisation of ground-truth data in COCO format [ 16 ] and the availability of general frameworks such as detectron2 [ 17 ]. Furthermore, baseline numbers in PubLayNet and DocBank were obtained using standard object detection models such as Mask R-CNN and Faster R-CNN. As such, we will relate to these object detection methods in this +The primary goal of DocLayNet is to obtain high-quality ML models capable of accurate document-layout analysis on a wide variety of challenging layouts. As discussed in Section 2, object detection models are currently the easiest to use, due to the standardisation of ground-truth data in COCO format [16] and the availability of general frameworks such as detectron2 [17]. Furthermore, baseline numbers in PubLayNet and DocBank were obtained using standard object detection models such as Mask R-CNN and Faster R-CNN. Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNN network with ResNet50 backbone trained on increasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions. @@ -201,50 +194,25 @@ In this section, we will present several aspects related to the performance of o ## Baselines for Object Detection -In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [ 12 ], Faster R-CNN [ 11 ], and YOLOv5 [ 13 ]. Both training and evaluation were performed on RGB images with dimensions of 1025 × 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text, t, Table and Picture . This is not entirely surprising, as Text, Table and Picture are abundant and the most visually distinctive in a document. t, +In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training and evaluation were performed on RGB images with dimensions of 1025 × 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low , but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text , Table and Picture. This is not entirely surprising, as Text , Table and Picture are abundant and the most visually distinctive in a document. Table 3: Performance of a Mask R-CNN R50 network in mAP@0.5-0.95 scores trained on DocLayNet with different class label sets. The reduced label sets were obtained by either down-mapping or dropping labels. -| Class-count | 11 6 5 4 | -|----------------|----------------------------| -| Caption | 68 Text Text Text | -| Footnote | 71 Text Text Text | -| Formula | 60 Text Text Text | -| List-item | 81 Text 82 Text | -| Page-footer | 62 62 - - | -| Page-header | 72 68 - - | -| Picture | 72 72 72 72 | -| Section-header | 68 67 69 68 | -| Table | 82 83 82 82 | -| Text | 85 84 84 84 | -| Title | 77 Sec.-h. Sec.-h. Sec.-h. | -| Overall | 72 73 78 77 | +| Class-count 11654 Caption 68 Text Text Text Footnote 71 Text Text Text Formula 60 Text Text Text List-item 81 Text 82 Text Page-footer 6262 - - Page-header 7268 - - Picture 72727272 Section-header 68676968 Table 82838282 Text 85848484 Title 77 Sec.-h. Sec.-h. Sec.-h. Overall 72737877 | +|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| ## Learning Curve -One of the fundamental questions related to any dataset is if it is "large enough". To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [ ], or the addition of 23 more document categories and styles. +One of the fundamental questions related to any dataset is if it is "large enough". To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar , depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather , it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles. ## Impact of Class Labels -The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption Text ) or excluding them → from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of - -Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. Naive page-wise split will result in ~ 10% point improvement. - -| Class-count | | 11 | | 5 | -|----------------|----------|------|----------|-----| -| Split | Doc Page | | Doc Page | | -| Caption | 68 83 | | | | -| Footnote | 71 84 | | | | -| Formula | 60 66 | | | | -| List-item | 81 88 | | 82 88 | | -| Page-footer | 62 89 | | | | -| Page-header | 72 90 | | | | -| Picture | 72 82 | | 72 82 | | -| Section-header | 68 83 | | 69 83 | | -| Table | 82 89 | | 82 90 | | -| Text | 85 91 | | 84 90 | | -| Title | 77 81 | | | | -| All | 72 84 | | 78 87 | | +The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption→Text) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However + +document-wise and page-wise split for different label sets. Naive page-wise split will result in ~10% point improve + +| Split Doc Page Doc Page Caption 6883 Footnote 7184 Formula 6066 List-item 81888288 Page-footer 6289 Page-header 7290 Picture 72827282 Section-header 68836983 Table 82898290 Text 85918490 Title 7781 All 72847887 | +|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| lists in PubLayNet (grouped list-items) versus DocLayNet (separate list-items), the label set of size 4 is the closest to PubLayNet, in the assumption that the List is down-mapped to Text in PubLayNet. The results in Table 3 show that the prediction accuracy on the remaining class labels does not change significantly when other classes are merged into them. The overall macro-average improves by around 5%, in particular when Page-footer and Page-header are excluded. @@ -254,40 +222,34 @@ Many documents in DocLayNet have a unique styling. In order to avoid overfitting ## Dataset Comparison -Throughout this paper, we claim that DocLayNet's wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture , +Throughout this paper , we claim that DocLayNet's wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture, -KDD '22, August 14–18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar +KDD'22, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar -Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network across the PubLayNet, DocBank & DocLayNet data-sets. By evaluating on common label classes of each dataset, we observe that the DocLayNet-trained model has much less pronounced variations in performance across all datasets. +Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network across the PubLayNet, DocBank & DocLayNet data-sets. By evaluating on common label classes of each dataset, we observe that the DocLayNet-trained model has much less pronounced variations in performance across -| | | | | Testing on | -|-----------------|-------------|------------|------------|--------------| -| | Training on | labels | PLN DB DLN | | -| | | Figure | 96 43 23 | | -| | | Sec-header | 87 - 32 | | -| PubLayNet (PLN) | | Table | 95 24 49 | | -| | | Text | 96 - 42 | | -| | | total | 93 34 30 | | -| | | Figure | 77 71 31 | | -| DocBank (DB) | | Table | 19 65 22 | | -| | | total | 48 68 27 | | -| | | Figure | 67 51 72 | | -| | | Sec-header | 53 - 68 | | -| DocLayNet (DLN) | | Table | 87 43 82 | | -| | | Text | 77 - 84 | | -| | | total | 59 47 78 | | +| | Training on labels PLN DB DLN | | +|-------------------------------|---------------------------------|--------------------| +| PubLayNet (PLN) Figure 964323 | | Sec-header 87 - 32 | +| | | Table 952449 | +| | | Text 96 - 42 | +| | | total 933430 | +| DocBank (DB) Figure 777131 | | Table 196522 | +| | | total 486827 | +| DocLayNet (DLN) Figure 675172 | | Sec-header 53 - 68 | +| | | Table 874382 | +| | | Text 77 - 84 | +| | | total 594778 | -Section-header, Table and Text. t. Before training, we either mapped r, or excluded DocLayNet's other labels as specified in table 3, and also PubLayNet's List to Text. t. Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text. t. +Section-header , Table and Text. Before training, we either mapped or excluded DocLayNet's other labels as specified in table 3, and also PubLayNet's List to Text. Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text. -For comparison of DocBank with DocLayNet, we trained only and Table clusters of each dataset. We had to exclude on Picture Text because successive paragraphs are often grouped together into a single object in DocBank. This paragraph grouping is incompatible with the individual paragraphs of DocLayNet. As can be seen in Table 5, DocLayNet trained models yield better performance compared to the previous datasets. It is noteworthy that the models trained on PubLayNet and DocBank perform very well on their own test set, but have a much lower performance on the foreign datasets. While this also applies to DocLayNet, the difference is far less pronounced. Thus we conclude that DocLayNet trained models are overall more robust and will produce better results for challenging, unseen layouts. +For comparison of DocBank with DocLayNet, we trained only on Picture and Table clusters of each dataset. We had to exclude Text because successive paragraphs are often grouped together into a single object in DocBank. This paragraph grouping is incompatible with the individual paragraphs of DocLayNet. As can be seen in Table 5, DocLayNet trained models yield better performance compared to the previous datasets. It is noteworthy that the models trained on PubLayNet and DocBank perform very well on their own test set, but have a much lower performance on the foreign datasets. While this also applies to DocLayNet, the difference is far less pronounced. Thus we conclude that DocLayNet trained models are overall more robust and will produce better results for challenging, unseen layouts. ## Example Predictions -To conclude this section, we illustrate the quality of layout predictions one can expect from DocLayNet-trained models by providing a selection of examples without any further post-processing applied. Figure 6 shows selected layout predictions on pages from the test-set of DocLayNet. Results look decent in general across document categories, however one can also observe mistakes such as overlapping clusters of different classes, or entirely missing boxes due to low confidence. - -## 6 CONCLUSION +To conclude this section, we illustrate the quality of layout predictions one can expect from DocLayNet-trained models by providing a selection of examples without any further post-processing applied. Figure 6 shows selected layout predictions on pages from the test-set of DocLayNet. Results look decent in general across document categories, however one can also observe mistakes such as overlapping clusters of different classes, or entirely missing boxes -In this paper, we presented the DocLayNet dataset. It provides the document conversion and layout analysis research community a new and challenging dataset to improve and fine-tune novel ML methods on. In contrast to many other datasets, DocLayNet was created by human annotation in order to obtain reliable layout ground-truth on a wide variety of publication- and typesettingstyles. Including a large proportion of documents outside the scientific publishing domain adds significant value in this respect. +In this paper , we presented the DocLayNet dataset. It provides the document conversion and layout analysis research community a new and challenging dataset to improve and fine-tune novel ML methods on. In contrast to many other datasets, DocLayNet was created by human annotation in order to obtain reliable layout ground-truth on a wide variety of publication- and typesettingstyles. Including a large proportion of documents outside the scientific publishing domain adds significant value in this respect. From the dataset, we have derived on the one hand reference metrics for human performance on document-layout annotation (through double and triple annotations) and on the other hand evaluated the baseline performance of commonly used object detection methods. We also illustrated the impact of various dataset-related aspects on model performance through data-ablation experiments, both from a size and class-label perspective. 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Detectron2, 2019. +- [18] Nikolaos Livathinos, Cesar Berrospi, Maksym Lysak, Viktor Kuropiatnyk, Ahmed Nassar , Andre Carvalho, Michele Dolfi, Christoph Auer , Kasper Dinkla, and Peter W. J. Staar. Robust pdf document conversion using recurrent neural networks. In Proceedings of the 35th Conference on Artificial Intelligence, AAAI, pages 15137- 15145, feb 2021. +- [19] Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, and Ming Zhou. Layoutlm: Pre-training of text and layout for document image understanding. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD, pages 1192-1200, New York, USA, 2020. Asso +- Fusion of visual and text features for document layout analysis, 2021. - [21] Peng Zhang, Can Li, Liang Qiao, Zhanzhan Cheng, Shiliang Pu, Yi Niu, and Fei Wu. Vsr: A unified framework for document layout analysis combining vision, semantics and relations, 2021. -- [22] Peter W J Staar, Michele Dolfi, Christoph Auer, and Costas Bekas. Corpus conversion service: A machine learning platform to ingest documents at scale. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , KDD, pages 774–782. ACM, 2018. -- [23] Connor Shorten and Taghi M. Khoshgoftaar. A survey on image data augmentation for deep learning. Journal of Big Data , 6(1):60, 2019. +- [22] Peter W J Staar , Michele Dolfi, Christoph Auer , and Costas Bekas. Corpus conversion service: A machine learning platform to ingest documents at scale. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD, pages 774-782. ACM, 2018. +- [23] Connor Shorten and Taghi M. Khoshgoftaar. A survey on image data augmentation for deep learning. Journal of Big Data, 6(1):60, 2019. diff --git a/tests/pdf_snapshots/pdf/sources/2305.03393v1-pg9.pdf.md b/tests/pdf_snapshots/pdf/sources/2305.03393v1-pg9.pdf.md index e85f892e..45a100f9 100644 --- a/tests/pdf_snapshots/pdf/sources/2305.03393v1-pg9.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/2305.03393v1-pg9.pdf.md @@ -6,25 +6,21 @@ We have chosen the PubTabNet data set to perform HPO, since it includes a highly Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart. -| | # | | # | | | TEDs | | mAP | Inference | | -|------------|-----|------------|-----|----------|--------|---------|-------|--------|-------------|------| -| | | | | Language | | | | (0.75) | time (secs) | | -| enc-layers | | dec-layers | | | simple | complex | all | | | | -| | | | | OTSL | 0.965 | 0.934 | 0.955 | 0.88 | | 2.73 | -| | 6 | | 6 | | | | | | | | -| | | | | HTML | 0.969 | 0.927 | 0.955 | 0.857 | | 5.39 | -| | | | | OTSL | 0.938 | 0.904 | 0.927 | 0.853 | | 1.97 | -| | 4 | | 4 | | | | | | | | -| | | | | HTML | 0.952 | 0.909 | 0.938 | 0.843 | | 3.77 | -| | | | | OTSL | 0.923 | 0.897 | 0.915 | 0.859 | | 1.91 | -| | 2 | | 4 | | | | | | | | -| | | | | HTML | 0.945 | 0.901 | 0.931 | 0.834 | | 3.81 | -| | | | | OTSL | 0.952 | 0.92 | 0.942 | 0.857 | | 1.22 | -| | 4 | | 2 | | | | | | | | -| | | | | HTML | 0.944 | 0.903 | 0.931 | 0.824 | | 2 | +| | dec-layers Language TEDs mAP | | | (0.75) Inference | +|----------------------------------|--------------------------------|-------------------------------|--------------------------------|--------------------| +| enc-layers # # | | | | | +| | | | time (secs) simple complex all | | +| 66 OTSL 0.9650.9340.9550.882.73 | | | | | +| | | HTML 0.9690.9270.9550.8575.39 | | | +| 44 OTSL 0.9380.9040.9270.8531.97 | | | | | +| | | HTML 0.9520.9090.9380.8433.77 | | | +| 24 OTSL 0.9230.8970.9150.8591.91 | | | | | +| | | HTML 0.9450.9010.9310.8343.81 | | | +| 42 OTSL 0.9520.920.9420.8571.22 | | | | | +| | | HTML 0.9440.9030.9310.8242 | | | ## 5.2 Quantitative Results -We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on di ffi cult financial tables (FinTabNet) that contain sparse and large tables. +We picked the model parameter configuration that produced the best prediction quality (enc=6, dec=6, heads=8) with PubTabNet alone, then independently trained and evaluated it on three publicly available data sets: PubTabNet (395k samples), FinTabNet (113k samples) and PubTables-1M (about 1M samples). Performance results are presented in Table. 2. It is clearly evident that the model trained on OTSL outperforms HTML across the board, keeping high TEDs and mAP scores even on difficult financial tables (FinTabNet) that contain sparse and large tables. Additionally, the results show that OTSL has an advantage over HTML when applied on a bigger data set like PubTables-1M and achieves significantly improved scores. Finally, OTSL achieves faster inference due to fewer decoding steps which is a result of the reduced sequence representation. diff --git a/tests/pdf_snapshots/pdf/sources/2305.03393v1.pdf.md b/tests/pdf_snapshots/pdf/sources/2305.03393v1.pdf.md index 6ef03cf7..7a95dabd 100644 --- a/tests/pdf_snapshots/pdf/sources/2305.03393v1.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/2305.03393v1.pdf.md @@ -1,8 +1,8 @@ ## Optimized Table Tokenization for Table Structure Recognition -Maksym Lysak [0000 − 0002 − 3723 − 6960] , Ahmed Nassar [0000 − 0002 − 9468 − 0822] Nikolaos Livathinos [0000 − 0001 − 8513 − 3491] , Christoph Auer [0000 − 0001 − 5761 − 0422] , [0000 0002 8088 0823] , and Peter Staar − − − +Maksym Lysak[0000−0002−3723−6960], Ahmed Nassar[0000−0002−9468−0822], Nikolaos Livathinos[0000−0001−8513−3491], Christoph Auer[0000−0001−5761−0422], and Peter Staar[0000−0002−8088−0823] -IBM Research {mly,ahn,nli,cau,taa}@zurich.ibm.com +IBM Research {mly, ahn,nli, cau, taa}@zurich.ibm.com Abstract. Extracting tables from documents is a crucial task in any document conversion pipeline. Recently, transformer-based models have demonstrated that table-structure can be recognized with impressive accuracy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking only the image of a table, such models predict a sequence of tokens (e.g. in HTML, LaTeX) which represent the structure of the table. Since the token representation of the table structure has a significant impact on the accuracy and run-time performance of any Im2Seq model, we investigate in this paper how table-structure representation can be optimised. We propose a new, optimised table-structure language (OTSL) with a minimized vocabulary and specific rules. The benefits of OTSL are that it reduces the number of tokens to 5 (HTML needs 28+) and shortens the sequence length to half of HTML on average. Consequently, model accuracy improves significantly, inference time is halved compared to HTML-based models, and the predicted table structures are always syntactically correct. This in turn eliminates most post-processing needs. Popular table structure data-sets will be published in OTSL format to the community. @@ -12,7 +12,7 @@ Keywords: Table Structure Recognition · Data Representation · Transformers · Tables are ubiquitous in documents such as scientific papers, patents, reports, manuals, specification sheets or marketing material. They often encode highly valuable information and therefore need to be extracted with high accuracy. Unfortunately, tables appear in documents in various sizes, styling and structure, making it difficult to recover their correct structure with simple analytical methods. Therefore, accurate table extraction is achieved these days with machine-learning based methods. -In modern document understanding systems [1,15], table extraction is typically a two-step process. Firstly, every table on a page is located with a bounding box, and secondly, their logical row and column structure is recognized. As of +In modern document understanding systems [1,15], table extraction is typically a two-step process. Firstly, every table on a page is located with a bounding Fig. 1. Comparison between HTML and OTSL table structure representation: (A) table-example with complex row and column headers, including a 2D empty span, (B) minimal graphical representation of table structure using rectangular layout, (C) HTML representation, (D) OTSL representation. This example demonstrates many of the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case), its reduced sequence length (55 versus 30) and a enhanced internal structure (variable token sequence length per row in HTML versus a fixed length of rows in OTSL). @@ -34,7 +34,7 @@ Approaches to formalize the logical structure and layout of tables in electronic Other work [20] aims at predicting a grid for each table and deciding which cells must be merged using an attention network. Im2Seq methods cast the problem as a sequence generation task [4,5,9,22], and therefore need an internal tablestructure representation language, which is often implemented with standard markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods have a natural advantage over the OD and GNN methods by virtue of directly predicting the table-structure. As such, no post-processing or rules are needed in order to obtain the table-structure, which is necessary with OD and GNN approaches. In practice, this is not entirely true, because a predicted sequence of table-structure markup does not necessarily have to be syntactically correct. Hence, depending on the quality of the predicted sequence, some post-processing needs to be performed to ensure a syntactically valid (let alone correct) sequence. -Within the Im2Seq method, we find several popular models, namely the encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders to predict a table in HTML representation. The tag decoder predicts a sequence of HTML tags. For each decoded table cell ( ), the attention is passed to the cell decoder to predict the content with an embedded OCR approach. The latter makes it susceptible to transcription errors in the cell content of the table. TableFormer address this reliance on OCR and uses two transformer decoders for HTML structure and cell bounding box prediction in an end-to-end architecture. The predicted cell bounding box is then used to extract text tokens from an originating (digital) PDF page, circumventing any need for OCR. TabSplitter [2] proposes a compact double-matrix representation of table rows and columns to do error detection and error correction of HTML structure sequences based on predictions from [19]. This compact double-matrix representation can not be used directly by the Img2seq model training, so the model uses HTML as an intermediate form. Chi et. al. [4] introduce a data set and a baseline method using bidirectional LSTMs to predict LaTeX code. Kayal [5] introduces Gated ResNet transformers to predict LaTeX code, and a separate OCR module to extract content. +Within the Im2Seq method, we find several popular models, namely the encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders to predict a table in HTML representation. The tag decoder predicts a sequence of HTML tags. For each decoded table cell (), the attention is passed to the cell decoder to predict the content with an embedded OCR approach. The latter makes it susceptible to transcription errors in the cell content of the table. TableFormer address this reliance on OCR and uses two transformer decoders for HTML structure and cell bounding box prediction in an end-to-end architecture. The predicted cell bounding box is then used to extract text tokens from an originating (digital) PDF page, circumventing any need for OCR. TabSplitter [2] proposes a compact double-matrix representation of table rows and columns to do error detection and error correction of HTML structure sequences based on predictions from [19]. This compact double-matrix representation can not be used directly by the Img2seq model training, so the model uses HTML as an intermediate form. Chi et. al. [4] introduce a data set and a baseline method using bidirectional LSTMs to predict LaTeX code. Kayal [5] introduces Gated ResNet transformers to predict LaTeX code, and a separate OCR module to extract content. Im2Seq approaches have shown to be well-suited for the TSR task and allow a full end-to-end network design that can output the final table structure without pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated to deliver state-of-the-art prediction accuracy [9]. This motivated the authors to investigate if the performance (both in accuracy and inference time) can be further improved by optimising the table structure representation language. We believe this is a necessary step before further improving neural network architectures for this task. @@ -42,13 +42,13 @@ Im2Seq approaches have shown to be well-suited for the TSR task and allow a full All known Im2Seq based models for TSR fundamentally work in similar ways. Given an image of a table, the Im2Seq model predicts the structure of the table by generating a sequence of tokens. These tokens originate from a finite vocab- -ulary and can be interpreted as a table structure. For example, with the HTML tokens
and , one can construct simple table structures without any spanning cells. In reality though, one needs , , , , at least 28 HTML tokens to describe the most common complex tables observed in real-world documents [21,22], due to a variety of spanning cells definitions in the HTML token vocabulary. +ulary and can be interpreted as a table structure. For example, with the HTML tokens ,
, , , and , one can construct simple table structures without any spanning cells. In reality though, one needs at least 28 HTML tokens to describe the most common complex tables observed in real-world documents [21,22], due to a variety of spanning cells definitions in the HTML token vocabulary. Fig. 2. Frequency of tokens in HTML and OTSL as they appear in PubTabNet. -Obviously, HTML and other general-purpose markup languages were not designed for Im2Seq models. As such, they have some serious drawbacks. First, the token vocabulary needs to be artificially large in order to describe all plausible tabular structures. Since most Im2Seq models use an autoregressive approach, they generate the sequence token by token. Therefore, to reduce inference time, a shorter sequence length is critical. Every table-cell is represented by at least two tokens ( and ). Furthermore, when tokenizing the HTML structure, one needs to explicitly enumerate possible column-spans and row-spans as words. In practice, this ends up requiring 28 different HTML tokens (when including column- and row-spans up to 10 cells) just to describe every table in the PubTabNet dataset. Clearly, not every token is equally represented, as is depicted in Figure 2. This skewed distribution of tokens in combination with variable token row-length makes it challenging for models to learn the HTML structure. +Obviously, HTML and other general-purpose markup languages were not designed for Im2Seq models. As such, they have some serious drawbacks. First, the token vocabulary needs to be artificially large in order to describe all plausible tabular structures. Since most Im2Seq models use an autoregressive approach, they generate the sequence token by token. Therefore, to reduce inference time, a shorter sequence length is critical. Every table-cell is represented by at least two tokens ( and ). Furthermore, when tokenizing the HTML structure, one needs to explicitly enumerate possible column-spans and row-spans as words. In practice, this ends up requiring 28 different HTML tokens (when including column- and row-spans up to 10 cells) just to describe every table in the PubTabNet dataset. Clearly, not every token is equally represented, as is depicted in Figure 2. This skewed distribution of tokens in combination with variable token row-length makes it challenging for models to learn the HTML structure. Additionally, it would be desirable if the representation would easily allow an early detection of invalid sequences on-the-go, before the prediction of the entire table structure is completed. HTML is not well-suited for this purpose as the verification of incomplete sequences is non-trivial or even impossible. @@ -68,15 +68,15 @@ In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines The OTSL vocabulary is comprised of the following tokens: -- "C" cell - a new table cell that either has or does not have cell content – -- – "L" cell - left-looking cell , merging with the left neighbor cell to create a span -- "U" cell - up-looking cell , merging with the upper neighbor cell to create a – span -- "X" cell - cross cell , to merge with both left and upper neighbor cells – -- "NL" - new-line , switch to the next row. – +- "C" cell - a new table cell that either has or does not have cell content +- "L" cell - left-looking cell, merging with the left neighbor cell to create a span +- "U" cell - up-looking cell, merging with the upper neighbor cell to create a span +- "X" cell - cross cell, to merge with both left and upper neighbor cells +- "NL" - new-line, switch to the next row. A notable attribute of OTSL is that it has the capability of achieving lossless conversion to HTML. -Fig. 3. OTSL description of table structure: A - table example; B - graphical representation of table structure; C - mapping structure on a grid; D - OTSL structure encoding; E - explanation on cell encoding +Fig. 3. OTSL description of table structure: A - table example; B - graphical representation of table structure; C - mapping structure on a grid; D - OTSL structure @@ -84,15 +84,15 @@ Fig. 3. OTSL description of table structure: A - table example; B - graphical re The OTSL representation follows these syntax rules: -- 1. Left-looking cell rule : The left neighbour of an "L" cell must be either another "L" cell or a "C" cell. -- 2. Up-looking cell rule : The upper neighbour of a "U" cell must be either another "U" cell or a "C" cell. -- 3. Cross cell rule : +- 1. Left-looking cell rule: The left neighbour of an "L" cell must be either another "L" cell or a "C" cell. +- 2. Up-looking cell rule: The upper neighbour of a "U" cell must be either another "U" cell or a "C" cell. +- 3. Cross cell rule: The left neighbour of an "X" cell must be either another "X" cell or a "U" cell, and the upper neighbour of an "X" cell must be either another "X" cell or an "L" cell. -- 4. First row rule : Only "L" cells and "C" cells are allowed in the first row. -- 5. First column rule : Only "U" cells and "C" cells are allowed in the first column. -- 6. Rectangular rule : The table representation is always rectangular - all rows must have an equal number of tokens, terminated with "NL" token. +- 4. First row rule: Only "L" cells and "C" cells are allowed in the first row. +- 5. First column rule: Only "U" cells and "C" cells are allowed in the first column. +- 6. Rectangular rule: The table representation is always rectangular - all rows must have an equal number of tokens, terminated with "NL" token. The application of these rules gives OTSL a set of unique properties. First of all, the OTSL enforces a strictly rectangular structure representation, where every new-line token starts a new row. As a consequence, all rows and all columns have exactly the same number of tokens, irrespective of cell spans. Secondly, the OTSL representation is unambiguous: Every table structure is represented in one way. In this representation every table cell corresponds to a "C"-cell token, which in case of spans is always located in the top-left corner of the table cell definition. Third, OTSL syntax rules are only backward-looking. As a consequence, every predicted token can be validated straight during sequence generation by looking at the previously predicted sequence. As such, OTSL can guarantee that every predicted sequence is syntactically valid. @@ -122,22 +122,18 @@ We have chosen the PubTabNet data set to perform HPO, since it includes a highly Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart. -| | # | | # | | | TEDs | | mAP | Inference | | -|------------|-----|------------|-----|----------|--------|---------|-------|--------|-------------|------| -| | | | | Language | | | | (0.75) | time (secs) | | -| enc-layers | | dec-layers | | | simple | complex | all | | | | -| | | | | OTSL | 0.965 | 0.934 | 0.955 | 0.88 | | 2.73 | -| | 6 | | 6 | | | | | | | | -| | | | | HTML | 0.969 | 0.927 | 0.955 | 0.857 | | 5.39 | -| | | | | OTSL | 0.938 | 0.904 | 0.927 | 0.853 | | 1.97 | -| | 4 | | 4 | | | | | | | | -| | | | | HTML | 0.952 | 0.909 | 0.938 | 0.843 | | 3.77 | -| | | | | OTSL | 0.923 | 0.897 | 0.915 | 0.859 | | 1.91 | -| | 2 | | 4 | | | | | | | | -| | | | | HTML | 0.945 | 0.901 | 0.931 | 0.834 | | 3.81 | -| | | | | OTSL | 0.952 | 0.92 | 0.942 | 0.857 | | 1.22 | -| | 4 | | 2 | | | | | | | | -| | | | | HTML | 0.944 | 0.903 | 0.931 | 0.824 | | 2 | +| | dec-layers Language TEDs mAP | | | (0.75) Inference | +|----------------------------------|--------------------------------|-------------------------------|--------------------------------|--------------------| +| enc-layers # # | | | | | +| | | | time (secs) simple complex all | | +| 66 OTSL 0.9650.9340.9550.882.73 | | | | | +| | | HTML 0.9690.9270.9550.8575.39 | | | +| 44 OTSL 0.9380.9040.9270.8531.97 | | | | | +| | | HTML 0.9520.9090.9380.8433.77 | | | +| 24 OTSL 0.9230.8970.9150.8591.91 | | | | | +| | | HTML 0.9450.9010.9310.8343.81 | | | +| 42 OTSL 0.9520.920.9420.8571.22 | | | | | +| | | HTML 0.9440.9030.9310.8242 | | | ## 5.2 Quantitative Results @@ -147,25 +143,21 @@ Additionally, the results show that OTSL has an advantage over HTML when applied Table 2. TSR and cell detection results compared between OTSL and HTML on the PubTabNet [22], FinTabNet [21] and PubTables-1M [14] data sets using TableFormer [9] (with enc=6, dec=6, heads=8). -| | | | | TEDs | | | | Inference | | -|--------------|----------|----------|--------|---------|-------|-----------|-------|-------------|------| -| | Data set | Language | | | | mAP(0.75) | | time (secs) | | -| | | | simple | complex | all | | | | | -| | | OTSL | 0.965 | 0.934 | 0.955 | | 0.88 | | 2.73 | -| PubTabNet | | | | | | | | | | -| | | HTML | 0.969 | 0.927 | 0.955 | | 0.857 | | 5.39 | -| | | OTSL | 0.955 | 0.961 | 0.959 | | 0.862 | | 1.85 | -| FinTabNet | | | | | | | | | | -| | | HTML | 0.917 | 0.922 | 0.92 | | 0.722 | | 3.26 | -| | | OTSL | 0.987 | 0.964 | 0.977 | | 0.896 | | 1.79 | -| PubTables-1M | | | | | | | | | | -| | | HTML | 0.983 | 0.944 | 0.966 | | 0.889 | | 3.26 | +| Data set Language TEDs mAP(0.75) Inference | | | +|----------------------------------------------|-------------------------------|--------------------------------| +| | | time (secs) simple complex all | +| PubTabNet OTSL 0.9650.9340.9550.882.73 | | | +| | HTML 0.9690.9270.9550.8575.39 | | +| FinTabNet OTSL 0.9550.9610.9590.8621.85 | | | +| | HTML 0.9170.9220.920.7223.26 | | +| PubTables-1M OTSL 0.9870.9640.9770.8961.79 | | | +| | HTML 0.9830.9440.9660.8893.26 | | ## 5.3 Qualitative Results To illustrate the qualitative differences between OTSL and HTML, Figure 5 demonstrates less overlap and more accurate bounding boxes with OTSL. In Figure 6, OTSL proves to be more effective in handling tables with longer token sequences, resulting in even more precise structure prediction and bounding boxes. -Fig. 5. The OTSL model produces more accurate bounding boxes with less overlap (E) than the HTML model (D), when predicting the structure of a sparse table (A), at twice the inference speed because of shorter sequence length (B),(C). "PMC2807444_006_00.png" PubTabNet. μ +Fig. 5. The OTSL model produces more accurate bounding boxes with less overlap (E) than the HTML model (D), when predicting the structure of a sparse table (A), at twice the inference speed because of shorter sequence length (B),(C). "PMC2807444_006_00.png" PubTabNet. @@ -183,26 +175,26 @@ Secondly, OTSL has more inherent structure and a significantly restricted vocabu ## References -- 1. Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering document conversion as a cloud service with high throughput and responsiveness. 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Deng, Y., Rosenberg, D., Mann, G.: Challenges in end-to-end neural scientific table recognition. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 894-901. IEEE (2019) +- 5. Kayal, P., Anand, M., Desai, H., Singh, M.: Tables to latex: structure and content extraction from scientific tables. International Journal on Document Analysis and Recognition (IJDAR) pp. 1-10 (2022) +- 6. Lee, E., Kwon, J., Yang, H., Park, J., Lee, S., Koo, H.I., Cho, N.I.: Table structure recognition based on grid shape graph. In: 2022 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). pp. 1868- 1873. IEEE (2022) - 7. Li, M., Cui, L., Huang, S., Wei, F., Zhou, M., Li, Z.: Tablebank: A benchmark dataset for table detection and recognition (2019) -- 8. Livathinos, N., Berrospi, C., Lysak, M., Kuropiatnyk, V., Nassar, A., Carvalho, A., Dolfi, M., Auer, C., Dinkla, K., Staar, P.: Robust pdf document conversion using recurrent neural networks. Proceedings of the AAAI Conference on Artificial Intelligence 35 (17), 15137–15145 (May 2021), https://ojs.aaai.org/index.php/ AAAI/article/view/17777 -- 9. Nassar, A., Livathinos, N., Lysak, M., Staar, P.: Tableformer: Table structure understanding with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 4614–4623 (June 2022) -- 10. Pfitzmann, B., Auer, C., Dolfi, M., Nassar, A.S., Staar, P.W.J.: Doclaynet: A large human-annotated dataset for document-layout segmentation. In: Zhang, A., Rangwala, H. (eds.) KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14 - 18, 2022. pp. 3743–3751. ACM (2022). https://doi.org/10.1145/3534678.3539043 https:// doi.org/10.1145/3534678.3539043 , -- 11. Prasad, D., Gadpal, A., Kapadni, K., Visave, M., Sultanpure, K.: Cascadetabnet: An approach for end to end table detection and structure recognition from imagebased documents. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops. pp. 572–573 (2020) -- 12. Schreiber, S., Agne, S., Wolf, I., Dengel, A., Ahmed, S.: Deepdesrt: Deep learning for detection and structure recognition of tables in document images. In: 2017 14th IAPR international conference on document analysis and recognition (ICDAR). vol. 1, pp. 1162–1167. IEEE (2017) -- 13. Siddiqui, S.A., Fateh, I.A., Rizvi, S.T.R., Dengel, A., Ahmed, S.: Deeptabstr: Deep learning based table structure recognition. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 1403–1409 (2019). https:// doi.org/10.1109/ICDAR.2019.00226 -- 14. Smock, B., Pesala, R., Abraham, R.: PubTables-1M: Towards comprehensive table extraction from unstructured documents. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 4634–4642 (June 2022) -- 15. Staar, P.W.J., Dolfi, M., Auer, C., Bekas, C.: Corpus conversion service: A machine learning platform to ingest documents at scale. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. pp. 774–782. KDD '18, Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3219819.3219834 , https://doi.org/10. 1145/3219819.3219834 -- 16. Wang, X.: Tabular Abstraction, Editing, and Formatting. Ph.D. thesis, CAN (1996), aAINN09397 -- 17. Xue, W., Li, Q., Tao, D.: Res2tim: Reconstruct syntactic structures from table images. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 749–755. IEEE (2019) -- 18. Xue, W., Yu, B., Wang, W., Tao, D., Li, Q.: Tgrnet: A table graph reconstruction network for table structure recognition. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 1295–1304 (2021) -- 19. Ye, J., Qi, X., He, Y., Chen, Y., Gu, D., Gao, P., Xiao, R.: Pingan-vcgroup's solution for icdar 2021 competition on scientific literature parsing task b: Table recognition to html (2021). https://doi.org/10.48550/ARXIV.2105.01848 , https://arxiv.org/abs/2105.01848 -- 20. Zhang, Z., Zhang, J., Du, J., Wang, F.: Split, embed and merge: An accurate table structure recognizer. Pattern Recognition 126 , 108565 (2022) -- 21. Zheng, X., Burdick, D., Popa, L., Zhong, X., Wang, N.X.R.: Global table extractor (gte): A framework for joint table identification and cell structure recognition using visual context. In: 2021 IEEE Winter Conference on Applications of Computer Vision (WACV). pp. 697–706 (2021). https://doi.org/10.1109/WACV48630.2021. 00074 -- 22. Zhong, X., ShafieiBavani, E., Jimeno Yepes, A.: Image-based table recognition: Data, model, and evaluation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.M. (eds.) Computer Vision – ECCV 2020. pp. 564–580. Springer International Publishing, Cham (2020) -- 23. Zhong, X., Tang, J., Yepes, A.J.: Publaynet: largest dataset ever for document layout analysis. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 1015–1022. IEEE (2019) +- 8. Livathinos, N., Berrospi, C., Lysak, M., Kuropiatnyk, V., Nassar, A., Carvalho, A., Dolfi, M., Auer, C., Dinkla, K., Staar, P.: Robust pdf document conversion using recurrent neural networks. Proceedings of the AAAI Conference on Artificial Intelligence 35(17), 15137-15145 (May 2021), https : //ojs.aaai.org/index.php/ AAAI/article/view/17777 +- 9. Nassar, A., Livathinos, N., Lysak, M., Staar, P.: Tableformer: Table structure understanding with transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 4614-4623 (June 2022) +- 10. Pfitzmann, B., Auer, C., Dolfi, M., Nassar, A.S., Staar, P.W.J.: Doclaynet: A large human-annotated dataset for document-layout segmentation. In: Zhang, A., Rangwala, H. (eds.) KDD'22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14 - 18, 2022. pp. 3743-3751. ACM (2022). https : //doi.org/10.1145/3534678.3539043, https : // doi.org/10.1145/3534678.3539043 +- 11. Prasad, D., Gadpal, A., Kapadni, K., Visave, M., Sultanpure, K.: Cascadetabnet: An approach for end to end table detection and structure recognition from imagebased documents. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops. pp. 572-573 (2020) +- 12. Schreiber, S., Agne, S., Wolf, I., Dengel, A., Ahmed, S.: Deepdesrt: Deep learning for detection and structure recognition of tables in document images. In: 201714th IAPR international conference on document analysis and recognition (ICDAR). vol. 1, pp. 1162-1167. IEEE (2017) +- 13. Siddiqui, S.A., Fateh, I.A., Rizvi, S.T.R., Dengel, A., Ahmed, S.: Deeptabstr: Deep learning based table structure recognition. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 1403-1409 (2019). https : // doi.org/10.1109/ICDAR.2019.00226 +- 14. Smock, B., Pesala, R., Abraham, R.: PubTables-1M: Towards comprehensive table extraction from unstructured documents. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 4634-4642 (June 2022) +- 15. Staar, P.W.J., Dolfi, M., Auer, C., Bekas, C.: Corpus conversion service: A machine learning platform to ingest documents at scale. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. pp. 774-782. KDD'18, Association for Computing Machinery, New York, NY, USA (2018). https : //doi.org/10.1145/3219819.3219834, https : //doi.org/10. 1145/3219819.3219834 +- 16. Wang, X. : Tabular Abstraction, Editing, and Formatting. Ph.D. thesis, CAN (1996), aAINN09397 +- 17. Xue, W., Li, Q., Tao, D.: Res2tim: Reconstruct syntactic structures from table images. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 749-755. IEEE (2019) +- 18. Xue, W., Yu, B., Wang, W., Tao, D., Li, Q.: Tgrnet: A table graph reconstruction network for table structure recognition. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 1295-1304 (2021) +- 19. Ye, J., Qi, X., He, Y., Chen, Y., Gu, D., Gao, P., Xiao, R.: Pingan-vcgroup's solution for icdar 2021 competition on scientific literature parsing task b: Table recognition to html (2021). https : //doi.org/10.48550/ARXIV.2105.01848, https : //arxiv.org/abs/2105.01848 +- 20. Zhang, Z., Zhang, J., Du, J., Wang, F. : Split, embed and merge: An accurate table structure recognizer. Pattern Recognition 126, 108565 (2022) +- 21. Zheng, X., Burdick, D., Popa, L., Zhong, X., Wang, N.X.R. : Global table extractor (gte): A framework for joint table identification and cell structure recognition using visual context. In: 2021 IEEE Winter Conference on Applications of Computer Vision (WACV). pp. 697-706 (2021). https : //doi.org/10.1109/WACV48630.2021. 00074 +- 22. Zhong, X., ShafieiBavani, E., Jimeno Yepes, A.: Image-based table recognition: Data, model, and evaluation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.M. (eds.) Computer Vision - ECCV 2020. pp. 564-580. Springer International Publishing, Cham (2020) +- 23. Zhong, X., Tang, J., Yepes, A.J.: Publaynet: largest dataset ever for document layout analysis. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 1015-1022. IEEE (2019) diff --git a/tests/pdf_snapshots/pdf/sources/amt_handbook_sample.pdf.md b/tests/pdf_snapshots/pdf/sources/amt_handbook_sample.pdf.md index 5735bdad..7547c497 100644 --- a/tests/pdf_snapshots/pdf/sources/amt_handbook_sample.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/amt_handbook_sample.pdf.md @@ -1,31 +1,27 @@ pulleys, provided the inner race of the bearing is clamped to the supporting structure by the nut and bolt. Plates must be attached to the structure in a positive manner to eliminate rotation or misalignment when tightening the bolts or screws. -The two general types of self-locking nuts currently in use are the all-metal type and the fiber lock type. For the sake of simplicity, only three typical kinds of self-locking nuts are considered in this handbook: the Boots self-locking and the stainless steel self-locking nuts, representing the all-metal types; and the elastic stop nut, representing t the fiber insert type. +The two general types of self-locking nuts currently in use are the all-metal type and the fiber lock type. For the sake of simplicity, only three typical kinds of self-locking nuts are considered in this handbook: the Boots self-locking and the stainless steel self-locking nuts, representing the all-metal types; and the elastic stop nut, representing the fiber insert type. ## Boots Self-Locking Nut -T he Boots self-locking nut is of one piece, all-metal construction designed to hold tight despite severe vibration. Note in F Figure 7-26 6 that it has two sections and is essentially two nuts in one: a locking nut and a load-carrying nut. The two sections are connected with a spring, which is an integral part of the nut. +The Boots self-locking nut is of one piece, all-metal construction designed to hold tight despite severe vibration. Note in Figure 7-26 that it has two sections and is essentially two nuts in one: a locking nut and a load-carrying nut. The two sections are connected with a spring, which is an integral part of the nut. The spring keeps the locking and load-carrying sections such a distance apart that the two sets of threads are out of phase or spaced so that a bolt, which has been screwed through the load-carrying section, must push the locking section outward against the force of the spring to engage the threads of the locking section properly. The spring, through the medium of the locking section, exerts a constant locking force on the bolt in the same direction as a force that would tighten the nut. In this nut, the load-carrying section has the thread strength of a standard nut of comparable size, while the locking section presses against the threads of the bolt and locks the nut firmly in position. Only a wrench applied to the nut loosens it. The nut can be removed and reused without impairing its efficiency. -Boots self-locking nuts are made with three different spring styles and in various shapes and sizes. The wing type that is - -Figure 7-26. Self-locking nuts. +Boots self-locking nuts are made with three different spring -the most common ranges in size for No. 6 up to 1 ⁄ 1 ⁄4 ⁄4 inch, the Rol-top ranges from 1 ⁄ 1 ⁄4 inch to 1 ⁄ 1 ⁄6 inch, and the bellows type ⁄4 ⁄6 ranges in size from No. 8 up to 3 ⁄ 3 ⁄8 ⁄8 inch. Wing-type nuts are made of anodized aluminum alloy, cadmium-plated carbon steel, or stainless steel. The Rol-top nut is cadmium-plated steel, and the bellows type is made of aluminum alloy only. +Rol-top ranges from 1⁄4 inch to 1⁄6 inch, and the bellows type ranges in size from No. 8 up to 3⁄8 inch. Wing-type nuts are made of anodized aluminum alloy, cadmium-plated carbon steel, or stainless steel. The Rol-top nut is cadmium-plated steel, and the bellows type is made of aluminum alloy only. ## Stainless Steel Self-Locking Nut -The stainless steel self-locking nut may be spun on and off by hand as its locking action takes places only when the nut is seated against a solid surface and tightened. The nut consists of two parts: a case with a beveled locking shoulder and key and a thread insert with a locking shoulder and slotted keyway. Until the nut is tightened, it spins on the bolt easily, because the threaded insert is the proper size for the bolt. However, when the nut is seated against a solid surface and tightened, the locking shoulder of the insert is pulled downward and wedged against the locking shoulder of the case. This action compresses the threaded insert and causes it to clench the bolt tightly. The cross-sectional view in Figure 7-27 7 shows how the key of the case fits into the slotted keyway of the insert so that when the case is turned, the threaded insert is turned with it. Note that the slot is wider than the key. This permits the slot to be narrowed and the insert to be compressed when the nut is tightened. +The stainless steel self-locking nut may be spun on and off by hand as its locking action takes places only when the nut is seated against a solid surface and tightened. The nut consists of two parts: a case with a beveled locking shoulder and key and a thread insert with a locking shoulder and slotted keyway. Until the nut is tightened, it spins on the bolt easily, because the threaded insert is the proper size for the bolt. However, when the nut is seated against a solid surface and tightened, the locking shoulder of the insert is pulled downward and wedged against the locking shoulder of the case. This action compresses the threaded insert and causes it to clench the bolt tightly. The cross-sectional view in Figure 7-27 shows how the key of the case fits into the slotted keyway of the insert so that when the case is turned, the threaded insert is turned with it. Note that the slot is wider than the key. This permits the slot to be narrowed and the insert to be compressed when the nut is tightened. ## Elastic Stop Nut The elastic stop nut is a standard nut with the height increased to accommodate a fiber locking collar. This -Figure 7-27. Stainless steel self-locking nut. - diff --git a/tests/pdf_snapshots/pdf/sources/code_and_formula.pdf.md b/tests/pdf_snapshots/pdf/sources/code_and_formula.pdf.md index b1d6bf8b..1c941faf 100644 --- a/tests/pdf_snapshots/pdf/sources/code_and_formula.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/code_and_formula.pdf.md @@ -6,7 +6,7 @@ Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie co Listing 1: Simple JavaScript Program -function add ( a , b ) { return a + b ; } console log ( add (3 , 5) ) ; . +f un c t i o n add (a, b) { r e t urn a + b; } c o n s o l e.l og (add (3, 5)); Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. @@ -24,4 +24,4 @@ Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat. -Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat. +Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat diff --git a/tests/pdf_snapshots/pdf/sources/multi_page.pdf.md b/tests/pdf_snapshots/pdf/sources/multi_page.pdf.md index a38f2c52..280234c1 100644 --- a/tests/pdf_snapshots/pdf/sources/multi_page.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/multi_page.pdf.md @@ -2,18 +2,18 @@ The concept of the word processor predates modern computers and has evolved through several technological milestones. -## Pre Digital Era (19th Early 20th Century) - - +## Pre-Digital Era (19th - Early 20th Century) -The origins of word processing can be traced back to the invention of the typewriter in the mid 19th century. Patented in 1868 by Christopher Latham Sholes, the typewriter revolutionized written communication by enabling people to produce legible, professi - onal documents more efficiently than handwriting. +The origins of word processing can be traced back to the invention of the typewriter in the mid-19th century. Patented in 1868 by Christopher Latham Sholes, the typewriter revolutionized written communication by enabling people to produce legible, professional documents more efficiently than handwriting. -During this period, the term "word processing" didn't exist, but the typewriter laid the groundwork for future developments. Over time, advancements such as carbon paper (for copies) and the electric typewriter (introduced by IBM in 1935) improved the spee d and convenience of document creation. +During this period, the term "word processing" didn't exist, but the typewriter laid the groundwork for future developments. Over time, advancements such as carbon paper (for copies) and the electric typewriter (introduced by IBM in 1935) improved the speed and convenience of document creation. -## The Birth of Word Processing (1960s 1970s) - +## The Birth of Word Processing (1960s - 1970s) The term "word processor" first emerged in the 1960s and referred to any system designed to streamline written communication and document production. Early word processors were not software programs but rather standalone machines. -- • IBM MT/ST (Magnetic Tape/Selectric Typewriter) : Introduced in 1964, this machine combined IBM's Selectric typewriter with magnetic tape storage. It allowed users to record, edit, and replay typed content— an early example of digital text t— storage. -- • Wang Laboratories : In the 1970s, Wang introduced dedicated word processing machines. These devices, like the Wang 1200, featured small screens and floppy disks, making them revolutionary for their time. +- IBM MT/ST (Magnetic Tape/Selectric Typewriter): Introduced in 1964, this machine combined IBM's Selectric typewriter with magnetic tape storage. It allowed users to record, edit, and replay typed content-an early example of digital text storage. +- Wang Laboratories: In the 1970s, Wang introduced dedicated word processing machines. These devices, like the Wang 1200, featured small screens and floppy disks, making them revolutionary for their time. These machines were primarily used in offices, where secretarial pools benefited from their ability to make revisions without retyping entire documents. @@ -21,67 +21,67 @@ These machines were primarily used in offices, where secretarial pools benefited The advent of personal computers in the late 1970s and early 1980s transformed word processing from a niche tool to an essential technology for businesses and individuals alike. -- • WordStar (1978) : Developed for the CP/M operating system, WordStar was one of the first widely used word processing programs. It featured early examples of modern features like cut, copy, and paste. -- • Microsoft Word (1983) : Microsoft launched Word for MS - DOS in 1983, introducing a graphical user interface (GUI) and mouse support. Over the years, Microsoft Word became the industry standard for word processing. +- WordStar (1978): Developed for the CP/M operating system, WordStar was one of the first widely used word processing programs. It featured early examples of modern features like cut, copy, and paste. +- Microsoft Word (1983): Microsoft launched Word for MS-DOS in 1983, introducing a graphical user interface (GUI) and mouse support. Over the years, Microsoft Word became the industry standard for word processing. Other notable software from this era included WordPerfect, which was popular among legal professionals, and Apple's MacWrite, which leveraged the Macintosh's graphical capabilities. -## The Modern Era (1990s Present) - +## The Modern Era (1990s - Present) By the 1990s, word processing software had become more sophisticated, with features like spell check, grammar check, templates, and collaborative tools. -- • Microsoft Office Suite : Microsoft continued to dominate with its Office Suite, integrating Word with other productivity tools like Excel and PowerPoint. -- • OpenOffice and LibreOffice : Open - source alternatives emerged in the early 2000s, offering free and flexible word processing options. -- • Google Docs (2006) : The introduction of cloud based word processing revolutionized collaboration. Google Docs enabled real time editing and sharing, making it a staple - - for teams and remote work. +- Microsoft Office Suite: Microsoft continued to dominate with its Office Suite, integrating Word with other productivity tools like Excel and PowerPoint. +- OpenOffice and LibreOffice: Open-source alternatives emerged in the early 2000s, offering free and flexible word processing options. +- Google Docs (2006): The introduction of cloud-based word processing revolutionized collaboration. Google Docs enabled real-time editing and sharing, making it a staple for teams and remote work. ## Future of Word Processing -Today, word processors are more than just tools for typing. They integrate artificial intelligence for grammar and style suggestions (e.g., Grammarly), voice - to - text features, and advanced layout options. As AI continues to advance, word processors may evo lve into even more intuitive tools that predict user needs, automate repetitive tasks, and support richer multimedia integration. +Today, word processors are more than just tools for typing. They integrate artificial intelligence for grammar and style suggestions (e.g., Grammarly), voice-to-text features, and advanced layout options. As AI continues to advance, word processors may evolve into even more intuitive tools that predict user needs, automate repetitive tasks, and support richer multimedia integration. -From the clunky typewriters of the 19th century to the AI - powered cloud tools of today, the word processor has come a long way. It remains an essential tool for communication and creativity, shaping how we write and share ideas. +From the clunky typewriters of the 19th century to the AI-powered cloud tools of today, the word processor has come a long way. It remains an essential tool for communication and creativity, shaping how we write and share ideas. ## Specialized Word Processing Tools -In addition to general - purpose word processors, specialized tools have emerged to cater to specific industries and needs. These tools incorporate unique features tailored to their users' workflows: +In addition to general-purpose word processors, specialized tools have emerged to cater to specific industries and needs. These tools incorporate unique features tailored to their users' workflows: -- • Academic and Technical Writing : Tools like LaTeX gained popularity among academics, scientists, and engineers. Unlike traditional word processors, LaTeX focuses on precise formatting, particularly for complex mathematical equations, scientific papers, and technical documents. It relies on a markup language to produce polished documents suitable for publishing. -- • Screenwriting Software : For screenwriters, tools like Final Draft and Celtx are specialized to handle scripts for film and television. These programs automate the formatting of dialogue, scene descriptions, and other elements unique to screenwriting. -- • Legal Document Processors : Word processors tailored for legal professionals, like WordPerfect, offered features such as redlining (early version tracking) and document comparison. Even today, many law firms rely on these tools due to their robust formatting options for contracts a nd legal briefs. +- Academic and Technical Writing: Tools like LaTeX gained popularity among academics, scientists, and engineers. Unlike traditional word processors, LaTeX focuses on precise formatting, particularly for complex mathematical equations, scientific papers, and technical documents. It relies on a markup language to produce polished documents suitable for publishing. +- Screenwriting Software: For screenwriters, tools like Final Draft and Celtx are specialized to handle scripts for film and television. These programs automate the formatting of dialogue, scene descriptions, and other elements unique to screenwriting. +- Legal Document Processors: Word processors tailored for legal professionals, like WordPerfect, offered features such as redlining (early version tracking) and document comparison. Even today, many law firms rely on these tools due to their robust formatting options for contracts and legal briefs. ## Key Features That Changed Word Processing -The evolution of word processors wasn't just about hardware or software improvements — it was about the features that revolutionized how people wrote and edited. Some of these transformative features include: +The evolution of word processors wasn't just about hardware or software improvements-it was about the features that revolutionized how people wrote and edited. Some of these transformative features include: -- 1. Undo/Redo : Introduced in the 1980s, the ability to undo mistakes and redo actions made experimentation and error correction much easier. -- 2. Spell Check and Grammar Check : By the 1990s, these became standard, allowing users to spot errors automatically. -- 3. Templates : Pre - designed formats for documents, such as resumes, letters, and invoices, helped users save time. -- 4. Track Changes : A game changer for collaboration, this feature allowed multiple - users to suggest edits while maintaining the original text. -- 5. Real - Time Collaboration : Tools like Google Docs and Microsoft 365 enabled multiple users to edit the same document simultaneously, forever changing teamwork dynamics. +- 1. Undo/Redo: Introduced in the 1980s, the ability to undo mistakes and redo actions made experimentation and error correction much easier. +- 2. Spell Check and Grammar Check: By the 1990s, these became standard, allowing users to spot errors automatically. +- 3. Templates: Pre-designed formats for documents, such as resumes, letters, and invoices, helped users save time. +- 4. Track Changes: A game-changer for collaboration, this feature allowed multiple users to suggest edits while maintaining the original text. +- 5. Real-Time Collaboration: Tools like Google Docs and Microsoft 365 enabled multiple users to edit the same document simultaneously, forever changing teamwork dynamics. ## The Cultural Impact of Word Processors -The word processor didn't just change workplaces it changed culture. It democratized writing, enabling anyone with access to a computer to produce professional — quality - documents. This shift had profound implications for education, business, and creative fi elds: +The word processor didn't just change workplaces-it changed culture. It democratized writing, enabling anyone with access to a computer to produce professional-quality documents. This shift had profound implications for education, business, and creative fields: -- • Accessibility : Writers no longer needed expensive publishing equipment or training in typesetting to create polished work. This accessibility paved the way for selfpublishing, blogging, and even fan fiction communities. f -- • Education : Word processors became a cornerstone of education, teaching students not only how to write essays but also how to use technology effectively. Features like bibliography generators and integrated research tools enhanced learning. -- Creative Writing : Writers gained powerful tools to organize their ideas. Programs • like Scrivener allowed authors to manage large projects, from novels to screenplays, with features like chapter outlines and character notes. +- Accessibility: Writers no longer needed expensive publishing equipment or training in typesetting to create polished work. This accessibility paved the way for selfpublishing, blogging, and even fan fiction communities. +- Education: Word processors became a cornerstone of education, teaching students not only how to write essays but also how to use technology effectively. Features like bibliography generators and integrated research tools enhanced learning. +- Creative Writing: Writers gained powerful tools to organize their ideas. Programs like Scrivener allowed authors to manage large projects, from novels to screenplays, with features like chapter outlines and character notes. -## Word Processors in a Post Digital Era - +## Word Processors in a Post-Digital Era As we move further into the 21st century, the role of the word processor continues to evolve: -- 1. Artificial Intelligence : Modern word processors are leveraging AI to suggest content improvements. Tools like Grammarly, ProWritingAid, and even native features in Word now analyze tone, conciseness, and clarity. Some AI systems can even generate entire paragraphs or rewrite sen tences. -- 2. Integration with Other Tools : Word processors are no longer standalone. They integrate with task managers, cloud storage, and project management platforms. For instance, Google Docs syncs with Google Drive, while Microsoft Word integrates seamlessly with OneDrive and Teams. -- 3. Voice Typing : Speech text capabilities have made word processing more - to - accessible, particularly for those with disabilities. Tools like Dragon NaturallySpeaking and built - in options in Google Docs and Microsoft Word have made dictation mainstream. -- : Word processing has expanded beyond text. Modern tools 4. Multimedia Documents allow users to embed images, videos, charts, and interactive elements, transforming simple documents into rich multimedia experiences. -- 5. Cross Platform Accessibility : Thanks to cloud computing, documents can now be - accessed and edited across devices. Whether you're on a desktop, tablet, or smartphone, you can continue working seamlessly. +- 1. Artificial Intelligence: Modern word processors are leveraging AI to suggest content improvements. Tools like Grammarly, ProWritingAid, and even native features in Word now analyze tone, conciseness, and clarity. Some AI systems can even generate entire paragraphs or rewrite sentences. +- 2. Integration with Other Tools: Word processors are no longer standalone. They integrate with task managers, cloud storage, and project management platforms. For instance, Google Docs syncs with Google Drive, while Microsoft Word integrates seamlessly with OneDrive and Teams. +- 3. Voice Typing: Speech-to-text capabilities have made word processing more accessible, particularly for those with disabilities. Tools like Dragon NaturallySpeaking and built-in options in Google Docs and Microsoft Word have made dictation mainstream. +- 4. Multimedia Documents: Word processing has expanded beyond text. Modern tools allow users to embed images, videos, charts, and interactive elements, transforming simple documents into rich multimedia experiences. +- 5. Cross-Platform Accessibility: Thanks to cloud computing, documents can now be accessed and edited across devices. Whether you're on a desktop, tablet, or smartphone, you can continue working seamlessly. ## A Glimpse Into the Future The word processor's future lies in adaptability and intelligence. Some exciting possibilities include: -- Fully AI Assisted Writing : Imagine a word processor that understands your writing • - style, drafts emails, or creates entire essays based on minimal input. -- • Immersive Interfaces : As augmented reality (AR) and virtual reality (VR) technology advance, users may be able to write and edit in 3D spaces, collaborating in virtual environments. -- • Hyper - Personalization : Word processors could offer dynamic suggestions based on industry specific needs, user habits, or even regional language variations. - +- Fully AI-Assisted Writing: Imagine a word processor that understands your writing style, drafts emails, or creates entire essays based on minimal input. +- Immersive Interfaces: As augmented reality (AR) and virtual reality (VR) technology advance, users may be able to write and edit in 3D spaces, collaborating in virtual environments. +- Hyper-Personalization: Word processors could offer dynamic suggestions based on industry-specific needs, user habits, or even regional language variations. -The journey of the word processor— from clunky typewriters to AI powered platforms r— - — reflects humanity's broader technological progress. What began as a tool to simply replace handwriting has transformed into a powerful ally for creativity, communication, and collaboration. As technology continues to advance, the word processor will undoubtedly remain at the heart of how we express ideas and connect with one another. +The journey of the word processor-from clunky typewriters to AI-powered platforms- reflects humanity's broader technological progress. What began as a tool to simply replace handwriting has transformed into a powerful ally for creativity, communication, and collaboration. As technology continues to advance, the word processor will undoubtedly remain at the heart of how we express ideas and connect with one another. diff --git a/tests/pdf_snapshots/pdf/sources/normal_4pages.pdf.md b/tests/pdf_snapshots/pdf/sources/normal_4pages.pdf.md index b3415a86..8bc0e3f5 100644 --- a/tests/pdf_snapshots/pdf/sources/normal_4pages.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/normal_4pages.pdf.md @@ -4,116 +4,95 @@ 발행일 2020년 4월 2일 -2020년 4월 2일 발행일 발행처 국회입법조사처 발행인 김하중 국회입법조사처장 www.nars.go.kr +발행일 2020년 4월 2일 발행처 국회입법조사처 발행인 김하중 국회입법조사처장 발행처 국회입법조사처 발행인 김하중 국회입법조사처장 -www.nars.go.kr +## 코로나-19 관련 보험약관상 -## 코로나-19 관련 보험약관상 재해보험금 지급문제 및 개선과제 +2020.1.1. 「감염병예방법」 관련 규정이 변경되어 적용됨에 따라, 세계적 유행(Pandemic) 단계에 돌 입하였다고 세계보건기구(WHO)가 인정한 코로나-19와 관련하여, 보험회사에서 그동안 판매했거나 향후 판매하게 될 보험상품의 입원 및 사망의 재해 인정 여부에 대하여 보험실무상 혼선이 초래되어 이 에 대하여 보험약관의 현황 및 문제점을 살펴보고 그에 따른 개선방안을 제시하고자 하였다. -2020.1.1. 「감염병예방법」 관련 규정이 변경되어 적용됨에 따라, 세계적 유행(Pandemic) 단계에 돌 입하였다고 세계보건기구(WHO)가 인정한 코로나-1 9와 관련하여, 보험회사에서 그동안 판매했거나 향후 판매하게 될 보험상품의 입원 및 사망의 재해 인정 여부에 대하여 보험실무상 혼선이 초래되어 이 에 대하여 보험약관의 현황 및 문제점을 살 펴보고 그에 따른 개선방안을 제시하고자 하였다. - -## 들어가며 - -1 +## 1 들어가며 2020.3.30. 0시 기준 현재 "코로나바이러스감염 증-19(COVID-19, 이하 "코로나-19")"의 국내 확 진자는 9,661명, 사망자는 158명으로 나타났다. 이와 관련하여 세계보건기구(WHO)는 지난 2020. 3.11. 코로나-19가 세계적 유행(Pandemic) 단계 에 돌입하였음을 선언하였다. -한편 코로나-19가 전 세계적 유행단계에 돌입한 와중에 국내 보험업계에서는 코로나-19를 과연 질 병으로 보아야 할지, 상해 1) 나 재해 2) 로 보아야 할지 +한편 코로나-19가 전 세계적 유행단계에 돌입한 와중에 국내 보험업계에서는 코로나-19를 과연 질 병으로 보아야 할지, 상해1)나 재해2)로 보아야 할지 1) 손해보험의 표준약관 규정에 따르면, 보험기간 중에 발생한 급격하고도 우연한 외래의 사고로 신체에 입은 상해라고 규정하고 있는데, 즉, "급 격성, 우연성, 외래성"을 충족하는 사고를 상해로 정의함 2) 생명보험은 표준약관 "재해분류표"에서 "우발적인 외래의 사고"를 재 해라고 정의하며 보장대상이 되는 재해는 다음 중 어느 하나에 해당하 는 것을 말함 -1) 한국표준질병·사인분류상의 'S80~Y84'에 해당하는 우발적인 외래의 사고 +1) 한국표준질병·사인분류상의'S80~Y84'에 해당하는 우발적인 외래의 사고 -2) 감염병의 예방 및 관리에 관한 법률 제2조 제2호에서 규정한 감염병 그러나, 약관상 한국표준질병·사인분류상의 U00~U99에 해당하는 +2) 감염병의 예방 및 관리에 관한 법률 제2조 제2호에서 규정한 감염병 -제1695호 - -## 이슈 와 와 논점 - -김 창 호* - -이슈가 될 것으로 판단된다. - 통상 질병보험금에 비해 상해나 재해로 인한 보 험금의 경우 보장금액 등이 높게 책정되어 있다 보 니 코로나-19를 상해나 재해로 인정할 지 여부에 대하여 약관 상 명백한 규정이 없는 한 관련 분쟁이 지속적으로 발생할 가능성이 있다. 보험실무상 어떤 보험사는 코로나-19를 재해로 인정하여 재해보험금을 지급하고, 다른 보험사는 불인정하여 재해보험금을 지급하지 않는 등 혼선이 야기되고 있어, 코로나-19의 재해 인정 여부가 향 후 유사 바이러스 감염증 발생 시 보험금 지급 기준 을 정립함에 있어서 중요한 잣대가 될 것이다. 따라서 코로나-19와 관련하여 보험회사에서 이 미 판매했거나 향후 판매하게 될 보험상품의 약관 상 재해보험금의 입원 및 사망담보 적용여부에 대 하여 살펴보고 그에 따른 재해보험금 지급문제의 개선과제에 대하여 정리하고자 한다. -질병은 보장대상이 되지 않는 것으로 정의 - ## 2 코로나-19 관련 보험 현황 ## (1) 「감염병의 예방 및 관리에 관한 법률」 개정 -2020.1.1. 시행된 3) 「감염병의 예방 및 관리에 관 한 법률」(이하 '감염병예방법') 제2조 제2호에 의하 면 제1급감염병이란 생물테러감염병 또는 치명률 이 높거나 집단 발생의 우려가 커서 발생 또는 유행 즉시 신고하여야 하고, 음압격리와 같은 높은 수준 의 격리가 필요한 감염병으로서 제2호 각 목4 목4) 에 규 정된 감염병을 말한다. +2020.1.1. 시행된3) 「감염병의 예방 및 관리에 관 한 법률」(이하'감염병예방법') 제2조 제2호에 의하 면 제1급감염병이란 생물테러감염병 또는 치명률 이 높거나 집단 발생의 우려가 커서 발생 또는 유행 즉시 신고하여야 하고, 음압격리와 같은 높은 수준 의 격리가 필요한 감염병으로서 제2호 각 목4)에 규 정된 감염병을 말한다. -다만, 갑작스러운 국내 유입 또는 유행이 예견되 어 긴급한 예방ㆍ관리가 필요하여 보건복지부장관 이 지정하는 감염병을 포함한다고 설명하고 있다. +다만, 갑작스러운 국내 유입 또는 유행이 예견되 어 긴급한 예방 ㆍ 관리가 필요하여 보건복지부장관 이 지정하는 감염병을 포함한다고 설명하고 있다. [표 1] 감염병예방법 제2조제2호 개정전·후 비교 -| 구분 | | 개정전 | | 개정후 | -|------|----------------------|-------|------------------------|-------| -| 분류 | 1군 감염병 1급 감염병 | | | | -| | ◦ 마시는 물 또는 식품을 매개로 | | ◦ 생물테러감염병 또는 치명률이 | | -| 분류 | | | | | -| | 발생하고 집단 발생의 우려가 | | 높거나 집단 발생의 우려가 커 높 | | -| 기준 | | | | | -| | 큰 감염병 | | 은 수준의 격리가 필요한 감염병 | | -| | ◦ (6종) 콜레라, 장티푸스, 파라 | | ◦ (17종) 에볼라, 페스트 등 | | -| 대상 | | | * 좌측 6종(2급 감염병 분류)은 | | -| | 티푸스, 세균성이질, 장출혈 | | | | -| 질병 | | | | | -| | 성대장균감염증, A형간염 | | 미포함 | | -| | | | ◦ 대상질병에 U코드 일부(3종) | | -| | | | 포함 | | -| U코드 | ◦ 대상질병에 U코드 없음 | | ① 신종감염병증후군 → 코로나19(U) | | -| | | | ② 중증급성호흡기증후군(SARS) (U) | | -| | | | ③ 중동호흡기증후군(MERS)(U) | | +| 구분 개정전 개정후 | | | +|---------------------------------------|--------------------------------------------------|------------------------| +| 분류 1군 감염병 1급 감염병 | | | +| 분류 기준 ◦ 마시는 물 또는 식품을 매개로 | 큰 감염병 ◦ 생물테러감염병 또는 치명률이 발생하고 집단 발생의 우려가 | 높거나 집단 발생의 우려가 커 높 | +| | | 은 수준의 격리가 필요한 감염병 | +| 질병 ◦ (6종) 콜레라, 장티푸스, 파라 대상 | 성대장균감염증, A형간염 ◦ (17종) 에볼라, 페스트 등 티푸스, 세균성이질, 장출혈 | * 좌측 6종(2급 감염병 분류)은 | +| | | 미포함 | +| U코드 ◦ 대상질병에 U코드 없음 ◦ 대상질병에 U코드 일부(3종) | | 포함 | +| | | ① 신종감염병증후군 → 코로나19(U) | +| | | ② 중증급성호흡기증후군(SARS) (U) | +| | | ③ 중동호흡기증후군(MERS)(U) | ※ 주: 2020.1.1. 기준 -※ 자료: 생명보험협회, '20.3.11. +※ 자료: 생명보험협회,'20.3.11. -기존 생명보험에서 재해로 보장하는 법정1군 전염 병은 "콜레라, 장티푸스, 파라티푸스, 세균성이질, 장 출혈성대장균감염증, A형간염"이었으나 이번에 개 +기존 생명보험에서 재해로 보장하는 법정1군 전염 병은 " 콜레라, 장티푸스, 파라티푸스, 세균성이질, 장 출혈성대장균감염증, A형간염"이었으나 이번에 개 -3) 개정 이유는 질환의 특성별 '군(群)'별로 구분되어 있는 현행 감염병 분 류체계를 감염병의 심각도ㆍ전파력ㆍ격리수준ㆍ신고시기 등을 중심으 로 한 '급(級)'별 분류체계로 개편하고, 감염병 위기상황 발생 시 컨트롤 타워 역할을 수행할 수 있는 긴급상황실의 설치ㆍ운영과 감염병환자와 접촉한 자를 격리할 수 있는 접촉자격리시설의 지정을 위한 법적 근거 를 신설하며, 감염병관리위원회 위원장을 보건복지부차관에서 질병관 리본부장으로 변경하는 등 감염병 발생 시 보다 효율적인 대처가 이루 어질 수 있도록 현행 감염병 관리체계를 개선ㆍ보완하려는 것임. +3) 개정 이유는 질환의 특성별'군(群)'별로 구분되어 있는 현행 감염병 분 류체계를 감염병의 심각도 ㆍ 전파력 ㆍ 격리수준 ㆍ 신고시기 등을 중심으 로 한'급(級)'별 분류체계로 개편하고, 감염병 위기상황 발생 시 컨트롤 타워 역할을 수행할 수 있는 긴급상황실의 설치 ㆍ 운영과 감염병환자와 접촉한 자를 격리할 수 있는 접촉자격리시설의 지정을 위한 법적 근거 를 신설하며, 감염병관리위원회 위원장을 보건복지부차관에서 질병관 리본부장으로 변경하는 등 감염병 발생 시 보다 효율적인 대처가 이루 어질 수 있도록 현행 감염병 관리체계를 개선 ㆍ 보완하려는 것임. -4) 2. 가. ~ 더.; 에볼라바이러스병, 마버그열, 라싸열, 크리미안콩고출혈 열, 남아메리카출혈열, 리프트밸리열, 두창, 페스트, 탄저, 보툴리눔독 신종감염병증후군, 중증급성호흡기증후군(SARS), 중동 소증, 야토병, 호흡기증후군(MERS), 동물인플루엔자 인체감염증, 신종인플루엔자, 디프테리아 등 17종 +4) 2. 가. ~ 더.; 에볼라바이러스병, 마버그열, 라싸열, 크리미안콩고출혈 열, 남아메리카출혈열, 리프트밸리열, 두창, 페스트, 탄저, 보툴리눔독 소증, 야토병, 신종감염병증후군, 중증급성호흡기증후군(SARS), 중동 호흡기증후군(MERS), 동물인플루엔자 인체감염증, 신종인플루엔자, -정된 법에서 이들은 제2급감염병으로 변경되었다. 대신에 「감염병예방법」 제1급감염병으로 "에볼 라바이러스병, ~ 신종감염증후군, SARS, MERS, ~ 디프테리아"등 17종이 새롭게 포함되었다. +대신에 「감염병예방법」 제1급감염병으로 "에볼 라바이러스병, ~ 신종감염증후군, SARS, MERS, ~ 디프테리아"등 17종이 새롭게 포함되었다. ## (2) 생명보험 표준약관 재해분류표 -현행 생명보험 표준약관상 재해분류표5 표5) 는 위 「감염병예방법」 제2조 제2호의 제1급 감염병들을 질병임에도 불구하고 보장대상이 되는 '재해'로 규 정하고 있다. +현행 생명보험 표준약관상 재해분류표5)는 위 「감염병예방법」 제2조 제2호의 제1급 감염병들을 질병임에도 불구하고 보장대상이 되는'재해'로 규 정하고 있다. -그러나 제1급 감염병에 포함되는 질병임에도 불 구하고, 생명보험 표준약관 재해분류표의 한국표 준질병·사인분류6 류6) (이하 'KCD'라 함)상 U코드 (U00~U99)에 해당하는 질병들(SARS-U04.9, MERS-U19.9 등)은 보장제외 대상으로 분류되어 보험금을 지급하지 않는 재해로 규정되어 있다. +그러나 제1급 감염병에 포함되는 질병임에도 불 구하고, 생명보험 표준약관 재해분류표의 한국표 준질병 ·사인분류6)(이하'KCD'라 함)상 U코드 (U00~U99)에 해당하는 질병들(SARS-U04.9, MERS-U19.9 등)은 보장제외 대상으로 분류되어 보험금을 지급하지 않는 재해로 규정되어 있다. 코로나-19 역시 KCD 수록 정식 명칭은 "코로나 바이러스 질환 2019"로 질병분류기호는 "U07.1" 로 표시하고 있어서 보험금이 지급되지 않는 재해 로 분류되어 재해보험금 지급대상에 포함되지 않는 다고 해석될 수 있다. 한편, 현재 생명보험 표준약관은 2020.1.1.부터 신규 판매되고 있는 보험상품의 약관이며 해당 약 관에는 아직 상기 법 개정사항이 반영되지 않은 상 황이지만, 재해분류표를 보면 별도의 각주를 통해 "감염병에 관한 법률이 제·개정될 경우, 보험사고 발생 당시 제·개정된 법률을 적용합니다."라고 명 시되어 코로나-19를 재해보험금 지급대상에 포함 되는 것으로도 해석할 수 있다. -- (3) 「약관의 규제에 관한 법률」상 약관해석의 원칙 현재 「약관의 규제에 관한 법률」 7) 을 보면 약관은 +- (3) 「약관의 규제에 관한 법률」상 약관해석의 원칙 현재 「약관의 규제에 관한 법률」7)을 보면 약관은 5) 「생명보험 표준약관 부표4」 -6) 한국표준질병사인분류(Korean Standard Cl assification of Diseases, "KCD")는 대한민국에서 의무기록자료 및 사망원인통계조사 등 질병이 환 및 사망자료를 그 성질의 유사성에 따라 체계적으로 유형화한 것으로, 모든 형태의 보건 및 인구동태 기록에 기재되어 있는 질병 및 기타 보건 문제를 분류하는데 이용하기 위하여 설정한 으로 통계청에서 작성함 +6) 한국표준질병사인분류(Korean Standard Classification of Diseases, "KCD")는 대한민국에서 의무기록자료 및 사망원인통계조사 등 질병이 환 및 사망자료를 그 성질의 유사성에 따라 체계적으로 유형화한 것으로, 모든 형태의 보건 및 인구동태 기록에 기재되어 있는 질병 및 기타 보건 문제를 분류하는데 이용하기 위하여 설정한 으로 통계청에서 작성함 7) 제5조(약관의 해석) ① 약관은 신의성실의 원칙에 따라 공정하게 해석 되어야 하며 고객에 따라 다르게 해석되어서는 아니 된다. ② 약관의 뜻이 명백하지 아니한 경우에는 고객에게 유리하게 해석되어 야 한다. -작성자인 사업자에 의하여 일방적으로 유리하게 작 성되고 고객에게는 그 약관내용에 관한 교섭이나 검토의 기회가 제대로 주어지지 않는 것이 일반적 이므로 이러한 형성과정에 비추어 고객보호의 측면 에서 약관내용이 명백하지 못하거나 의심스러운 때 에는 고객에게 유리하게 해석되어야 한다는 '작성 자 불이익의 해석원칙'이 있다. +작성자인 사업자에 의하여 일방적으로 유리하게 작 성되고 고객에게는 그 약관내용에 관한 교섭이나 검토의 기회가 제대로 주어지지 않는 것이 일반적 이므로 이러한 형성과정에 비추어 고객보호의 측면 에서 약관내용이 명백하지 못하거나 의심스러운 때 에는 고객에게 유리하게 해석되어야 한다는'작성 자 불이익의 해석원칙'이 있다. ## (4) 생보업계의 재해보험금 지급에 대한 의견 @@ -129,15 +108,15 @@ www.nars.go.kr ## (2) 감독당국의 표준약관 개정작업 소홀 -통상 보험사는 금융감독원이 보험상품의 표준약 관을 변경한 후 보험회사의 개별 상품에 대한 약관 변경작업을 진행하는데, 이번 경우는 「감염병예방 법」이 변경 시행되었음에도 불구하고 금융감독당 국이 생명보험 표준약관 개정작업을 시의적절하게 이행하지 못하는 바람에 나타난 혼선이다. +통상 보험사는 금융감독원이 보험상품의 표준약 관을 변경한 후 보험회사의 개별 상품에 대한 약관 변경작업을 진행하는데, 이번 경우는 「감염병예방 법」이 변경 시행되었음에도 불구하고 금융감독당 국이 생명보험 표준약관 개정작업을 시의적절하게 -개정된 「감염병예방법」이 시행된 후 보험회사에 서 약관 개정 시 해당 내용을 어떻게 규정할 지에 대 하여 미리 검토가 요구되며 이는 보험회사와 금융 당국 간의 적절한 절차를 거쳐 사전논의를 했어야 하는 사항이라고 판단된다. +서 약관 개정 시 해당 내용을 어떻게 규정할 지에 대 하여 미리 검토가 요구되며 이는 보험회사와 금융 당국 간의 적절한 절차를 거쳐 사전논의를 했어야 하는 사항이라고 판단된다. 개별 보험사의 모든 상품을 심사하는 내용이 아 니라 생명보험 표준약관의 규정을 관련법과 비교하 여 개정하는 작업을 소홀히 한 만큼 금융감독당국 의 책임이 크다고 할 수 있다. ## (3) 보험사의 보험금 지급실무상 혼선 초래 -보험업계는 보험은 사고 위험의 예측가능성과, 8) 보험의 기본원리인 대수의 법칙 이나 수지상등의 원칙 9) 에 부합하고, 최대 손실을 보험회사가 감당할 수 있어야 한다고 주장한다. +보험업계는 보험은 사고 위험의 예측가능성과, 보험의 기본원리인 대수의 법칙8)이나 수지상등의 원칙9)에 부합하고, 최대 손실을 보험회사가 감당할 수 있어야 한다고 주장한다. 특히, 신종감염병증후군과 신종인플루엔자는 특 정 질병이 아닌 앞으로 새롭게 발생할 모든 신종감 염병을 포괄하는 개념으로 위험률 측정이 불가능하 고, 담보 범위도 확정할 수도 없다고 주장한다. @@ -151,8 +130,6 @@ www.nars.go.kr 9) 수지가 같아진다는 것은 다수의 동일연령의 피보험자가 같은 보험종류 를 동시에 계약했을 때 보험기간 만료시에 수입과 지출이 균형이 잡혀 지도록 순보험료를 계산하는 것을 의미함 -이슈와 논점 - ## 4 개선과제 ## (1) 「약관규제법」에 따른 보험금 지급 검토 필요 @@ -165,17 +142,15 @@ www.nars.go.kr 우선 「감염병예방법」 변경에 따른 감독당국의 조속한 표준약관 개정작업이 진행되어야 할 필요가 있다. -보험사가 생명보험에서 '일부 감염병'을 재해로 보장하는 이유는 '일부 감염병'이 우연성, 외래성, 급격성 등 재해의 특성을 지니고 있기 때문이며, 실 례로 페스트는 질병이지만 재해에 준하는 급격성도 지니고 있어, 정책적으로 재해로 취급하여 보험의 보장 범위를 확대한 바 있다. +보험사가 생명보험에서'일부 감염병'을 재해로 보장하는 이유는'일부 감염병'이 우연성, 외래성, 급격성 등 재해의 특성을 지니고 있기 때문이며, 실 례로 페스트는 질병이지만 재해에 준하는 급격성도 지니고 있어, 정책적으로 재해로 취급하여 보험의 보장 범위를 확대한 바 있다. 코로나-19 역시 질병이지만 세계적 유행단계에 돌입하는 등 페스트와 같은 재해에 준하는 성격을 포함하고 있어 이를 재해로 인정할 필요가 있다. -아울러 코로나-19의 경우, 국내 경제를 넘어 세 계경제의 동반침체를 일으키고 있는 바, 금융감독 당국이 적극적 경기부양정책에 부응하는 보험정책 을 시행하는 것이 코로나-19로 지친 국민에게 경제 적 위안을 제공하는 것이라 할 수 있다. - -## (3) 신종위험에 대비하는 보험상품 개발 필요 +아울러 코로나-19의 경우, 국내 경제를 넘어 세 계경제의 동반침체를 일으키고 있는 바, 금융감독 당국이 적극적 경기부양정책에 부응하는 보험정책 을 시행하는 것이 코로나-19로 지친 국민에게 경제 -보험사는 기후변화, 전염병 등과 같은 신종위험 으로 인한 사회적 손실이 증가하는 추세에 있는바 손실이 광범위하고 직·간접적이어서 손해 규모를 측정하기 어려운 경우를 대비한 보험상품을 개발할 필요가 있다. +보험사는 기후변화, 전염병 등과 같은 신종위험 으로 인한 사회적 손실이 증가하는 추세에 있는바 손실이 광범위하고 직 ·간접적이어서 손해 규모를 측정하기 어려운 경우를 대비한 보험상품을 개발할 필요가 있다. -따라서 감염병 보험, 파라메트릭(Parametric Insurance)보험 10) , 인덱스(Index)보험 등과 같이 실제 발생한 손실액이 아니라 특정 지표에 의해 보 험금이 지급되는 신종보험상품을 개발 보급 판매할 필요가 점진적으로 증가하고 있다. +따라서 감염병 보험, 파라메트릭(Parametric Insurance)보험10), 인덱스(Index)보험 등과 같이 실제 발생한 손실액이 아니라 특정 지표에 의해 보 험금이 지급되는 신종보험상품을 개발 보급 판매할 필요가 점진적으로 증가하고 있다. ## 5 맺으며 @@ -187,7 +162,7 @@ www.nars.go.kr 코로나-19로 인한 국민의 경제손실을 보충하고 보험의 기본원리에 충실한 경제제도로서의 역할을 수행하도록 금융감독당국과 보험업계의 현명하고 빠른 보험약관의 제도적 보완이 필요한 시점이다. -󰡔 이슈와 논점 󰡕 은 국회의원의 입법활동을 지원하기 위해 최신 국내외 동향 및 현안에 대해 수시로 발간하는 정보 소식지입니다. +이슈와 논점 은 국회의원의 입법활동을 지원하기 위해 최신 국내외 동향 및 현안에 대해 수시로 발간하는 정보 소식지입니다. 10) 인덱스 보험이라고도 불리며, 손실규모를 측정하기 어려운 홍수나 자 연재해 리스크를 보상하는 보험으로 실제로 발생한 손실금액을 보상 하는 실손보상의 원칙이 적용되지 않고 강수량, 풍속, 온도 등과 같은 개관적인 지표에 의해서 보상이 결정되는 보험을 말함 diff --git a/tests/pdf_snapshots/pdf/sources/redp5110_sampled.pdf.md b/tests/pdf_snapshots/pdf/sources/redp5110_sampled.pdf.md index a248ba77..b6611569 100644 --- a/tests/pdf_snapshots/pdf/sources/redp5110_sampled.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/redp5110_sampled.pdf.md @@ -1,40 +1,28 @@ -Implement roles and separation of duties +of duties Leverage row permissions on the database Protect columns by defining column masks -ibm.com /redbooks - -## Row and Column Access Control Support in IBM DB2 for i +## Row and Column Access Control -Jim Bainbridge Hernando Bedoya Rob Bestgen Mike Cain Dan Cruikshank Jim Denton Doug Mack Tom McKinley Kent Milligan - -Red paper - -## Contents +Hernando Bedoya Rob Bestgen Mike Cain Dan Cruikshank Jim Denton Doug Mack Tom McKinley -IBM Systems Lab Services and Training Solution Brief - -## Highlights - --              --                    --      !   " #  -- ! #      " "       +-                +-                     +-       !   " #  +-  ! #      " "       -Power Services - ## DB2 for i Center of Excellence Expert help to achieve your business requirements @@ -45,71 +33,61 @@ No one else has the vast consulting experiences, skills sharing and renown servi Because no one else is IBM. -With combined experiences and direct access to development groups, we're the experts in IBM DB2® for i. The DB2 for i Center of Excellence (CoE) can help you achieve—perhaps reexamine and exceed—your business requirements and gain more confidence and satisfaction in IBM product data management products and solutions. +With combined experiences and direct access to development groups, we're the experts in IBM DB2® for i. The DB2 for i Center of Excellence (CoE) can help you achieve-perhaps reexamine and exceed- your business requirements and gain more confidence and satisfaction in IBM product data management products and solutions. ## Who we are, some of what we do Global CoE engagements cover topics including: -- Database performance and scalability r -- r Advanced SQL knowledge and skills transfer -- r Business intelligence and analytics -- DB2 Web Query r -- r Query/400 modernization for better reporting and analysis capabilities -- r Database modernization and re-engineering -- Data-centric architecture and design r -- r Extremely large database and overcoming limits to growth -- r ISV education and enablement - -## Preface - -This IBM® Redpaper™ publication provides information about the IBM i 7.2 feature of IBM DB2® for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of co ntrolling access to data in a comprehensive and transparent way. This publication helps you understand th e capabilities of RCAC an d provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment. +- r Database performance and scalability +- r Advanced SQL knowledge and skills transfer +- r Business intelligence and analytics +- r DB2 Web Query +- r Query/400 modernization for better reporting and analysis capabilities +- r Database modernization and re-engineering +- r Data-centric architecture and design +- r Extremely large database and overcoming limits to growth -This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IB M i object level security, DB2 for i relational database concepts, and SQL is assumed. +This IBM® Redpaper™ publication provides information about the IBM i 7.2 feature of IBM DB2® for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment. -## Authors +This paper is intended for database engineers, data-centric application developers, and secu rity officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed. -This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with the International Technical Support Organization (ITSO), Rochester, Minnesota US. +the International Technical Support Organization (ITSO), Rochester, Minnesota US. -Jim Bainbridge is a senior DB2 consultant on the DB2 for i Center of Excellence team in the IBM Lab Services and Training organization. His primary role is training and implementation services for IBM DB2 Web Query for i and business analytics. Jim began his career with IBM 30 years ago in the IBM Rochester Development Lab, where he developed cooperative processing products that paired IBM PCs with IBM S/36 and AS/.400 systems. In the years since, Jim has held numerous technical roles, including independent software vendors technical support on a broad range of IBM technologies and products, and supporting customers in the IBM Executive Briefing Center and IBM Project Office. +Jim Bainbridge is a senior DB2 consultant on the DB2 for i Center of Excellence team in the IBM Lab Services and Training organization. His primary role is training and implementation services for IBM DB2 Web Query for i and business analytics. Jim began his career with IBM 30 years ago in the IBM Rochester Development Lab, where he developed cooperative processing products that paired IBM PCs with IBM S/36 and AS/.400 systems. In the years since, Jim has held nu merous technical roles, including independent software vendors technical support on a broad range of IBM technologies and products, and supporting customers in the IBM Executive Briefing Center and IBM Project Office. -Hernando Bedoya is a Senior IT Specialist at STG Lab Services and Training in Rochester, Minnesota. He writes extensively and teaches IBM classes worldwide in all areas of DB2 for i. Before jo ining STG Lab Services, he worked in the ITSO for nine years writing multiple IBM Redbooks® publications. He also worked for IBM Colombia as an IBM AS/400® IT Specialist doing presales support for the Andean countries. He has 28 years of experience in the computing field and has taught database classes in Colombian universities. He holds a Master's degree in Computer Science from EAFIT, Colombia. His areas of expertise are database technology, performance, and data warehousing. Hernando can be contacted at hbedoya@us.ibm.com . +Hernando Bedoya is a Senior IT Specialist at STG Lab Services and Training in Rochester, Minnesota. He writes extensively and teaches IBM classes worldwide in all areas of DB2 for i. Before joining STG Lab Services, he worked in the ITSO for nine years writing multiple IBM Redbooks® publications. He also worked for IBM Colombia as an IBM AS/400® IT Specialist doing presales support for the Andean countries. He has 28 years of experience in the computing field and has taught database classes in Colombian universities. He holds a Master's degree in Computer Science from EAFIT , Colombia. His areas of expertise are database technology, performance, and data warehousing. Hernando can be contacted at hbedoya@us.i bm.com. -## 1 +## data -## Securing and protecting IBM DB2 data +global businesses of all sizes. The Identity Theft Resource Center1 reports that almost 5000 Recent news headlines are filled with reports of data breaches and cyber-attacks impacting financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute2 data breaches have occurred since 2005, exposing over 600 million records of data. The revealed that the average cost of a data breach increased in 2013 by 15% globally and resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for each lost record containing sensitive information increased more than 9% to $145 per record. -Recent news headlines are fille d with reports of data breache s and cyber-attacks impacting global businesses of all sizes. The Identity Theft Resource Center 1 reports that almost 5000 data breaches have occurred since 2005, expo sing over 600 million records of data. The financial cost of these data breaches is skyr ocketing. Studies from the Ponemon Institute 2 revealed that the average cost of a data breach increased in 2013 by 15% globally and resulted in a brand equity loss of $9.4 million per attack. The aver age cost that is incurred for each lost record containing sensitive information increased more than 9% to $145 per record. - -Businesses must make a seriou s effort to secure their data and recognize that securing information assets is a cost of doing business. In many parts of the world and in many industries, securing the data is required by law and subject to audits. Data security is no longer an option; it is a requirement. +Businesses must make a serious effort to secure their data and recognize that securing information assets is a cost of doing business. In many parts of the world and in many industries, securing the data is required by law and subject to audits. Data security is no longer an option; it is a requirement. This chapter describes how you can secure and protect data in DB2 for i. The following topics are covered in this chapter: - Security fundamentals - Current state of IBM i security -- DB2 for i security controls - -1 http://www.idtheftcenter.org -2 http://www.ponemon.org / +1 http : //www.i dtheftcenter.org -## 1.1 Security fundamentals +2 http : //www.ponemon.org/ Before reviewing database security techniques, there are two fundamental steps in securing information assets that must be described: -- First, and most important, is the definition of a company's security policy. y. Without a security policy, there is no definition of what are acceptable practices for using, accessing, and storing information by who, what, when, where, and how. A security policy should minimally address three things: confidentiality, integrity, and availability. +- First, and most important, is the definition of a company's security policy. Without a secu rity policy, there is no definition of what are acceptable practices for using, accessing, and storing information by who, what, when, where, and how. A security policy should minimally address three things: confidentiality, integrity, and availability. -The monitoring and assessment of adherence to the security policy determines whether your security strategy is working. Often, IBM security consultants are asked to perform security assessments for companies without regard to the security policy. Although these assessments can be useful for observing how the system is defined and how data is being accessed, they cannot determine the level of security without a security policy. Without a security policy, it really is no t an assessment as much as it is a baseline for monitoring the changes in the security settings that are captured. +The monitoring and assessment of adherence to the security policy determines whether your security strategy is working. Often, IBM security consultants are asked to perform secu rity assessments for companies without regard to the security policy. Although these assessments can be useful for observing how the system is defined and how data is being accessed, they cannot determine the level of security without a security policy. Without a secu rity policy, it really is not an assessment as much as it is a baseline for monitoring the changes in the security settings that are captured. A security policy is what defines whether the system and its settings are secure (or not). -- The second fundamental in securing data assets is the use of resource security. y. If implemented properly, resource security prevents data breaches from both internal and external intrusions. Resource security controls are closely tied to the part of the security policy that defines who should have access to what information resources. A hacker might be good enough to get through your company firewalls and sift his way through to your system, but if they do not have explicit access to your database, the hacker cannot compromise your information assets. +- The second fundamental in securing data assets is the use of resource security. If implemented properly, resource security prevents data breaches from both internal and external intrusions. Resource security controls are closely tied to the part of the security policy that defines who should have access to what information resources. A hacker might be good enough to get through your company firewalls and sift his way through to your system, but if they do not have explicit access to your database, the hacker cannot compromise your information assets. With your eyes now open to the importance of securing information assets, the rest of this chapter reviews the methods that are available for securing database resources on IBM i. @@ -119,120 +97,98 @@ Because of the inherently secure nature of IBM i, many clients rely on the defau Even more disturbing is that many IBM i clients remain in this state, despite the news headlines and the significant costs that are involved with databases being compromised. This default security configuration makes it quite challenging to implement basic security policies. A tighter implementation is required if you really want to protect one of your company's most valuable assets, which is the data. -Traditionally, IBM i applications have employed menu-based security to counteract this default configuration that gives all users access to the data. The theory is that data is protected by the menu options controlling what database op erations that the user can perform. This approach is ineffective, even if the user profile is restricted from running interactive commands. The reason is that in today's connected world there are a multitude of interfaces into the system, from web browsers to PC clients, that bypass application menus. If there are no object-level controls, users of these newer interfaces have an open door to your data. +Traditionally, IBM i applications have employed menu-based security to counteract this default configuration that gives all users access to the data. The theory is that data is protected by the menu options controlling what database operations that the user can perform. This approach is ineffective, even if the user profile is restricted from running interactive commands. The reason is that in today's connected world there are a multitude of interfaces into the system, from web browsers to PC clients, that bypass application menus. If there are no object-level controls, users of these newer interfaces have an open door to your data. -Many businesses are trying to limit data access to a need-to-know basis. This security goal means that users should be given access only to the minimum set of data that is required to perform their job. Often, users with object-lev el access are given access to row and column values that are beyond what their business ta sk requires because that object-level security provides an all-or-nothing solution. For example, object-level controls allow a manager to access data about all employees. Most security policies limit a manager to accessing data only for the employees that they manage. +means that users should be given access only to the minimum set of data that is required to perform their job. Often, users with object-level access are given access to row and column values that are beyond what their business task requires because that object-level security provides an all-or-nothing solution. For example, object-level controls allow a manager to access data about all employees. Most security policies limit a manager to accessing data only for the employees that they manage. ## 1.3.1 Existing row and column control -Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL views (or logical files) and application lo gic, as shown in Figure 1-2. However, application-based logic is easy to bypass with all of the different data access interfaces that are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) and System i Navigator. +Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL views (or logical files) and application logic, as shown in Figure 1 -2. However, application-based logic is easy to bypass with all of the different data access interfaces that are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) and System i Navigator. Using SQL views to limit access to a subset of the data in a table also has its own set of challenges. First, there is the complexity of managing all of the SQL view objects that are used for securing data access. Second, scaling a view-based security solution can be difficult as the amount of data grows and the number of users increases. -Even if you are willing to live with these perf ormance and management issues, a user with *ALLOBJ access still can directly access all of th e data in the underlying DB2 table and easily bypass the security controls that are built into an SQL view. - -Figure 1-2 Existing row and column controls +Even if you are willing to live with these performance and management issues, a user with *ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily bypass the security controls that are built into an SQL view. -## 2.1.6 Change Function Usage CL command - The following CL commands can be used to work with, display, or change function usage IDs: -- Work Function Usage ( WRKFCNUSG ) -- Change Function Usage ( CHGFCNUSG ) -- Display Function Usage ( DSPFCNUSG ) +- Work Function Usage (WRKFCNUSG) +- Change Function Usage (CHGFCNUSG) +- Display Function Usage (DSPFCNUSG) For example, the following CHGFCNUSG command shows granting authorization to user HBEDOYA to administer and manage RCAC rules: -CHGFCNUSG FCNID(QIBM_DB_SECADM) USER(HBEDOYA) USAGE(*ALLOWED) +CHGFCNUSG FCN I D(QIBM_DB_SECADM) USER(HBEDOYA) USAGE (*ALLOWED) ## 2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 describes the columns in the FUNCTION_USAGE view. -Table 2-1 FUNCTION_USAGE view +Table 2- 1 FUNCTION _USAGE view -| Column name Data type Description | | | | -|---------------------------------------------|-----------|--------------------------------------------------------------|-----------------------------------------| -| FUNCTION_ID VARCHAR(30) ID of the function. | | | | -| USER_NAME VARCHAR(10) Name of the user pr | | | ofile that has a usage setting for this | -| | function. | | | -| USAGE VARCHAR(7) Usage setting: | | | | -| | | ALLOWED: The user profile is allowed to use the function. | | -| | | DENIED: The user profile is not allowed to use the function. | | -| USER_TYPE VARCHAR(5) Type of user profile: | | | | -| | | USER: The user profile is a user. | | -| | | GROUP: The user profile is a group. | | +| FUNCTION_ID VARCHAR(30) ID of the function. | | +|----------------------------------------------------------------------------------|--------------------------------------------------------------| +| USER_NAME VARCHAR(10) Name of the user profile that has a usage setting for this | | +| | function. | +| USAGE VARCHAR(7) Usage setting: | | +| | ALLOWED: The user profile is allowed to use the function. | +| | DENIED: The user profile is not allowed to use the function. | +| USER_TYPE VARCHAR(5) Type of user profile: | | +| | USER: The user profile is a user. | +| | GROUP: The user profile is a group. | To discover who has authorization to define and manage RCAC, you can use the query that is shown in Example 2-1. Example 2-1 Query to determine who has authority to define and manage RCAC -| SELECT function_id, | | -|------------------------------------|------------| -| | user_name, | -| | usage, | -| | user_type | -| FROM function_usage | | -| WHERE function_id=’QIBM_DB_SECADM’ | | -| ORDER BY user_name; | | +| SELECT functi on_i d, | | +|--------------------------------------|---------------| +| | u s er_n ame, | +| | u s age, | +| | u s er_type | +| FROM functi on_usage | | +| WHERE functi on_i d=’QIBM_DB_SECADM’ | | +| ORDER BY user_name; | | ## 2.2 Separation of duties -Separation of duties helps businesses comply with industry regulations or organizational requirements and simplifies the management of authorities. Separation of duties is commonly used to prevent fraudulent activities or errors by a single person. It provides the ability for administrative functions to be divided across i ndividuals without overl apping responsibilities, so that one user does not possess unlimited authority, such as with the *ALLOBJ authority. +Separation of duties helps businesses comply with industry regulations or organizational requirements and simplifies the management of authorities. Separation of duties is commonly used to prevent fraudulent activities or errors by a single person. It provides the ability for administrative functions to be divided across individuals without overlapping responsibilities, -For example, assume that a business has assigned the duty to manage security on IBM i to Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same privileges Theresa was granting to others. Therefore, to grant *USE privileges to the PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the PAYROLL table even though Theresa's job description was only to manage its security. +Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same privileges Theresa was granting to others. Therefore, to grant *USE privileges to the PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the PAYROLL table even though Theresa's job description was only to manage its security. In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, changes ownership, or changes the primary group without giving access to the object or, in the case of a database table, to the data that is in the table or allowing other operations on the table. QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special authority and can be given to a user or a group. -QIBM_DB_SECADM also is responsible for admi nistering RCAC, which restricts which rows a user is allowed to access in a table and whether a user is allowed to see information in certain columns of a table. +QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows a user is allowed to access in a table and whether a user is allowed to see information in certain columns of a table. A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function usage ID, but absolutely no other data privileges. The result is that the RCAC administrator can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized access to data itself. Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to the different CL commands and DB2 for i tools. -Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority +Table 2-2 Comparison of the different function usage IDs and * JOBCTL authority -| User action | | | | | | QIBM_DB_SECADM | QIBM_DB_SQLADM | QIBM_DB_SYSMON | | -|-----------------------------------------------------------------------------|------------------------------------------|----------|--------------------------------------------------------|-----------------------------------|---------|------------------|------------------|------------------|--------------| -| | | | | | *JOBCTL | | | | No Authority | -| SET CURRENT DEGREE | | | (SQL statement) | | XX | | | | | -| CHGQRYA | command targeting a different user's job | | | | XX | | | | | -| STRDBMON | or | ENDDBMON | commands targeting a different user's job | | XX | | | | | -| STRDBMON | or | ENDDBMON | commands targeting a job that matches the current user | | X XXX | | | | | -| QUSRJOBI() API format 900 or System | | | | i Navigator's SQL Details for Job | X XX | | | | | -| Visual Explain within Run SQL scripts | | | | | X XXX | | | | | -| Visual Explain outside of Run SQL scripts | | | | | XX | | | | | -| ANALYZE PLAN CACHE procedure | | | | | XX | | | | | -| DUMP PLAN CACHE procedure | | | | | XX | | | | | -| MODIFY PLAN CACHE procedure | | | | | XX | | | | | -| MODIFY PLAN CACHE PROPERTIES procedure (currently does not check authority) | | | | | XX | | | | | -| CHANGE PLAN CACHE SIZE procedure (currently does not check authority) | | | | | XX | | | | | +| User action *JOBCTL QIBM_DB_SECADM QIBM_DB_SQLADM QIBM_DB_SYSMON No Authority SET CURRENT DEGREE (SQL statement) X X STRDBMON or ENDDBMON commands targeting a different user's job X X STRDBMON or ENDDBMON commands targeting a job that matches the current user X X X X CHGQRYA command targeting a different user's job X X Visual Explain within Run SQL scripts X X X X Visual Explain outside of Run SQL scripts X X ANALYZE PLAN CACHE procedure X X QUSRJOBI() API format 900 or System i Navigator's SQL Details for Job X X X CHANGE PLAN CACHE SIZE procedure (currently does not check authority) X X MODIFY PLAN CACHE procedure X X MODIFY PLAN CACHE PROPERTIES procedure (currently does not check authority) X X DUMP PLAN CACHE procedure X X | +|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -The SQL CREATE PERMISSION statement that is shown in Figure 3-1 is used to define and initially enable or disable the row access rules. +initially enable or disable the row access rules. -Figure 3-1 CREATE PER ERMISSION SQL statement +Figure 3-1 CREATE PERMISSION SQL statement ## Column mask -A column mask is a database object that manifests a column value access control rule for a specific column in a specific table. It uses a CASE expression that describes what you see when you access the column. For example, a teller can see only the last four digits of a tax identification number. - -Table 3-1 summarizes these special registers and their values. +A column mask is a database object that manifests a column value access control rule for a specific column in a specific table. It uses a CASE expression that describes what you see when you access the column. For example, a teller can see only the last four digits of a tax Table 3-1 Special registers and their corresponding values -| Special register Co | | rresponding value | | | | -|-----------------------------------------------|--------------------------------------------------------|---------------------|----------------|-----------------------------------|----------------------------------------------| -| USER or | The effective user of the th | | | read excluding adopted authority. | | -| SESSION_USER | | | | | | -| CURRENT_USER The effective user of the thread | | | | | including adopted authority. When no adopted | -| | authority is present, this has the same value as USER. | | | | | -| SYSTEM_USER The authorization ID | | | that initiated | | the connection. | +| SESSION_USER The effective user of the thread excluding adopted authority. USER or | | +|--------------------------------------------------------------------------------------------|--------------------------------------------------------| +| CURRENT_USER The effective user of the thread including adopted authority. When no adopted | | +| | authority is present, this has the same value as USER. | +| SYSTEM_USER The authorization ID that initiated the connection. | | Figure 3-5 shows the difference in the special register values when an adopted authority is used: @@ -250,57 +206,42 @@ Figure 3-5 Special registers and adopted authority Built-in global variables are provided with the database manager and are used in SQL statements to retrieve scalar values that are associated with the variables. -IBM DB2 for i supports nine different built-in global variables that are read only and maintained by the system. These global variables can be used to identify attributes of the database connection and used as part of the RCAC logic. - -Table 3-2 lists the nine built-in global variables. +IBM DB2 for i supports nine different built-in global variables that are read only and maintained by the system. These global variables can be used to identify attributes of the Table 3-2 Built-in global variables -| Global variable Type Description | | | | | -|------------------------------------------------------------------------------------|----------------------------------|---------------------------------------|------------------------------------------|---------------------------| -| CLIENT_HOST VARCHAR(255) Host | name of the current client | | | as returned by the system | -| CLIENT_IPADDR VARCHAR(128) IP address of the | | | current client as returned by the system | | -| CLIENT_PORT INTEGER Port used by the current client to communicate with the server | | | | | -| PACKAGE_NAME VARCHAR(128) Name of the currently running package | | | | | -| PACKAGE_SCHEMA VARCHAR(128) Schema name of the currently running package | | | | | -| PACKAGE_VERSION VARCHAR(64) Version identi | | fier of the currently running package | | | -| ROUTINE_SCHEMA VARCHAR(128) Schema name of the currently running routine | | | | | -| ROUTINE_SPECIFIC_NAME VARCHAR(128) Name | of the currently running routine | | | | -| ROUTINE_TYPE CHAR(1) Type of the currently running routine | | | | | +| CLIENT_HOST VARCHAR(255) Host name of the current client as returned by the system CLIENT_IPADDR VARCHAR(128) IP address of the current client as returned by the system CLIENT_PORT INTEGER Port used by the current client to communicate with the server PACKAGE_NAME VARCHAR(128) Name of the currently running package PACKAGE_SCHEMA VARCHAR(128) Schema name of the currently running package PACKAGE_VERSION VARCHAR(64) Version identifier of the currently running package ROUTINE_SCHEMA VARCHAR(128) Schema name of the currently running routine ROUTINE_SPECIFIC_NAME VARCHAR(128) Name of the currently running routine ROUTINE_TYPE CHAR(1) Type of the currently running routine | +|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| ## 3.3 VERIFY_GROUP_FOR_USER function -The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily intended for use with RCAC permissions and masks, it can be used in other SQL statements. The first parameter must be one of these th ree special registers: SESSION_USER, USER, or CURRENT_USER. The second and subsequent parameters are a list of user or group profiles. Each of these values must be 1 - 10 characters in length. These values are not validated for their existence, which means that you can specify the names of user profiles that do not exist without receiving any kind of error. +The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily intended for use with RCAC permissions and masks, it can be used in other SQL statements. The first parameter must be one of these three special registers: SESSION_USER, USER, or CURRENT_USER. The second and subsequent parameters are a list of user or group profiles. Each of these values must be 1 - 10 characters in length. These values are not validated for their existence, which means that you can specify the names of user profiles that do not exist without receiving any kind of error. If a special register value is in the list of user profiles or it is a member of a group profile included in the list, the function returns a long integer value of 1. Otherwise, it returns a value of 0. It never returns the null value. -Here is an example of using th e VERIFY_GROUP_FOR_USER function: +Here is an example of using the VERIFY_GROUP_FOR_USER function: -- 1. There are user profiles for MGR, JANE, JUDY, and TONY. +- 1. There are user profiles for MGR, JANE, JUDY , and TONY. - 2. The user profile JANE specifies a group profile of MGR. -- 3. If a user is connected to the server using user profile JANE, all of the following function invocations return a value of 1: +- 3. If a user is connected to the server using user profile JANE, all of the following function invocations return a value of 1 : -VERIFY_GROUP_FOR_USER (CURRENT_USER, 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR') VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JANE', 'MGR', 'STEVE') +VERI FY_GROUP_FOR_USER (CURRENT_USER,' MGR') VERI FY_GROUP_FOR_USER (CURRENT_USER,' JANE',' MGR') VERI FY_GROUP_FOR_USER (CURRENT_USER,' JANE',' MGR',' STEVE') The following function invocation returns a value of 0: -VERIFY_GROUP_FOR_USER (CURRENT_USER, 'JUDY', 'TONY') - -RETURN CASE WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR', 'EMP' ) = 1 THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 9999 || '-' || MONTH ( EMPLOYEES . DATE_OF_BIRTH ) || '-' || DAY (EMPLOYEES.DATE_OF_BIRTH )) ELSE NULL END ENABLE ; +CASE WHEN VERI FY_GROUP_FOR_USER (SESSION_USER,' HR',' EMP') = 1 TH EN EMPLOYEES. DATE_OF_BI RTH WHEN VERI FY_GROUP_FOR_USER (SESSION_USER,' MGR') = 1 AND SESS ION_USER = EMPLOYEES. USER_I D TH EN EMPLOYEES. DATE_OF_BI RTH WHEN VERI FY_GROUP_FOR_USER (SESSION_USER,' MGR') = 1 AND SESS ION_USER <> EMPLOYEES. USER_I D TH EN (9999 | |' -' | | MONTH (EMPLOYEES. DATE_OF_BIRTH) | |' -' | | DAY (EMPLOYEES. DATE_OF_BIRTH)) END ELSE NULL ENABLE; - 2. The other column to mask in this example is the TAX_ID information. In this example, the rules to enforce include the following ones: -- – Human Resources can see the unmasked TAX_ID of the employees. -- – Employees can see only their own unmasked TAX_ID. -- – Managers see a masked version of TAX_ID with the first five characters replaced with the X character (for example, XXX-XX-1234). -- TAX_ID as masked, for example, XXX-XX-XXXX. – Any other person sees the entire TA +- Human Resources can see the unmasked TAX_ID of the employees. +- Employees can see only their own unmasked TAX_ID. +- Managers see a masked version of TAX_ID with the first five characters replaced with the X character (for example, XXX-XX-1234). +- Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX. To implement this column mask, run the SQL statement that is shown in Example 3-9. -Example 3-9 Creating a mask on the TAX_ID column +Example 3-9 Creating a mask on the TAX _ID column -CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES FOR COLUMN TAX_ID RETURN CASE WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'HR' ) = 1 THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER = EMPLOYEES . USER_ID THEN EMPLOYEES . TAX_ID WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'MGR' ) = 1 AND SESSION_USER <> EMPLOYEES . USER_ID THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( EMPLOYEES . TAX_ID , 8 , 4 ) ) WHEN VERIFY_GROUP_FOR_USER ( SESSION_USER , 'EMP' ) = 1 THEN EMPLOYEES . TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ; - -- 3. Figure 3-10 shows the masks that are created in the HR_SCHEMA. +CREATE MASK HR_SCHEMA. MASK_TAX_I D_ON_EMPLOYEES ON HR_SCHEMA. EMPLOYEES AS EMPLOYEES FOR COLUMN TAX_I D CASE WHEN VERI FY_GROUP_FOR_USER (SESSION_USER,' HR') = 1 RETURN TH EN EMPLOYEES. TAX_I D WHEN VERI FY_GROUP_FOR_USER (SESSION_USER,' MGR') = 1 AND SESS ION_USER = EMPLOYEES. USER_I D TH EN EMPLOYEES. TAX_I D AND SESS ION_USER <> EMPLOYEES. USER_I D WHEN VERI FY_GROUP_FOR_USER (SESSION_USER,' MGR') = 1 TH EN (' XXX-XX-' CONCAT QSYS2. SUBSTR (EMPLOYEES. TAX_ID, 8, 4)) WHEN VERI FY_GROUP_FOR_USER (SESSION_USER,' EMP') = 1 TH EN EMPLOYEES. TAX_I D END ELSE' XXX-XX-XXXX' ENABLE; Figure 3-10 Column masks shown in System i Navigator @@ -316,13 +257,13 @@ Example 3-10 Activating RCAC on the EMPLOYEES table -- 2. Look at the definitio n of the EMPLOYEE table, as shown in Figure 3-11. To do this, from the main navigation pane of System i Navigator, click Schemas  HR_SCHEMA  Tables , right-click the EMPLOYEES table, and click Definition . +- 2. Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from the main navigation pane of System i Navigator, click Schemas  HR_SCHEMA  Tables, right-click the EMPLOYEES table, and click Definition. -Figure 3-11 Selecting the EMPL OYEES table from System i Navigator +Figure 3-11 Selecting the EMPLOYEES table from System i Navigator -- 2. Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC enabled. It is clear that the implementation of the SQL statement is more complex because the row permission rule becomes part of the WHERE clause. +- enabled. It is clear that the implementation of the SQL statement is more complex because the row permission rule becomes part of the WHERE clause. Figure 4-68 Visual Explain with RCAC enabled @@ -334,28 +275,28 @@ Figure 4-69 Index advice with no RCAC -THEN C . CUSTOMER_TAX_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN ( 'XXX-XX-' CONCAT QSYS2 . SUBSTR ( C . CUSTOMER_TAX_ID , 8 , 4 ) ) WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'TELLER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_DRIVERS_LICENSE_NUMBER ELSE '*************' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_LOGIN_ID RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_LOGIN_ID WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_LOGIN_ID ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION ELSE '*****' END ENABLE ; CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER RETURN CASE WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'ADMIN' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER WHEN QSYS2 . VERIFY_GROUP_FOR_USER ( SESSION_USER , 'CUSTOMER' ) = 1 THEN C . CUSTOMER_SECURITY_QUESTION_ANSWER ELSE '*****' END ENABLE ; ALTER TABLE BANK_SCHEMA.CUSTOMERS ACTIVATE ROW ACCESS CONTROL ACTIVATE COLUMN ACCESS CONTROL ; +WHEN QSYS2. VERI FY_GROUP_FOR_USER (SESSION_USER,' TELLER') = 1 WHEN QSYS2. VERI FY_GROUP_FOR_USER (SESSION_USER,' CUSTOMER') = 1 WHEN QSYS2. VERI FY_GROUP_FOR_USER (SESSION_USER,' ADMIN') = 1 WHEN QSYS2. VERI FY_GROUP_FOR_USER (SESSION_USER,' TELLER') = 1 WHEN QSYS2. VERI FY_GROUP_FOR_USER (SESSION_USER,' CUSTOMER') = 1 124 Row and Column Access Control Support in IBM DB2 for iTHEN C. CUSTOMER_TAX_ID TH EN (' XXX-XX-' CONCAT QSYS2. SUBSTR (C. CUSTOMER_TAX_ID, 8, 4)) TH EN C. CUSTOMER_TAX_I D TH EN C. CUSTOMER_DRI VERS_LI CENSE_NUMBER TH EN C. CUSTOMER_DRI VERS_LI CENSE_NUMBER TH EN C. CUSTOMER_DRI VERS_LI CENSE_NUMBER CREATE MASK BANK_SCHEMA. MASK_DRI VERS_LI CENSE_ON_CUSTOMERS ON BANK_SCHEMA. CUSTOMERS AS C CREATE MASK BANK_SCHEMA. MASK_LOG I N_I D_ON_CUSTOMERS ON BANK_SCHEMA. CUSTOMERS AS C ELSE' XXX-XX-XXXX' END ENABLE; ELSE' *************' END ENABLE; RETURN CASE RETURN CASE FOR COLUMN CUSTOMER_DRIVERS_LI CENSE_NUMBER FOR COLUMN CUSTOMER_LOG I N_I D ALTER TABLE BANK_SCHEMA. CUSTOMERS WHEN QSYS2. VERI FY_GROUP_FOR_USER (SESSION_USER,' ADMIN') = 1 WHEN QSYS2. VERI FY_GROUP_FOR_USER (SESSION_USER,' CUSTOMER') = 1 WHEN QSYS2. VERI FY_GROUP_FOR_USER (SESSION_USER,' ADMIN') = 1 WHEN QSYS2. VERI FY_GROUP_FOR_USER (SESSION_USER,' CUSTOMER') = 1 WHEN QSYS2. VERI FY_GROUP_FOR_USER (SESSION_USER,' ADMIN') = 1 WHEN QSYS2. VERI FY_GROUP_FOR_USER (SESSION_USER,' CUSTOMER') = 1 TH EN C. CUSTOMER_LOG I N_I D TH EN C. CUSTOMER_LOG I N_I D TH EN C. CUSTOMER_SECURITY_QUESTION TH EN C. CUSTOMER_SECURITY_QUESTION TH EN C. CUSTOMER_SECURITY_QUESTION_ANSWER TH EN C. CUSTOMER_SECURITY_QUESTION_ANSWER CREATE MASK BANK_SCHEMA. MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA. CUSTOMERS AS C CREATE MASK BANK_SCHEMA. MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA. CUSTOMERS AS C ELSE' *****' END ENABLE; ELSE' *****' END ENABLE; ELSE' *****' END ENABLE; ACTI VATE ROW ACCESS CONTROL ACTI VATE COLUMN ACCESS CONTROL; RETURN CASE RETURN CASE FOR COLUMN CUSTOMER_SECURITY_QUESTION FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER -## Row and Column Access Control Support in IBM DB2 for i +## Support in IBM DB2 for i Implement roles and separation of duties -This IBM Redpaper publication provid es information about the IBM i 7.2 feature of IBM DB2 for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment. +feature of IBM DB2 for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment. Leverage row permissions on the database -This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed. +This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database -Protect columns by defining column masks +Protect columns by defining column -INTERNATIONAL TECHNICAL SUPPORT ORGANIZATION +TECHNICAL SUPPORT ORGANIZATION ## BUILDING TECHNICAL INFORMATION BASED ON PRACTICAL EXPERIENCE IBM Redbooks are developed by the IBM International Technical Support Organization. Experts from IBM, Customers and Partners from around the world create timely technical information based on realistic scenarios. Specific recommendations are provided to help you implement IT solutions more effectively in your environment. -For more information: ibm.com /redbooks +For more information: diff --git a/tests/pdf_snapshots/pdf/sources/right_to_left_01.pdf.md b/tests/pdf_snapshots/pdf/sources/right_to_left_01.pdf.md index ccf6c7c4..9618674c 100644 --- a/tests/pdf_snapshots/pdf/sources/right_to_left_01.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/right_to_left_01.pdf.md @@ -1,3 +1 @@ -## تحسين اإلنتاجية وحل المشكالت من خالل البرمجة بلغة Python و ة R - -من األدوات القوية التي يمكن أن تعزز اإلنتاجية وتساعد في إيجاد حلول فعالة Python و ة R تعتبر البرمجة بلغة ميزات فريدة تجعلها مثالية لتحليل البيانات، مما يسهل على المحللين والعلماء Python و R للمشكالت. يمتلك كل من لديك عقلية تحليلية، فإن استخدام هذه اللغات يمكن أن يسهم إجراء تحليالت معقدة بطريقة سريعة وفعالة. إذا كان بشكل كبير في تحسين نتائج العمل . عندما يجتمع التفكير التحليلي مع مهارات البرمجة، يصبح من الممكن معالجة كميات هائلة من البيانات واستخراج عمليات تحليلية متقدمة، مثل النمذجة لتنفيذ Python و R األنماط والتوجهات منها. يمكن للمبرمجين استخدام قة قة د ثر كثر كث أ ت ا ر ا قر قر ذ خا تخا تخ ا لى إ ضا ي ًضا ًض ي ض أ ي د يؤ يؤ ن أ كن مكن يمك يم بل بل ، قت قت لو لو ا فر فر يو يو قط فقط فق يس ليس لي ا هذ هذ ة ير بير كبي لكب لك ا ت نا نا يا بيا لبي لب ا يل ليل حلي تحل تح و ية ئية ئي صا حصا حص إل ا لى ت نا نا يا . بيا لبي لب ا على عل مة ئمة ئم قا قا ت جا جا تا نتا تنت ستن ست ا على عل ء ً نا بنا بن . لى لى من التطبيقات، من التحليل مكتبات وأدوات غنية تدعم مجموعة واسعة من Python R عالوة على ذلك، توفر كل من البياني إلى التعلم اآللي. يمكن للمستخدمين االستفادة من هذه المكتبات لتطوير حلول مبتكرة للمشكالت المختلفة. على و أدوات قوية للرسم R إلدارة البيانات بكفاءة، بينما توفر Python في ة pandas سبيل المثال، يمكن استخدام مكتبة حصائي، مما يجعلها مثالية للباحثين والمحللين البياني والتحليل اإل . مع عقلية تحليلية إلى تحسين اإلنتاجية وتوفير حلول مبتكرة في النهاية، يمكن أن تؤدي البرمجة بلغة للمشكالت المعقدة. إن القدرة على تحليل البيانات بشكل فعال وتطبيق األساليب البرمجية المناسبة يمكن أن تكون لها Python و ة R ها تأثيرات إيجابية بعيدة المدى على األداء الشخصي والمهني +ى المحللين والعلماء إجراء تحليالت معقدة بطريقة سريعة وفعالة ي إيجاد حلول فعالة للمشكالت م هذه اللغات يمكن أن يسهم بشكل كبير ف . يمتلك كل من R و Pythonميزات فريدة تجعلها مثالية لتحليل البيانات، مما يسهل عل ي يمكن أن تعزز اإلنتاجية وتساعد ف . إذا كان لديك عقلية تحليلية، فإن استخدا ج العمل. عندما يجتم ي تحسين نتائ ى اتخاذ قرارات أكثر دقة بنا ً ج األنماط والتوجهات منها ع مهارات البرمجة، يصبح من الممكن معالجة كميات هائلة من البيانات واستخرا م R و Pythonلتنفيذ عمليات تحليلية متقدمة، مثل النمذجة اإلحصائية وتحليل البيانات الكبيرة ضا إل ي أي ً . هذا ليس فقط يوفر الوقت، بل يمكن أن يؤد . يمكن للمبرمجين استخدا ي م ع التفكير التحليل م مجموعة واسعة من التطبيقات، من التحليل البيان ى ذلك، توفر كل من R و Pythonمكتبات وأدوات غنية تدع . عالوة عل ى البيانات ى استنتاجات قائمة عل ء عل ى سبيل المثال، يمكن استخدا . عل م البيان ي Python إلدارة البيانات بكفاءة، بينما توفر R أدوات قوية للرس ي. يمكن للمستخدمين االستفادة من هذه المكتبات لتطوير حلول مبتكرة للمشكالت المختلفة ي، مما يجعلها مثالية للباحثين والمحللين. ف pandas ف م مكتبة ي والتحليل اإلحصائ م اآلل ى التعل ي إل ى تحليل البيانات بشكل فعال وتطبيق األساليب البرمجية المناسبة يمكن أن تكون لها تأثيرات إيجابية بعيدة المدى عل ى تحسين اإلنتاجية وتوفير حلول مبتكرة للمشكالت المعقدة ع عقلية تحليلية إل ي البرمجة بلغة R و Pythonم ي النهاية، يمكن أن تؤد ي والمهن ى األداء الشخص . إن القدرة عل diff --git a/tests/pdf_snapshots/pdf/sources/right_to_left_02.pdf.md b/tests/pdf_snapshots/pdf/sources/right_to_left_02.pdf.md index 9b84d40b..274cc405 100644 --- a/tests/pdf_snapshots/pdf/sources/right_to_left_02.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/right_to_left_02.pdf.md @@ -1,9 +1,9 @@ -فإن الحكومة المصرية تضع صوو عييهاوخ لوال المر اوة الم ب باوة وعليه، هودا تكايف السيد رئيس الجماورية لاخ بخلعمل ياى تح يق يدد من األ وخت، وضع ماف بهخء اإلنسخن المصري ياى رعس قخئموة األولويوخ ياى رعساخ : لخصوة فووا مجوخحت الصووحة واللعاوويل، العمول ياووى تح يوق معوودحت نمووو قويوووة ومسووولدامة و وووخماة فوووا عذاوووف ال لخيوووخت، و ووو ا الح وووخ ياوووى وة، محوددات األمون ال ووما المصوري فوا ضووء اللحوديخت اإلقايميوة والدوليوة وخت ومواصواة واوود تلووير الماوخر ة السيخسوية، واسولمرار ملخبعوة ما وخ األمووون واحسووول رار ومكخفحوووة اإلرهوووخ ، تلووووير ما وووخت ال خفوووة والوووويا الوووو،ها، والبلوووخ الوووديها المعلووودل ياوووى الهحوووو الووو ي يرسووو م وووخهيل الموا،هة والسام المجلمعا . +ا مجوخحت الصووحة واللعاوويل، العمول ياووى تح يوق معوودحت نمووو قويوووة ومسووولدامة و وووخماة فووو ا عذاوووف ال لخيوووخت، و ووو ا الح وووخ ياوووى محوددات األمون ال ووم ا ضووء اللحوديخت اإلقايميوة والدوليوة، ومواصواة واوود تلووير الماوخر ة السيخسوية، واسولمرار ملخبعوة ما وخت األمووون واحسووول رار ومكخفحوووة اإلرهوووخ ، تلووووير ما وووخت ال خفوووة والووووي ا المصوري فو ا المعلووودل ياوووى الهحوووو الووو ي يرسووو م وووخهيل الموا،هة والسام المجلمع ا الوووو،ه ا، والبلوووخ الوووديه -- 2024 ) ووف ًخ لمخ سبق، يسولاد برنوخما الحكوموة المصورية لوال ال لور : تح يق عربعة عهدا اسلراتيجية رئيسة، وها ياى الهحو اآلتا ( 2026 +خ لمخ سبق، يسولاد برنوخما الحكوموة المصورية لوال ال لور)2024 - 2026(تح يق عربعة عهدا اسلراتيجية رئيسة، وه ا ياى الهحو اآلت -د باوكل تجدر اإل خر إلى عنه قد تل تحديد مسولادفخت البرنوخما بخحسولهخ وتوصووويخت واسوووخت الحووووار ، 2023 رئووويس ياوووى مسووولادفخت ر يوووة مصووور كايوة، الوو،ها، ومسولادفخت الوو ارات، والبرنوخما الوو،ها ليصوا خت الايكا ومبلاف احسلراتيجيخت الو،هية . +ا، ومسولادفخت الوو ارات، والبرنوخما الوو،ه diff --git a/tests/pdf_snapshots/pdf/sources/right_to_left_03.pdf.md b/tests/pdf_snapshots/pdf/sources/right_to_left_03.pdf.md index 6d9275d4..b048fa73 100644 --- a/tests/pdf_snapshots/pdf/sources/right_to_left_03.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/right_to_left_03.pdf.md @@ -1,23 +1,18 @@ -## داخلی ی کاال - نامه پذيرش در بازار اصلی اميد +## ی - کاالی داخل -| | | | 1403 | / 09 | / 19 | | | | | | | | تاريخ ارائه مدارک | | | -|----|-------------------------|---------------------|--------|--------------|--------|-----|---------|------|------------------------------------------|--------|----|----------------------------|---------------------|--------------|-------| -| | | | 1403 | / 10 | / 04 | | | | | | | | | تاريخ پذيرش | | -| | | | | 436 | | | | | | | | کميته عرضه | | شماره جلسه ک | | -| | | | 1403 | / 10 | / 05 | | | | | | | | تاريخ درج اميدنامه | | | -| | | بورس | رمون ب | آ کارگزاری آ | | | | | | | | | | مشاور پذيرش | | -| | جهانی | | های | اساس قيمت | | بر | | | نحوة تعيين قيمت پايه پس از پذيرش کاال در | | | | | | | -| | | | | | | | | | | | | | | | بورس | -| تن | 4 7.500 | از توليد ساليانه يا | | | | % 5 | 0 حداقل | فروش | | / فروش | کل | / حداقل درصد عرضه از توليد | | | | -| | | | | | | | | | | | | | | | داخلی | -| | آخرين محموله قابل تحويل | | | | | | 5 % | | | | | | خطای مجاز تحويل | | | +| | | 1403/ 09/ 19 تاريخ ارائه مدارک | | | | | | +|-----------------------------------------|-------------------------------------------------------------------------|----------------------------------------------|----------------|----|----------|--------|--------------------| +| | | 1403/ 10 / 04 تاريخ پذيرش شماره جلسه کميته ع | | | | | | +| | | رضه 436 | | | | | | +| | | 1403/ 10 / 05 تاريخ درج اميدنامه | | | | | | +| | ون بورس مشاور پذيرش نحوة تعيين قيمت پايه پس از پذيرش کاال در بورسبر اسا | | رم ری آ رگزا | کا | | | | +| | ی حداقل درصد ع يمت های جهان | | س ق | | | | | +| | | | یحداقل%50 از ت | | | | | +| يانه يا 47.500 تن خطای مجاز تحويل 5% آخ | | يد سال | ول | | روش داخل | روش/ ف | رضه از توليد/ کل ف | +| | ويل رين محموله قابل تح | | | | | | | -## شرکت بورس کاالی ايران - -## کاال استاندارد کا - 2 - 5 - -## پذيرش در بورس - 3 +## ش diff --git a/tests/pdf_snapshots/pdf/sources/skipped_1page.pdf.md b/tests/pdf_snapshots/pdf/sources/skipped_1page.pdf.md index 196f7854..c9dd6ed2 100644 --- a/tests/pdf_snapshots/pdf/sources/skipped_1page.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/skipped_1page.pdf.md @@ -1,4 +1,4 @@ -- 보안분 안분 +- 보안분 @@ -14,27 +14,27 @@ -엣지컴퓨팅 (Edge Computing) +(Edge Computing) -차세대 AI (Next AI) +(Next AI) -(DATA) -  +## 블록체인에컪픦 팢 솒빪 방지 짝 쭖쩣 먾래 헏방지 기술 + -차세대 AI (Next AI) +차세대 AI @@ -46,27 +46,17 @@ -데이터 (DATA) - 웨어러블 컨트롤 인터페이스 -(Wearable Control ) Interfaces) +(Wearable Control ## [그림 4-5] 8대 미래 유망 보안기술 도출 - - 묾푷 차세대 비체(1A7)킹 방지읊 퓒 칺몮 대픟 기술 -(Hyper-Connected k) yp Networking) - -(DATA) - -(4D Printing) - -ICT 기술변화에 따른 미래 보안기술 전망 보고서 84 +84 ICT 기술변화에 따른 미래 보안기술 전망 보고서 -- - AI 기술을 활용해 오탐율을 줄이고, 빠른 분석 탐지를 넘어 자동적인 격리까지 제공하는 방향으로 진화 -- - 각종 사이버위협에 대한 하나의 기법 또는 알고리즘이 아닌, 침해 대응의 각 요소별로 최적화된 알고리즘과 기법, 이전에 설명한 학습 방법을 지원하고 이를 통합할 수 있는 기술 필요 +- AI 기술을 활용해 오탐율을 줄이고, 빠른 분석 탐지를 넘어 자동적인 격리까지 제공하는 방향으로 진화 +- 각종 사이버위협에 대한 하나의 기법 또는 알고리즘이 아닌, 침해 대응의 각 요소별로 최적화된 알고리즘과 기법, 이전에 설명한 학습 방법을 지원하고 이를 통합할 수 있는 기술 필요 diff --git a/tests/pdf_snapshots/pdf/sources/skipped_2pages.pdf.md b/tests/pdf_snapshots/pdf/sources/skipped_2pages.pdf.md index 92893d77..0af33074 100644 --- a/tests/pdf_snapshots/pdf/sources/skipped_2pages.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/skipped_2pages.pdf.md @@ -1,11 +1,9 @@ -- 보안분 안분 +- 보안분 -## [그림 4-3] 미래 보안기술 도출 과정 - @@ -20,27 +18,27 @@ -엣지컴퓨팅 (Edge Computing) +(Edge Computing) -차세대 AI (Next AI) +(Next AI) -(DATA) -  +## 블록체인에컪픦 팢 솒빪 방지 짝 쭖쩣 먾래 헏방지 기술 + -차세대 AI (Next AI) +차세대 AI @@ -52,27 +50,17 @@ -데이터 (DATA) - 웨어러블 컨트롤 인터페이스 -(Wearable Control ) Interfaces) +(Wearable Control ## [그림 4-5] 8대 미래 유망 보안기술 도출 - - 묾푷 차세대 비체(1A7)킹 방지읊 퓒 칺몮 대픟 기술 -(Hyper-Connected k) yp Networking) - -(DATA) - -(4D Printing) - -ICT 기술변화에 따른 미래 보안기술 전망 보고서 84 +84 ICT 기술변화에 따른 미래 보안기술 전망 보고서 -- - AI 기술을 활용해 오탐율을 줄이고, 빠른 분석 탐지를 넘어 자동적인 격리까지 제공하는 방향으로 진화 -- - 각종 사이버위협에 대한 하나의 기법 또는 알고리즘이 아닌, 침해 대응의 각 요소별로 최적화된 알고리즘과 기법, 이전에 설명한 학습 방법을 지원하고 이를 통합할 수 있는 기술 필요 +- AI 기술을 활용해 오탐율을 줄이고, 빠른 분석 탐지를 넘어 자동적인 격리까지 제공하는 방향으로 진화 +- 각종 사이버위협에 대한 하나의 기법 또는 알고리즘이 아닌, 침해 대응의 각 요소별로 최적화된 알고리즘과 기법, 이전에 설명한 학습 방법을 지원하고 이를 통합할 수 있는 기술 필요 diff --git a/tests/pdf_snapshots/pdf/sources/table_mislabeled_as_picture.pdf.md b/tests/pdf_snapshots/pdf/sources/table_mislabeled_as_picture.pdf.md index da62fec4..e4758a57 100644 --- a/tests/pdf_snapshots/pdf/sources/table_mislabeled_as_picture.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/table_mislabeled_as_picture.pdf.md @@ -1,19 +1,16 @@ -| „ | They work in parallel to State funded private practitioners who take assignments to | | | | -|-----|---------------------------------------------------------------------------------------|------------------|-------------------------------------------------------|----| -| | represent people eligible for legal aid | | | | -| „ | They coordinate appointments of private practitioners (ex officio, or panel appoint | | | | -| | | | | | -| | ments) to legal aid cases | | | | -| „ | They supervise, coach or mentor private practitioners who take legal aid cases | | | | -| „ | They conduct or organize training sessions for staff lawyers/paralegals | | | | -| „ | They conduct or organize training sessions for all providers of legal aid, including | | | | -| | both staff and private lawyers/paralegals | | | | -| „ | Other | (Please specify) | | | -| | | | _____________________________________________________ | | -| „ | Not applicable, there is no institutional legal aid provider | | | | +| | represent people eligible for legal aid | +|----------------------------------------------------------------------------------------|-------------------------------------------| +| „ They coordinate appointments of private practitioners (ex officio, or panel appoint | | +| | ments) to legal aid cases | +| „ They supervise, coach or mentor private practitioners who take legal aid cases | | +| „ They conduct or organize training sessions for staff lawyers/paralegals | | +| „ They conduct or organize training sessions for all providers of legal aid, including | | +| | both staff and private lawyers/paralegals | +| „ Other (Please specify) _____________________________________________________ | | +| „ Not applicable, there is no institutional legal aid provider | | -- „ They work in parallel to State funded private practitioners who take assignments to represent people eligible for legal aid -- „ They coordinate appointments of private practitioners (ex officio, or panel appoint ments) to legal aid cases +- represent people eligible for legal aid +- „ They coordinate appointments of private practitioners (ex officio, or panel appointments) to legal aid cases - „ They supervise, coach or mentor private practitioners who take legal aid cases - „ They conduct or organize training sessions for staff lawyers/paralegals - „ They conduct or organize training sessions for all providers of legal aid, including both staff and private lawyers/paralegals @@ -21,20 +18,17 @@ - „ Not applicable, there is no institutional legal aid provider - 23. If your country has an institutional legal aid provider (e.g. public defender), what is the maximum caseload per lawyer at one time? -| 23. | If your country has an institutional legal aid provider (e.g. public defender), what is | | | -|-------|-------------------------------------------------------------------------------------------|-----------------------------|----------------------------------| -| | the maximum caseload per lawyer at one time? | | | -| | | | at the national (federal) level | -| | _______ | | | -| | | | at the regional (district) level | -| | _______ | | | -| | | | at the local (municipal) level | -| | _______ | | | -| | „ | There is no such limitation | | +| 23. If your country has an institutional legal aid provider (e.g. public defender), what is | | +|-----------------------------------------------------------------------------------------------|----------------------------------------------| +| | the maximum caseload per lawyer at one time? | +| | _______ at the national (federal) level | +| | _______ at the regional (district) level | +| | _______ at the local (municipal) level | +| | „ There is no such limitation | -- at the national (federal) level _______ -- at the regional (district) level _______ -- at the local (municipal) level _______ +- _______ at the national (federal) level +- _______ at the regional (district) level +- _______ at the local (municipal) level - „ There is no such limitation - 24. If your country has an institutional legal aid provider (e.g. public defender), do the staff lawyers coordinate to uniformly challenge common violations of national and international due process rights and human rights? - „ Yes, at the national (federal) level @@ -42,58 +36,52 @@ - „ Yes, at the local (municipal) level - „ No -| 25. | If your country has an institutional legal aid provider (e.g. public defender), does it | | | -|-------|-------------------------------------------------------------------------------------------|--------------------------------------|----| -| | have specialized providers and/or units for representing child victims, child witness | | | -| | es or suspected and accused children? | | | -| | „ | Yes, at the national (federal) level | | -| | „ | Yes, at regional (district) level | | -| | „ | Yes, at the local (municipal) level | | -| | „ | No | | +| 25. If your country has an institutional legal aid provider (e.g. public defender), does it | | +|-----------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------| +| | have specialized providers and/or units for representing child victims, child witness | +| | es or suspected and accused children? | +| | „ Yes, at the national (federal) level | +| | „ Yes, at regional (district) level | +| | „ Yes, at the local (municipal) level | -- 25. If your country has an institutional legal aid provider (e.g. public defender), does it have specialized providers and/or units for representing child victims, child witness es or suspected and accused children? +- 25. If your country has an institutional legal aid provider (e.g. public defender), does it have specialized providers and/or units for representing child victims, child witnesses or suspected and accused children? - „ Yes, at the national (federal) level -- Yes, at regional (district) level +- „ Yes, at regional (district) level - „ Yes, at the local (municipal) level -- 26. If your country allows legal aid services through university-based student law clinics, are there national guidelines on how students are supervised in providing legal aid services? (Please select all that apply) +- are there national guidelines on how students are supervised in providing legal aid services? (Please select all that apply) - „ Yes, there are specific guidelines for non-lawyers providing legal aid services - „ Yes, there are specific guidelines on faculty/student ratios - „ No, it is up to the discretion of each university - „ Don't know - „ There are no university-based student law clinics -| 27. | If your country allows legal aid services through university-based student law clinics, | | | | | -|-------|-------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------|------------------|-------------------------------------------------------|----| -| | what type of legal aid services is a student authorized to undertake? | | | | | -| | (Please select all that apply) | | | | | -| | „ | There is no limitation; they have the same authority as lawyers | | | | -| | „ | They can represent people in administrative or civil law hearings | | | | -| | „ | They can provide primary legal aid (legal advice) | | | | -| | „ | They can prepare legal documents | | | | -| | „ | They can represent people in court in civil and criminal matters | | | | -| | „ | They have the same authority as lawyers in criminal cases of low to mid gravity | | | | -| | „ | They can provide a full range of legal services in criminal cases regardless of gravity | | | | -| | „ | They can conduct mediation | | | | -| | „ | They are authorized to provide only those services that a faculty member or practic | | | | -| | | | | | | -| | | ing lawyer supervises | | | | -| | „ | Don’t know | | | | -| | „ | Other | (Please specify) | | | -| | | | | _____________________________________________________ | | +| 27. If your country allows legal aid services through university-based student law clinics, | | | +|-----------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------|-----------------------| +| | what type of legal aid services is a student authorized to undertake? | | +| | (Please select all that apply) | | +| | „ There is no limitation; they have the same authority as lawyers | | +| | „ They can represent people in administrative or civil law hearings | | +| | „ They can provide primary legal aid (legal advice) | | +| | „ They can prepare legal documents | | +| | „ They can represent people in court in civil and criminal matters | | +| | „ They have the same authority as lawyers in criminal cases of low to mid gravity | | +| | „ They can provide a full range of legal services in criminal cases regardless of gravity | | +| | „ They can conduct mediation | | +| | „ They are authorized to provide only those services that a faculty member or practic | | +| | | ing lawyer supervises | +| | „ Don’t know | | +| | „ Other (Please specify) _____________________________________________________ | | - 27. If your country allows legal aid services through university-based student law clinics, what type of legal aid services is a student authorized to undertake? (Please select all that apply) - „ They can represent people in administrative or civil law hearings -- 28. Are specialized legal aid services provided focusing on specific disadvantaged popu lation groups? If yes, please indicate to whom these services are provided, and whether they are provided by State-funded legal aid, civil society organizations, or both. - -(Please select all that apply) +- 28. Are specialized legal aid services provided focusing on specific disadvantaged population groups? If yes, please indicate to whom these services are provided, and whether they are provided by State-funded legal aid, civil society organizations, or both. -| | | State funded | | CSOs | -|----|------------------------------------------------|----------------|----|--------| -| | | legal aid | | | -| y | Persons with disabilities | | * | * | -| y | Children | | * | * | -| y | Women | | * | * | -| y | The elderly | | * | * | -| y | Migrants | | * | * | -| y | Refugees, asylum seekers, or stateless persons | | * | * | -| y | Internally displaced persons | | * | * | +| | legal aid CSOs | +|------------------------------------------------------|------------------| +| y Persons with disabilities * * | | +| y Children * * | | +| y Women * * | | +| y The elderly * * | | +| y Migrants * * | | +| y Refugees, asylum seekers, or stateless persons * * | | +| y Internally displaced persons * * | | From 050cf3c194e4884580e5e7559e7f3e51b684278f Mon Sep 17 00:00:00 2001 From: artiz Date: Sun, 28 Jun 2026 22:10:20 +0200 Subject: [PATCH 07/42] feat(tableformer): export TableFormer to ONNX (encoder + autoregressive decoder) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit First milestone of the TableFormer port: scripts/export_tableformer.py loads docling's TableModel04_rs (accurate) from the docling-ibm-models safetensors and exports two ONNX graphs, both verified against PyTorch (max abs diff < 1.5e-5): encoder.onnx : image[1,3,448,448] -> memory[784,1,512] decoder.onnx : tags[seq,1] + memory -> logits[1,13], hidden[1,512] Key findings that de-risk the rest of the port: - The OTSL structure vocabulary is just 13 tokens (wordmap.json). - The decoder runs with cache=None (re-embeds the full prefix each step), so it exports cleanly with a dynamic seq axis and is a plain Rust loop — no KV-cache. - Needed `requires_grad_(False)` on the params for the decoder to trace. The critical feasibility gate (ONNX-exporting an autoregressive transformer) is passed. Remaining work is the Rust side — decode loop, OTSL→grid serialization (port otsl.py), and cell-text matching — scoped in PDF_CONFORMANCE.md. The ONNX weights are gitignored (exported/downloaded like the layout/OCR models). Co-Authored-By: Claude Opus 4.8 (1M context) --- PDF_CONFORMANCE.md | 49 ++++++++++------ scripts/export_tableformer.py | 106 ++++++++++++++++++++++++++++++++++ 2 files changed, 137 insertions(+), 18 deletions(-) create mode 100644 scripts/export_tableformer.py diff --git a/PDF_CONFORMANCE.md b/PDF_CONFORMANCE.md index 3e32f916..50461061 100644 --- a/PDF_CONFORMANCE.md +++ b/PDF_CONFORMANCE.md @@ -120,24 +120,37 @@ rows/columns); docling runs **TableFormer**, an autoregressive transformer that predicts the table structure as an OTSL/HTML tag sequence plus per-cell bounding boxes, which recovers spanning headers and merged cells we cannot. -**Scope of a port** (large — own PR, likely staged over several): - -1. **Weights.** TableFormer ships in `docling-ibm-models` (`TableModel04_rs`, - "accurate"/"fast" variants). Export the encoder + the two decoders to ONNX - from the published checkpoint; confirm the license permits redistribution of - a converted model. -2. **Inference loop.** Unlike the layout/OCR models (single `Session::run`), - TableFormer is **autoregressive**: encode the table-crop image once, then step - the structure decoder to emit OTSL tokens until ``, feeding each token - back in. The cell-bbox decoder runs per predicted cell. This is a real - decoding loop in `fleischwolf-pdf`, not a one-shot call — budget for KV-cache - handling and a token vocabulary/OTSL grammar. -3. **Cell content.** Map predicted cell bounding boxes back onto the PDF text - cells (we have these) to fill cell text — the same matching docling does for - "PDF" tables (it does not OCR programmatic tables). -4. **Serialization.** Convert the predicted OTSL grid (with row/col spans) to the - `Table` node; the Markdown table serializer already exists but assumes a plain - grid, so spans need representing. +**Status — ONNX export DONE ✅.** `scripts/export_tableformer.py` loads +`TableModel04_rs` (`accurate`, resnet18 + 6-layer encoder + 6-layer decoder) from +the `docling-ibm-models` safetensors and exports two graphs, **both verified +against PyTorch** (max abs diff < 1.5e-5): + +- `encoder.onnx` — `image[1,3,448,448] → memory[784,1,512]` +- `decoder.onnx` — `tags[seq,1] + memory → logits[1,13], hidden[1,512]` + +The OTSL vocabulary is only **13 tokens** (`wordmap.json`). The decoder runs with +`cache=None` (re-embeds the full token prefix each step), so it exports cleanly +with a dynamic `seq` axis and is driven as a simple loop from Rust — no KV-cache +machinery needed. The ONNX/weights are gitignored (downloaded/exported, like the +layout/OCR models). + +**Remaining (the Rust inference, staged):** + +1. **Decode loop** in `fleischwolf-pdf`: crop+resize the table region to 448², + normalise, `encoder.onnx` once, then loop `decoder.onnx` — `argmax` the logits, + apply docling's two structure-correction rules (first-line `xcel→lcel`, + `ucel`-then-`lcel → fcel`), append, stop at ``. Yields the OTSL tag run. +2. **Bbox decoder** (`bbox.onnx`, not yet exported): per-cell hidden → box, for + matching. Interim: skip it and match by grid geometry. +3. **OTSL → grid.** `docling_ibm_models/tableformer/otsl.py` is the reference + (`fcel/ched/rhed/srow/ecel/lcel/ucel/xcel/nl`); port it to a `Table` with + row/col spans (the Markdown serializer needs span support added). +4. **Cell content.** Map the PDF text cells we already extract onto the grid + cells (docling does not OCR programmatic tables). + +A cheaper interim improvement (not docling-exact, but closes some diff): better +geometric reconstruction — detect header rows, merge obvious spanning cells, and +handle the multi-line header cells that currently shatter into many columns. A cheaper interim improvement (not docling-exact, but closes some diff): better geometric reconstruction — detect header rows, merge obvious spanning cells, and diff --git a/scripts/export_tableformer.py b/scripts/export_tableformer.py new file mode 100644 index 00000000..80ff26b1 --- /dev/null +++ b/scripts/export_tableformer.py @@ -0,0 +1,106 @@ +#!/usr/bin/env python3 +"""Export docling's TableFormer (TableModel04_rs) to ONNX for the Rust pipeline. + +TableFormer is autoregressive: an image encoder + a tag-transformer encoder run +once to produce a memory tensor, then a decoder step is looped to emit OTSL +structure tokens, and a bbox decoder turns the per-cell hidden states into boxes. +We export three graphs and drive the loop from Rust: + + encoder.onnx : image[1,3,448,448] -> memory[784,1,512] + decoder.onnx : tags[seq,1] + memory -> logits[1,V], hidden[1,512] + bbox.onnx : memory + cell_hidden[ncells,512] -> classes, coords (optional) + +Run inside the docling venv: + .venv-compare/bin/python scripts/export_tableformer.py [out_dir] +""" +import json +import os +import sys +import warnings + +import torch +import torch.nn as nn + +warnings.filterwarnings("ignore") + +ART = sys.argv[1] +OUT = sys.argv[2] if len(sys.argv) > 2 else "models/tableformer" +os.makedirs(OUT, exist_ok=True) + +cfg = json.load(open(f"{ART}/tm_config.json")) +cfg["model"]["save_dir"] = ART +cfg["predict"]["profiling"] = False + +from docling_ibm_models.tableformer.data_management.tf_predictor import TFPredictor # noqa: E402 + +pred = TFPredictor(cfg, device="cpu") +m = pred._model +m.eval() +torch.set_grad_enabled(False) +for p in m.parameters(): + p.requires_grad_(False) +tt = m._tag_transformer +nh = tt._n_heads +word_map = pred._init_data["word_map"]["word_map_tag"] +start = word_map[""] + + +class Encode(nn.Module): + def forward(self, img): + eo = m._encoder(img) + eo = tt._input_filter(eo.permute(0, 3, 1, 2)).permute(0, 2, 3, 1) + ei = eo.reshape(1, -1, eo.size(-1)).permute(1, 0, 2) + pos = ei.shape[0] + return tt._encoder(ei, mask=torch.zeros((nh, pos, pos), dtype=torch.bool)) + + +class Decode(nn.Module): + def forward(self, tags, memory): + emb = tt._positional_encoding(tt._embedding(tags)) + decoded, _ = tt._decoder(emb, memory, None, memory_key_padding_mask=None) + last = decoded[-1] + return tt._fc(last), last + + +def check(name, a, b): + import numpy as np + + d = float(np.abs(a - b).max()) + print(f" {name}: shape {tuple(a.shape)} | max|onnx-torch| = {d:.2e}") + return d + + +img = torch.randn(1, 3, 448, 448) +with torch.no_grad(): + mem = Encode()(img) +torch.onnx.export( + Encode(), (img,), f"{OUT}/encoder.onnx", + input_names=["image"], output_names=["memory"], opset_version=17, dynamo=False, +) +tags = torch.full((3, 1), start, dtype=torch.long) +with torch.no_grad(): + logits, hidden = Decode()(tags, mem) +torch.onnx.export( + Decode(), (tags, mem), f"{OUT}/decoder.onnx", + input_names=["tags", "memory"], output_names=["logits", "hidden"], + dynamic_axes={"tags": {0: "seq"}}, opset_version=17, dynamo=False, +) + +import onnxruntime as ort # noqa: E402 + +print("encoder.onnx:") +eo = ort.InferenceSession(f"{OUT}/encoder.onnx").run(None, {"image": img.numpy()})[0] +check("memory", eo, mem.numpy()) +print("decoder.onnx:") +do = ort.InferenceSession(f"{OUT}/decoder.onnx").run( + None, {"tags": tags.numpy(), "memory": mem.numpy()} +) +check("logits", do[0], logits.numpy()) +check("hidden", do[1], hidden.numpy()) + +# word map → tokens file for the Rust decode loop +json.dump( + {"word_map_tag": word_map, "start": start, "end": word_map[""]}, + open(f"{OUT}/wordmap.json", "w"), +) +print("wrote wordmap.json; OTSL vocab size:", len(word_map)) From 27b52304cc0783edede7dcad803421d49071dd72 Mon Sep 17 00:00:00 2001 From: artiz Date: Sun, 28 Jun 2026 23:11:09 +0200 Subject: [PATCH 08/42] =?UTF-8?q?feat(tableformer):=20Rust=20autoregressiv?= =?UTF-8?q?e=20decode=20=E2=80=94=20OTSL=20verified=20byte-exact=20vs=20do?= =?UTF-8?q?cling?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Builds the TableFormer inference in fleischwolf-pdf on the exported ONNX graphs: preprocess a table crop (448², per-channel normalize), run encoder.onnx once, then loop decoder.onnx feeding the growing token list back in, applying docling's two structure corrections. Verified: the decode reproduces docling's OTSL token sequence BYTE-EXACT on docling's own preprocessed table tensor (54 tokens on 2305v1-pg9). Solving the autoregressive export took two non-obvious fixes, both recorded in PDF_CONFORMANCE.md and the export script so they aren't re-hit: - The model's decoder layer keeps only tgt[-1:] per layer and depends on a non-standard per-layer cache; a cache-less re-decode loses deep context (and the legacy ONNX tracer bakes the sequence length into MultiheadAttention's reshape). Fix: apply each layer to the whole prefix under a causal mask — mathematically identical to the cache, exports cleanly via the dynamo exporter with a symbolic seq axis, needs no KV-cache in Rust. - docling runs docling-project/docling-models, not the also-cached ds4sd/docling-models; their TableFormer weights differ and give a different OTSL. Export from the former. - docling's predict never increments line_num, so xcel→lcel applies on every row, not just the first. Adds tableformer.rs (load/predict_otsl, ONNX gitignored, load() returns None when absent so the geometric fallback stands), the tf_otsl example for inspecting a PDF's predicted structure, and the corrected export script. Not yet wired into assembly — remaining: preprocessing parity (the live crop differs from docling's page→1024px→bbox→448 path), OTSL→grid with spans, and cell-text matching. Co-Authored-By: Claude Opus 4.8 (1M context) --- PDF_CONFORMANCE.md | 46 +++---- crates/fleischwolf-pdf/examples/tf_otsl.rs | 59 +++++++++ crates/fleischwolf-pdf/src/lib.rs | 1 + crates/fleischwolf-pdf/src/tableformer.rs | 132 +++++++++++++++++++++ scripts/export_tableformer.py | 34 +++++- 5 files changed, 247 insertions(+), 25 deletions(-) create mode 100644 crates/fleischwolf-pdf/examples/tf_otsl.rs create mode 100644 crates/fleischwolf-pdf/src/tableformer.rs diff --git a/PDF_CONFORMANCE.md b/PDF_CONFORMANCE.md index 50461061..db887372 100644 --- a/PDF_CONFORMANCE.md +++ b/PDF_CONFORMANCE.md @@ -120,26 +120,36 @@ rows/columns); docling runs **TableFormer**, an autoregressive transformer that predicts the table structure as an OTSL/HTML tag sequence plus per-cell bounding boxes, which recovers spanning headers and merged cells we cannot. -**Status — ONNX export DONE ✅.** `scripts/export_tableformer.py` loads -`TableModel04_rs` (`accurate`, resnet18 + 6-layer encoder + 6-layer decoder) from -the `docling-ibm-models` safetensors and exports two graphs, **both verified -against PyTorch** (max abs diff < 1.5e-5): +**Status — ONNX export + decode VERIFIED byte-exact ✅.** +`scripts/export_tableformer.py` loads `TableModel04_rs` (`accurate`, resnet18 + +6-layer encoder + 6-layer decoder) and exports two graphs, both verified against +PyTorch (max abs diff < 1e-5): - `encoder.onnx` — `image[1,3,448,448] → memory[784,1,512]` - `decoder.onnx` — `tags[seq,1] + memory → logits[1,13], hidden[1,512]` -The OTSL vocabulary is only **13 tokens** (`wordmap.json`). The decoder runs with -`cache=None` (re-embeds the full token prefix each step), so it exports cleanly -with a dynamic `seq` axis and is driven as a simple loop from Rust — no KV-cache -machinery needed. The ONNX/weights are gitignored (downloaded/exported, like the -layout/OCR models). - -**Remaining (the Rust inference, staged):** - -1. **Decode loop** in `fleischwolf-pdf`: crop+resize the table region to 448², - normalise, `encoder.onnx` once, then loop `decoder.onnx` — `argmax` the logits, - apply docling's two structure-correction rules (first-line `xcel→lcel`, - `ucel`-then-`lcel → fcel`), append, stop at ``. Yields the OTSL tag run. +The Rust loop (`crates/fleischwolf-pdf/src/tableformer.rs`) feeds the growing +token list back in and applies docling's two corrections (`xcel→lcel` on *every* +row — docling's `line_num` is never incremented; `ucel`-then-`lcel → fcel`). +**Verified: this reproduces docling's OTSL token sequence byte-exact** on +docling's own preprocessed table tensor (54-token sequence on `2305v1-pg9`). + +Two findings that cost real debugging, recorded so they aren't re-hit: +- The model's decoder layer keeps only `tgt[-1:]` per layer and relies on a + non-standard per-layer cache; re-running it cache-less loses deep context. + Equivalent stateless form: apply each layer to the whole prefix under a causal + mask. (Export uses the dynamo exporter so the `seq` axis stays symbolic — the + legacy tracer bakes it into `nn.MultiheadAttention`'s reshape.) +- **Export from `docling-project/docling-models`, not `ds4sd/docling-models`** — + both are cached, weights differ, and the old ones give a different OTSL. + +**Remaining (the Rust integration, staged):** + +1. **Preprocessing parity.** The decode is exact on docling's input tensor, but + the pipeline's own crop differs (docling resizes the *page* to 1024px height, + crops the table bbox, then resizes to 448²; we crop the 2× render directly). + Match this so the live OTSL matches (currently 88 vs 54 tokens on `2305v1-pg9` + purely from the crop). 2. **Bbox decoder** (`bbox.onnx`, not yet exported): per-cell hidden → box, for matching. Interim: skip it and match by grid geometry. 3. **OTSL → grid.** `docling_ibm_models/tableformer/otsl.py` is the reference @@ -152,10 +162,6 @@ A cheaper interim improvement (not docling-exact, but closes some diff): better geometric reconstruction — detect header rows, merge obvious spanning cells, and handle the multi-line header cells that currently shatter into many columns. -A cheaper interim improvement (not docling-exact, but closes some diff): better -geometric reconstruction — detect header rows, merge obvious spanning cells, and -handle the multi-line header cells that currently shatter into many columns. - ## Blocker 3 — RTL / bidi (Arabic) `right_to_left_01/02/03`. Two compounding issues: (a) reading order — Latin runs diff --git a/crates/fleischwolf-pdf/examples/tf_otsl.rs b/crates/fleischwolf-pdf/examples/tf_otsl.rs new file mode 100644 index 00000000..6908e4c0 --- /dev/null +++ b/crates/fleischwolf-pdf/examples/tf_otsl.rs @@ -0,0 +1,59 @@ +//! Verify TableFormer inference: run it on every table region of a PDF and print +//! the predicted OTSL structure. Usage: `... --example tf_otsl -- file.pdf` + +use fleischwolf_pdf::layout::LayoutModel; +use fleischwolf_pdf::tableformer::TableFormer; +use fleischwolf_pdf::PdfDocument; +use image::imageops; + +fn name(t: i64) -> &'static str { + match t { + 4 => "ecel", + 5 => "fcel", + 6 => "lcel", + 7 => "ucel", + 8 => "xcel", + 9 => "nl", + 10 => "ched", + 11 => "rhed", + 12 => "srow", + _ => "?", + } +} + +fn main() { + let path = std::env::args().nth(1).expect("usage: tf_otsl "); + let bytes = std::fs::read(&path).expect("read"); + let doc = PdfDocument::open(&bytes, None).expect("open"); + let mut layout = LayoutModel::load().expect("layout"); + let mut tf = TableFormer::load().expect("tableformer models missing"); + for (pi, page) in doc.pages.iter().enumerate() { + let regions = layout + .predict(&page.image, page.width, page.height) + .expect("layout"); + for r in regions.iter().filter(|r| r.label == "table") { + let s = page.scale; + let x = (r.l * s).max(0.0) as u32; + let y = (r.t * s).max(0.0) as u32; + let w = ((r.r - r.l) * s) as u32; + let h = ((r.b - r.t) * s) as u32; + let crop = imageops::crop_imm(&page.image, x, y, w, h).to_image(); + let otsl = tf.predict_otsl(&crop).expect("predict"); + let rows = otsl.iter().filter(|&&t| t == 9).count(); + let cols = otsl.iter().take_while(|&&t| t != 9).count(); + println!( + "page {} table {}x{}px -> {} tokens, {} rows x {} cols", + pi + 1, + w, + h, + otsl.len(), + rows, + cols + ); + println!( + " {}", + otsl.iter().map(|&t| name(t)).collect::>().join(" ") + ); + } + } +} diff --git a/crates/fleischwolf-pdf/src/lib.rs b/crates/fleischwolf-pdf/src/lib.rs index 845a5a6b..a81f211f 100644 --- a/crates/fleischwolf-pdf/src/lib.rs +++ b/crates/fleischwolf-pdf/src/lib.rs @@ -14,6 +14,7 @@ pub mod layout; mod mets; mod ocr; mod pdfium_backend; +pub mod tableformer; use std::fmt; diff --git a/crates/fleischwolf-pdf/src/tableformer.rs b/crates/fleischwolf-pdf/src/tableformer.rs new file mode 100644 index 00000000..68e22d06 --- /dev/null +++ b/crates/fleischwolf-pdf/src/tableformer.rs @@ -0,0 +1,132 @@ +//! TableFormer: table-structure recovery via docling-ibm-models, exported to +//! ONNX by `scripts/export_tableformer.py`. The image encoder + tag-transformer +//! encoder run once to a memory tensor; the decoder is then stepped +//! autoregressively to emit an OTSL structure-token sequence (the same model +//! docling runs). See PDF_CONFORMANCE.md. + +use image::imageops::FilterType; +use image::RgbImage; +use ort::session::Session; +use ort::value::Tensor; + +const SIDE: u32 = 448; +// Verbatim from docling's tm_config.json image_normalization (more digits than +// f32 holds; kept exact for provenance). +#[allow(clippy::excessive_precision)] +const MEAN: [f32; 3] = [0.94247851, 0.94254675, 0.94292611]; +#[allow(clippy::excessive_precision)] +const STD: [f32; 3] = [0.17910956, 0.17940403, 0.17931663]; +const MAX_STEPS: usize = 1024; + +/// OTSL structure tokens (TableModel04_rs wordmap indices). +pub const START: i64 = 2; +pub const END: i64 = 3; +pub const ECEL: i64 = 4; // empty cell +pub const FCEL: i64 = 5; // full (content) cell +pub const LCEL: i64 = 6; // left-looking: extends the cell to its left (colspan) +pub const UCEL: i64 = 7; // up-looking: extends the cell above (rowspan) +pub const XCEL: i64 = 8; // cross: spans both ways +pub const NL: i64 = 9; // new row +pub const CHED: i64 = 10; // column header +pub const RHED: i64 = 11; // row header +pub const SROW: i64 = 12; // section row + +pub struct TableFormer { + encoder: Session, + decoder: Session, +} + +impl TableFormer { + /// Load the exported encoder/decoder ONNX graphs (env overrides, else + /// `models/tableformer/{encoder,decoder}.onnx`). Returns `None` if either is + /// absent, so the pipeline falls back to geometric reconstruction. + pub fn load() -> Option { + let enc = std::env::var("DOCLING_TABLEFORMER_ENCODER") + .unwrap_or_else(|_| "models/tableformer/encoder.onnx".to_string()); + let dec = std::env::var("DOCLING_TABLEFORMER_DECODER") + .unwrap_or_else(|_| "models/tableformer/decoder.onnx".to_string()); + if !std::path::Path::new(&enc).exists() || !std::path::Path::new(&dec).exists() { + return None; + } + let build = |path: &str| -> Result { + Session::builder() + .map_err(|e| e.to_string())? + .with_intra_threads(crate::intra_threads()) + .map_err(|e| e.to_string())? + .commit_from_file(path) + .map_err(|e| format!("tableformer load {path}: {e}")) + }; + match (build(&enc), build(&dec)) { + (Ok(encoder), Ok(decoder)) => Some(Self { encoder, decoder }), + _ => None, + } + } + + /// Predict the OTSL structure-token sequence for a table-region image. + pub fn predict_otsl(&mut self, img: &RgbImage) -> Result, String> { + // Preprocess: resize to 448², normalize per channel, lay out CHW. + let resized = image::imageops::resize(img, SIDE, SIDE, FilterType::Triangle); + let n = (SIDE * SIDE) as usize; + let mut data = vec![0f32; 3 * n]; + for (i, px) in resized.pixels().enumerate() { + for c in 0..3 { + data[c * n + i] = (px[c] as f32 / 255.0 - MEAN[c]) / STD[c]; + } + } + let input = Tensor::from_array(([1usize, 3, SIDE as usize, SIDE as usize], data)) + .map_err(|e| format!("tableformer: input: {e}"))?; + let enc_out = self + .encoder + .run(ort::inputs!["image" => input]) + .map_err(|e| format!("tableformer: encode: {e}"))?; + let (mshape, mem) = enc_out["memory"] + .try_extract_tensor::() + .map_err(|e| format!("tableformer: memory: {e}"))?; + let mshape: Vec = mshape.iter().map(|&x| x as usize).collect(); + let mem: Vec = mem.to_vec(); + + // Autoregressive decode: the decoder graph re-applies the layers to the + // whole prefix under a causal mask (statelessly reproducing the model's + // per-layer cache), so we just feed the growing token list back in. The + // two structure corrections mirror docling's `predict` exactly — note its + // `line_num` is never incremented, so `xcel→lcel` applies on every row. + let mut tags: Vec = vec![START]; + let mut out: Vec = Vec::new(); + let mut prev_ucel = false; + while out.len() < MAX_STEPS { + let tags_t = Tensor::from_array(([tags.len(), 1usize], tags.clone())) + .map_err(|e| format!("tableformer: tags: {e}"))?; + let mem_t = Tensor::from_array((mshape.clone(), mem.clone())) + .map_err(|e| format!("tableformer: mem: {e}"))?; + let dout = self + .decoder + .run(ort::inputs!["tags" => tags_t, "memory" => mem_t]) + .map_err(|e| format!("tableformer: decode: {e}"))?; + let (_, logits) = dout["logits"] + .try_extract_tensor::() + .map_err(|e| format!("tableformer: logits: {e}"))?; + let mut tag = argmax(logits) as i64; + if tag == XCEL { + tag = LCEL; + } + if prev_ucel && tag == LCEL { + tag = FCEL; + } + if tag == END { + break; + } + out.push(tag); + tags.push(tag); + prev_ucel = tag == UCEL; + } + Ok(out) + } +} + +fn argmax(v: &[f32]) -> usize { + v.iter() + .enumerate() + .max_by(|a, b| a.1.total_cmp(b.1)) + .map(|(i, _)| i) + .unwrap_or(0) +} diff --git a/scripts/export_tableformer.py b/scripts/export_tableformer.py index 80ff26b1..34f018bb 100644 --- a/scripts/export_tableformer.py +++ b/scripts/export_tableformer.py @@ -10,6 +10,14 @@ decoder.onnx : tags[seq,1] + memory -> logits[1,V], hidden[1,512] bbox.onnx : memory + cell_hidden[ncells,512] -> classes, coords (optional) +IMPORTANT: export from the *same* checkpoint docling runs. Current docling pulls +`docling-project/docling-models` (NOT the older `ds4sd/docling-models`); their +TableFormer weights differ and produce different OTSL. Point the arg at: + ~/.cache/huggingface/hub/models--docling-project--docling-models/snapshots/*/model_artifacts/tableformer/accurate + +Verified: with these weights the exported graphs reproduce docling's OTSL token +sequence byte-exact on docling's own preprocessed table tensor. + Run inside the docling venv: .venv-compare/bin/python scripts/export_tableformer.py [out_dir] """ @@ -55,10 +63,20 @@ def forward(self, img): class Decode(nn.Module): + # The model's custom decoder layer keeps only the last token per layer and + # relies on a (non-standard) cache for the previous tokens' per-layer states. + # Re-running it cache-less loses deep context. Equivalently and statelessly we + # apply each layer to the *whole* prefix under a causal mask — verified to + # match the cache-based output exactly — so the ONNX graph is a plain step. def forward(self, tags, memory): - emb = tt._positional_encoding(tt._embedding(tags)) - decoded, _ = tt._decoder(emb, memory, None, memory_key_padding_mask=None) - last = decoded[-1] + o = tt._positional_encoding(tt._embedding(tags)) + s = o.shape[0] + cm = torch.triu(torch.full((s, s), float("-inf")), diagonal=1) + for mod in tt._decoder.layers: + o = mod.norm1(o + mod.self_attn(o, o, o, attn_mask=cm, need_weights=False)[0]) + o = mod.norm2(o + mod.multihead_attn(o, memory, memory, need_weights=False)[0]) + o = mod.norm3(o + mod.linear2(mod.activation(mod.linear1(o)))) + last = o[-1] return tt._fc(last), last @@ -77,13 +95,19 @@ def check(name, a, b): Encode(), (img,), f"{OUT}/encoder.onnx", input_names=["image"], output_names=["memory"], opset_version=17, dynamo=False, ) -tags = torch.full((3, 1), start, dtype=torch.long) +tags = torch.full((4, 1), start, dtype=torch.long) with torch.no_grad(): logits, hidden = Decode()(tags, mem) +# The dynamo exporter is needed here: the legacy tracer bakes the sequence length +# into nn.MultiheadAttention's reshape, so a 1-token first step fails. dynamo keeps +# the `seq` axis symbolic. +from torch.export import Dim # noqa: E402 + +seq = Dim("seq", min=1, max=1024) torch.onnx.export( Decode(), (tags, mem), f"{OUT}/decoder.onnx", input_names=["tags", "memory"], output_names=["logits", "hidden"], - dynamic_axes={"tags": {0: "seq"}}, opset_version=17, dynamo=False, + dynamo=True, dynamic_shapes=({0: seq}, {}), ) import onnxruntime as ort # noqa: E402 From afbb4c81bd8959a532d3c5fae731340ca4d06ca6 Mon Sep 17 00:00:00 2001 From: artiz Date: Sun, 28 Jun 2026 23:18:26 +0200 Subject: [PATCH 09/42] =?UTF-8?q?feat(tableformer):=20replicate=20docling'?= =?UTF-8?q?s=20table-crop=20preprocessing=20(page=E2=86=921024px=E2=86=92b?= =?UTF-8?q?box=E2=86=92448)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The decode is byte-exact on docling's input tensor; the live pipeline must feed the same pixels. docling resizes the whole page to 1024px height (cv2.INTER_AREA), crops the table bbox out of that, then resizes the crop to 448² (bilinear). The tf_otsl example now does the page→1024px (box-average) crop, and predict_otsl's 448 resize is bilinear, matching docling's two distinct interpolations. This lands the correct method and confirms the crop geometry matches docling (434×172px bbox at the same position on 2305v1-pg9). It does NOT yet reproduce docling's OTSL live: the model is hyper-sensitive to exact pixels, and matching docling's full raster pipeline byte-for-byte across different libraries (pdfium render anti-aliasing, cv2 INTER_AREA, torchvision bilinear) is a separate, hard parity problem — different filter choices swing the token count (54 target vs 88/121 observed). Recorded as the gating step in PDF_CONFORMANCE.md before OTSL→grid, cell matching, and serialization. Co-Authored-By: Claude Opus 4.8 (1M context) --- crates/fleischwolf-pdf/examples/tf_otsl.rs | 18 ++++++++++++------ crates/fleischwolf-pdf/src/tableformer.rs | 6 ++++-- 2 files changed, 16 insertions(+), 8 deletions(-) diff --git a/crates/fleischwolf-pdf/examples/tf_otsl.rs b/crates/fleischwolf-pdf/examples/tf_otsl.rs index 6908e4c0..3f115905 100644 --- a/crates/fleischwolf-pdf/examples/tf_otsl.rs +++ b/crates/fleischwolf-pdf/examples/tf_otsl.rs @@ -31,13 +31,19 @@ fn main() { let regions = layout .predict(&page.image, page.width, page.height) .expect("layout"); + // docling resizes the whole page to 1024px height, then crops the table + // bbox out of *that*. Replicate so the model sees the same pixels. + let sf = 1024.0 / page.image.height() as f32; + let pw1024 = (page.image.width() as f32 * sf).round() as u32; + let page1024 = imageops::thumbnail(&page.image, pw1024, 1024); for r in regions.iter().filter(|r| r.label == "table") { - let s = page.scale; - let x = (r.l * s).max(0.0) as u32; - let y = (r.t * s).max(0.0) as u32; - let w = ((r.r - r.l) * s) as u32; - let h = ((r.b - r.t) * s) as u32; - let crop = imageops::crop_imm(&page.image, x, y, w, h).to_image(); + // bbox (points) → 1024px-page coords: scale*sf = 1024/page_h_pt. + let k = 1024.0 / page.height; + let x = (r.l * k).max(0.0) as u32; + let y = (r.t * k).max(0.0) as u32; + let w = ((r.r - r.l) * k) as u32; + let h = ((r.b - r.t) * k) as u32; + let crop = imageops::crop_imm(&page1024, x, y, w, h).to_image(); let otsl = tf.predict_otsl(&crop).expect("predict"); let rows = otsl.iter().filter(|&&t| t == 9).count(); let cols = otsl.iter().take_while(|&&t| t != 9).count(); diff --git a/crates/fleischwolf-pdf/src/tableformer.rs b/crates/fleischwolf-pdf/src/tableformer.rs index 68e22d06..6f0b725b 100644 --- a/crates/fleischwolf-pdf/src/tableformer.rs +++ b/crates/fleischwolf-pdf/src/tableformer.rs @@ -4,7 +4,6 @@ //! autoregressively to emit an OTSL structure-token sequence (the same model //! docling runs). See PDF_CONFORMANCE.md. -use image::imageops::FilterType; use image::RgbImage; use ort::session::Session; use ort::value::Tensor; @@ -65,7 +64,10 @@ impl TableFormer { /// Predict the OTSL structure-token sequence for a table-region image. pub fn predict_otsl(&mut self, img: &RgbImage) -> Result, String> { // Preprocess: resize to 448², normalize per channel, lay out CHW. - let resized = image::imageops::resize(img, SIDE, SIDE, FilterType::Triangle); + // docling's final 448 resize is BILINEAR (the page→1024px step that + // box-averages happens earlier, in the caller). + let resized = + image::imageops::resize(img, SIDE, SIDE, image::imageops::FilterType::Triangle); let n = (SIDE * SIDE) as usize; let mut data = vec![0f32; 3 * n]; for (i, px) in resized.pixels().enumerate() { From 5521e75fa41905ce72739a42ff8d0b4673e2d0af Mon Sep 17 00:00:00 2001 From: artiz Date: Mon, 29 Jun 2026 07:29:43 +0200 Subject: [PATCH 10/42] =?UTF-8?q?feat(tableformer):=20pixel-exact=20prepro?= =?UTF-8?q?cessing=20=E2=80=94=20live=20OTSL=20now=20byte-exact=20vs=20doc?= =?UTF-8?q?ling?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Reproduces docling's TableFormer preprocessing pipeline exactly, so the live Rust inference now emits the SAME OTSL token sequence docling does (verified on 2305v1-pg9: 54 tokens, 6×8, identical; and all 5 tables of 2206.01062: matching token counts). The model is hyper-sensitive to input pixels; four things had to match byte-for-byte: - Render: docling renders at 1.5× the target scale and downsamples (pypdfium2 → PIL BICUBIC). pdfium_backend now supersamples 3× and downsamples to 2× with a Catmull-Rom (a=-0.5) cubic — the same kernel. Cuts page-bitmap diff vs docling from 8.7% of pixels (max 185) to 2.0% (max 12). - Page→1024px: cv2.INTER_AREA. New resample::inter_area is a from-scratch, separable area-average resampler, verified vs cv2 (max diff 1/255). - Crop→448: cv2.INTER_LINEAR done in float (not rounded u8), folded into predict_otsl, verified vs cv2 (<1e-4). - Tensor layout: docling transposes (2,1,0) → the model wants (C, W, H), not C,H,W. This transpose alone was scrambling rows/cols. The supersampled render is now used for every page (matches docling); regenerated 16 snapshot fixtures, pdf_conformance stays 76/76, groundtruth text PDFs unchanged. Adds the dump_stages example for render-parity debugging. Next: OTSL→grid with spans, cell-text matching, serialization, pipeline wiring. Co-Authored-By: Claude Opus 4.8 (1M context) --- .../fleischwolf-pdf/examples/dump_stages.rs | 20 ++++++ crates/fleischwolf-pdf/examples/tf_otsl.rs | 20 +++--- crates/fleischwolf-pdf/src/lib.rs | 1 + crates/fleischwolf-pdf/src/pdfium_backend.rs | 14 +++- crates/fleischwolf-pdf/src/resample.rs | 70 +++++++++++++++++++ crates/fleischwolf-pdf/src/tableformer.rs | 37 +++++++--- .../latex/sources/2305.03393/llncsdoc.pdf.md | 2 +- .../sources/2310.06825/images/swa.pdf.md | 6 +- .../figures/relative_expert_load_multi.pdf.md | 4 -- .../relative_expert_load_multi_7-12.pdf.md | 6 -- .../sources/odf_presentation_02.odp.pdf.md | 2 - .../pdf/sources/2203.01017v2.pdf.md | 33 +++++++-- .../pdf/sources/2206.01062.pdf.md | 4 +- .../pdf/sources/normal_4pages.pdf.md | 2 +- .../pdf/sources/redp5110_sampled.pdf.md | 8 ++- .../pdf/sources/skipped_1page.pdf.md | 2 - .../pdf/sources/skipped_2pages.pdf.md | 2 - .../table_mislabeled_as_picture.pdf.md | 1 - .../scanned/sources/nemotron_multipage.pdf.md | 8 +-- .../sources/ocr_test_rotated_180.pdf.md | 2 +- .../sources/ocr_test_rotated_270.pdf.md | 2 +- .../sources/ocr_test_rotated_90.pdf.md | 4 +- 22 files changed, 191 insertions(+), 59 deletions(-) create mode 100644 crates/fleischwolf-pdf/examples/dump_stages.rs create mode 100644 crates/fleischwolf-pdf/src/resample.rs diff --git a/crates/fleischwolf-pdf/examples/dump_stages.rs b/crates/fleischwolf-pdf/examples/dump_stages.rs new file mode 100644 index 00000000..f12f8e70 --- /dev/null +++ b/crates/fleischwolf-pdf/examples/dump_stages.rs @@ -0,0 +1,20 @@ +//! Dump preprocessing stages for render-parity debugging against docling. +//! Usage: `... --example dump_stages -- file.pdf ` +use fleischwolf_pdf::PdfDocument; +use image::imageops; + +fn main() { + let path = std::env::args().nth(1).expect("pdf"); + let out = std::env::args().nth(2).expect("out_dir"); + let bytes = std::fs::read(&path).expect("read"); + let doc = PdfDocument::open(&bytes, None).expect("open"); + let page = &doc.pages[0]; + page.image.save(format!("{out}/my_page.png")).unwrap(); + println!("my_page: {}x{}", page.image.width(), page.image.height()); + + let sf = 1024.0 / page.image.height() as f32; + let pw = (page.image.width() as f32 * sf).round() as u32; + let p1024 = imageops::thumbnail(&page.image, pw, 1024); + p1024.save(format!("{out}/my_p1024.png")).unwrap(); + println!("my_p1024: {}x{}", p1024.width(), p1024.height()); +} diff --git a/crates/fleischwolf-pdf/examples/tf_otsl.rs b/crates/fleischwolf-pdf/examples/tf_otsl.rs index 3f115905..51bcc9d1 100644 --- a/crates/fleischwolf-pdf/examples/tf_otsl.rs +++ b/crates/fleischwolf-pdf/examples/tf_otsl.rs @@ -31,18 +31,20 @@ fn main() { let regions = layout .predict(&page.image, page.width, page.height) .expect("layout"); - // docling resizes the whole page to 1024px height, then crops the table - // bbox out of *that*. Replicate so the model sees the same pixels. + // docling resizes the whole page to 1024px height (cv2.INTER_AREA), then + // crops the table bbox out of *that*. Replicate exactly. let sf = 1024.0 / page.image.height() as f32; - let pw1024 = (page.image.width() as f32 * sf).round() as u32; - let page1024 = imageops::thumbnail(&page.image, pw1024, 1024); + let pw1024 = (page.image.width() as f32 * sf) as u32; // docling: int(w*r) + let page1024 = fleischwolf_pdf::resample::inter_area(&page.image, pw1024, 1024); for r in regions.iter().filter(|r| r.label == "table") { - // bbox (points) → 1024px-page coords: scale*sf = 1024/page_h_pt. + // bbox (points) → 1024px-page coords: scale*sf = 1024/page_h_pt; + // docling rounds the crop edges. let k = 1024.0 / page.height; - let x = (r.l * k).max(0.0) as u32; - let y = (r.t * k).max(0.0) as u32; - let w = ((r.r - r.l) * k) as u32; - let h = ((r.b - r.t) * k) as u32; + let x = (r.l * k).round().max(0.0) as u32; + let y = (r.t * k).round().max(0.0) as u32; + let x2 = (r.r * k).round() as u32; + let y2 = (r.b * k).round() as u32; + let (w, h) = (x2 - x, y2 - y); let crop = imageops::crop_imm(&page1024, x, y, w, h).to_image(); let otsl = tf.predict_otsl(&crop).expect("predict"); let rows = otsl.iter().filter(|&&t| t == 9).count(); diff --git a/crates/fleischwolf-pdf/src/lib.rs b/crates/fleischwolf-pdf/src/lib.rs index a81f211f..f48f3513 100644 --- a/crates/fleischwolf-pdf/src/lib.rs +++ b/crates/fleischwolf-pdf/src/lib.rs @@ -14,6 +14,7 @@ pub mod layout; mod mets; mod ocr; mod pdfium_backend; +pub mod resample; pub mod tableformer; use std::fmt; diff --git a/crates/fleischwolf-pdf/src/pdfium_backend.rs b/crates/fleischwolf-pdf/src/pdfium_backend.rs index 0bb71d4c..ed5b6b07 100644 --- a/crates/fleischwolf-pdf/src/pdfium_backend.rs +++ b/crates/fleischwolf-pdf/src/pdfium_backend.rs @@ -115,13 +115,21 @@ fn extract_page( cells = segment_cells(&page.text()?, height); } - let tw = (width * RENDER_SCALE).round().max(1.0) as i32; - let th = (height * RENDER_SCALE).round().max(1.0) as i32; + // docling renders at 1.5× the target scale and downsamples "to make it + // sharper" (pypdfium2 → PIL BICUBIC). Replicate exactly: the TableFormer + // model is pixel-sensitive, so the page bitmap must match byte-for-byte. + // `CatmullRom` is the same a=-0.5 cubic kernel as PIL's BICUBIC. + const SUPERSAMPLE: f32 = 1.5; + let tw = (width * RENDER_SCALE * SUPERSAMPLE).round().max(1.0) as i32; + let th = (height * RENDER_SCALE * SUPERSAMPLE).round().max(1.0) as i32; let cfg = PdfRenderConfig::new() .set_target_width(tw) .set_target_height(th); let bitmap = page.render_with_config(&cfg)?; - let image = bitmap.as_image().into_rgb8(); + let big = bitmap.as_image().into_rgb8(); + let dw = (width * RENDER_SCALE).round().max(1.0) as u32; + let dh = (height * RENDER_SCALE).round().max(1.0) as u32; + let image = image::imageops::resize(&big, dw, dh, image::imageops::FilterType::CatmullRom); Ok(PdfPage { width, diff --git a/crates/fleischwolf-pdf/src/resample.rs b/crates/fleischwolf-pdf/src/resample.rs new file mode 100644 index 00000000..a88c849b --- /dev/null +++ b/crates/fleischwolf-pdf/src/resample.rs @@ -0,0 +1,70 @@ +//! Pixel-exact reimplementations of the OpenCV resize kernels docling uses for +//! TableFormer preprocessing, so the model sees byte-identical input. Verified +//! against cv2 on docling's own bitmaps (INTER_AREA max diff 1/255, INTER_LINEAR +//! < 1e-4 in float). + +use image::{Rgb, RgbImage}; + +/// Per-output-pixel source spans + overlap weights for area resampling. +fn area_weights(src: usize, dst: usize, scale: f64) -> Vec> { + (0..dst) + .map(|d| { + let f1 = d as f64 * scale; + let f2 = (d + 1) as f64 * scale; + let s1 = f1.floor() as usize; + let s2 = (f2.ceil() as usize).min(src); + (s1..s2) + .map(|si| { + let w = (((si + 1) as f64).min(f2) - (si as f64).max(f1)) / scale; + (si, w) + }) + .collect() + }) + .collect() +} + +/// `cv2.resize(..., interpolation=INTER_AREA)` for shrinking — area-weighted +/// averaging, separable (horizontal then vertical), f64 accumulation. +pub fn inter_area(src: &RgbImage, dw: u32, dh: u32) -> RgbImage { + let (sw, sh) = (src.width() as usize, src.height() as usize); + let (dwu, dhu) = (dw as usize, dh as usize); + let hw = area_weights(sw, dwu, sw as f64 / dw as f64); + let vw = area_weights(sh, dhu, sh as f64 / dh as f64); + + let mut tmp = vec![[0f64; 3]; sh * dwu]; // (sh × dw) + for y in 0..sh { + let row = y * dwu; + for (dx, ws) in hw.iter().enumerate() { + let mut acc = [0f64; 3]; + for &(si, w) in ws { + let p = src.get_pixel(si as u32, y as u32); + acc[0] += p[0] as f64 * w; + acc[1] += p[1] as f64 * w; + acc[2] += p[2] as f64 * w; + } + tmp[row + dx] = acc; + } + } + let mut out = RgbImage::new(dw, dh); + for (dy, ws) in vw.iter().enumerate() { + for dx in 0..dwu { + let mut acc = [0f64; 3]; + for &(si, w) in ws { + let t = tmp[si * dwu + dx]; + acc[0] += t[0] * w; + acc[1] += t[1] * w; + acc[2] += t[2] * w; + } + out.put_pixel( + dx as u32, + dy as u32, + Rgb([round_u8(acc[0]), round_u8(acc[1]), round_u8(acc[2])]), + ); + } + } + out +} + +fn round_u8(v: f64) -> u8 { + v.round().clamp(0.0, 255.0) as u8 +} diff --git a/crates/fleischwolf-pdf/src/tableformer.rs b/crates/fleischwolf-pdf/src/tableformer.rs index 6f0b725b..895e5485 100644 --- a/crates/fleischwolf-pdf/src/tableformer.rs +++ b/crates/fleischwolf-pdf/src/tableformer.rs @@ -63,16 +63,37 @@ impl TableFormer { /// Predict the OTSL structure-token sequence for a table-region image. pub fn predict_otsl(&mut self, img: &RgbImage) -> Result, String> { - // Preprocess: resize to 448², normalize per channel, lay out CHW. - // docling's final 448 resize is BILINEAR (the page→1024px step that - // box-averages happens earlier, in the caller). - let resized = - image::imageops::resize(img, SIDE, SIDE, image::imageops::FilterType::Triangle); + // Preprocess exactly as docling: bilinear (cv2.INTER_LINEAR) resize the + // crop to 448², normalize (x/255 − mean)/std, laid out as (C, W, H) — + // docling transposes (2,1,0), so width is the major spatial axis (not + // C,H,W). The page→1024px box-average (cv2.INTER_AREA) is the caller's. let n = (SIDE * SIDE) as usize; + let side = SIDE as usize; + let (sw, sh) = (img.width() as i32, img.height() as i32); + let sxr = sw as f32 / SIDE as f32; + let syr = sh as f32 / SIDE as f32; let mut data = vec![0f32; 3 * n]; - for (i, px) in resized.pixels().enumerate() { - for c in 0..3 { - data[c * n + i] = (px[c] as f32 / 255.0 - MEAN[c]) / STD[c]; + for h in 0..side { + let fy = (h as f32 + 0.5) * syr - 0.5; + let wy = fy - fy.floor(); + let y0c = (fy.floor() as i32).clamp(0, sh - 1) as u32; + let y1c = (fy.floor() as i32 + 1).clamp(0, sh - 1) as u32; + for w in 0..side { + let fx = (w as f32 + 0.5) * sxr - 0.5; + let wx = fx - fx.floor(); + let x0c = (fx.floor() as i32).clamp(0, sw - 1) as u32; + let x1c = (fx.floor() as i32 + 1).clamp(0, sw - 1) as u32; + let p00 = img.get_pixel(x0c, y0c); + let p01 = img.get_pixel(x1c, y0c); + let p10 = img.get_pixel(x0c, y1c); + let p11 = img.get_pixel(x1c, y1c); + let idx = w * side + h; // (C, W, H): c*n + w*H + h + for c in 0..3 { + let top = p00[c] as f32 * (1.0 - wx) + p01[c] as f32 * wx; + let bot = p10[c] as f32 * (1.0 - wx) + p11[c] as f32 * wx; + let v = top * (1.0 - wy) + bot * wy; + data[c * n + idx] = (v / 255.0 - MEAN[c]) / STD[c]; + } } } let input = Tensor::from_array(([1usize, 3, SIDE as usize, SIDE as usize], data)) diff --git a/tests/pdf_snapshots/latex/sources/2305.03393/llncsdoc.pdf.md b/tests/pdf_snapshots/latex/sources/2305.03393/llncsdoc.pdf.md index 62d75fab..ff4cb455 100644 --- a/tests/pdf_snapshots/latex/sources/2305.03393/llncsdoc.pdf.md +++ b/tests/pdf_snapshots/latex/sources/2305.03393/llncsdoc.pdf.md @@ -30,7 +30,7 @@ The llncs class is invoked by replacing article by llncs in the first line of yo If your file is already coded with LATEX, you can easily adapt it to the llncs document class by replacing -\documentclass{article} with \documentclass{llncs} +\documentclass{article} with diff --git a/tests/pdf_snapshots/latex/sources/2310.06825/images/swa.pdf.md b/tests/pdf_snapshots/latex/sources/2310.06825/images/swa.pdf.md index ad1ed05c..0847b137 100644 --- a/tests/pdf_snapshots/latex/sources/2310.06825/images/swa.pdf.md +++ b/tests/pdf_snapshots/latex/sources/2310.06825/images/swa.pdf.md @@ -2,12 +2,10 @@ Effective Context Length - - -Vanilla Attention +Sliding Window Attention -Sliding Window Attention +Vanilla Attention diff --git a/tests/pdf_snapshots/latex/sources/2412.19437/figures/relative_expert_load_multi.pdf.md b/tests/pdf_snapshots/latex/sources/2412.19437/figures/relative_expert_load_multi.pdf.md index 0e5ee4a1..d01027fa 100644 --- a/tests/pdf_snapshots/latex/sources/2412.19437/figures/relative_expert_load_multi.pdf.md +++ b/tests/pdf_snapshots/latex/sources/2412.19437/figures/relative_expert_load_multi.pdf.md @@ -2,8 +2,4 @@ -- Github - - - diff --git a/tests/pdf_snapshots/latex/sources/2412.19437/figures/relative_expert_load_multi_7-12.pdf.md b/tests/pdf_snapshots/latex/sources/2412.19437/figures/relative_expert_load_multi_7-12.pdf.md index 72526975..ff1c8ef0 100644 --- a/tests/pdf_snapshots/latex/sources/2412.19437/figures/relative_expert_load_multi_7-12.pdf.md +++ b/tests/pdf_snapshots/latex/sources/2412.19437/figures/relative_expert_load_multi_7-12.pdf.md @@ -1,9 +1,3 @@ -DM Mathematics Aux-Loss-Based Layer 10 12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758596061626364Wikipedia (en) Github - -DM Mathematics Aux-Loss-Free Layer 10 12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758596061626364Wikipedia (en) Github - -DM Mathematics Aux-Loss-Based Layer 11 12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758596061626364Wikipedia (en) Github - DM Mathematics Aux-Loss-Based Layer 12 12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758596061626364Wikipedia (en) Github diff --git a/tests/pdf_snapshots/odf/sources/odf_presentation_02.odp.pdf.md b/tests/pdf_snapshots/odf/sources/odf_presentation_02.odp.pdf.md index 0d843b9e..effd2638 100644 --- a/tests/pdf_snapshots/odf/sources/odf_presentation_02.odp.pdf.md +++ b/tests/pdf_snapshots/odf/sources/odf_presentation_02.odp.pdf.md @@ -26,8 +26,6 @@ m n m e a e t b a r . h n a i x p u e n t . o l M c i d m , o u l c N e r o u t o l -o - diff --git a/tests/pdf_snapshots/pdf/sources/2203.01017v2.pdf.md b/tests/pdf_snapshots/pdf/sources/2203.01017v2.pdf.md index 49bce6b2..112a662b 100644 --- a/tests/pdf_snapshots/pdf/sources/2203.01017v2.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/2203.01017v2.pdf.md @@ -217,6 +217,30 @@ Figure 5: One of the benefits of TableFormer is that it is language agnostic, as +- b. Structure predicted by TableFormer + +| 計 9452946511122955 Text is aligned to match original for ease of viewingWeighted Average Grant Date Fair | | RSUsShares (in millions) | | Value | +|------------------------------------------------------------------------------------------------------------|-------------------------------------------------|----------------------------|----------------|---------| +| | | | PSUs RSUs PSUs | | +| | Nonvested on January 11.10.390.10 $ $ 91.19 | | | | +| | Granted 0.50.1117.44122.41 | | | | +| | Vested (0.5) (0.1) 87.0881.14 | | | | +| | Canceled or forfeited (0.1) — 102.0192.18 | | | | +| | Nonvested on December 311.00.3104.85 $ $ 104.51 | | | | + +| | | 論文フ ァ イ ル 参考文献 | +|--------------------------------------------------------------|---------------------------|------------------| +| | 出典 フ ァ イ ル数 英語 日本語 英語 日本語 | | +| Association for Computational Linguistics(ACL2003) 656501500 | | | +| Computational Linguistics(COLING2002) 14014001500 | | | +| 電気情報通信学会2003年総合大会 1508142223147 | | | +| 情報処理学会第65回全国大会(2003) 1771176150236 | | | +| 第17回人工知能学会全国大会(2003) 2085203152244 | | | +| 自然言語処理研究会第146〜155回 98296150232 | | | +| WWWから収集 した論文 107733414796 | | | + + + Figure 6: An example of TableFormer predictions (bounding boxes and structure) from generated SynthTabNet table. @@ -272,9 +296,7 @@ Computer Vision and Pattern Recognition, pages 658-666, 2019. 6 - [35] Quanzeng You, Hailin Jin, Zhaowen Wang, Chen Fang, and Jiebo Luo. Image captioning with semantic attention. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4651-4659, 2016. 4 - [36] Xinyi Zheng, Doug Burdick, Lucian Popa, Peter Zhong, and Nancy Xin Ru Wang. Global table extractor (gte): A framework for joint table identification and cell structure recognition using visual context. Winter Conference for Applications in Computer Vision (WACV), 2021. 2, 3 - [37] Xu Zhong, Elaheh ShafieiBavani, and Antonio Ji - -Brox, and Jan-Michael Frahm, editors, Computer Vision - ECCV 2020, pages 564-580, Cham, 2020. Springer International Publishing. 2, 3, 7 - +- Brox, and Jan-Michael Frahm, editors, Computer Vision - ECCV 2020, pages 564-580, Cham, 2020. Springer International Publishing. 2, 3, 7 - [38] Xu Zhong, Jianbin Tang, and Antonio Jimeno Yepes. Publaynet: Largest dataset ever for document layout analysis. In 2019 International Conference on Document Analysis and Recognition (ICDAR), pages 1015-1022, 2019. 1 ## TableFormer: Table Structure Understanding with Transformers Supplementary Material @@ -344,7 +366,8 @@ ing the computations, helps to eliminate outliers caused by occasional column sp - 9b. Intersect the orphan's bounding box with the row bands, and map the cell to the closest grid row. - 9c. Compute the left and right boundary of the vertical band for each grid column (min/max x coordinates per column). - 9d. Intersect the orphan's bounding box with the column bands, and map the cell to the closest grid column. -- 9e. If the table cell under the identified row and column is not empty, extend its content with the content of the or + +9e. If the table cell under the identified row and column is not empty, extend its content with the content of the or phan cell. @@ -376,6 +399,8 @@ Figure 14: Example with multi-line text. + + mis-aligned bounding boxes prediction artifact. Figure 17: Example of long table. End-to-end example from initial PDF cells to prediction of bounding boxes, post process diff --git a/tests/pdf_snapshots/pdf/sources/2206.01062.pdf.md b/tests/pdf_snapshots/pdf/sources/2206.01062.pdf.md index ef0008f9..307de141 100644 --- a/tests/pdf_snapshots/pdf/sources/2206.01062.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/2206.01062.pdf.md @@ -60,7 +60,9 @@ In this paper , we present the DocLayNet dataset. It provides pageby-page layout - (2) Large Layout Variability: We include diverse and complex layouts from a large variety of public sources. - (3) Detailed Label Set: We define 11 class labels to distinguish layout features in high detail. PubLayNet provides 5 labels; DocBank provides 13, although not a superset of ours. - (4) Redundant Annotations: A fraction of the pages in the DocLayNet data set carry more than one human annotation. -- and quality control analysis. + +and quality control analysis. + - (5) Pre-defined Train-, Test- & Validation-set: Like DocBank, we provide fixed train-, test- & validation-sets to ensure proportional representation of the class-labels. Further , we prevent leakage of unique layouts across sets, which has a large effect on model accuracy scores. All aspects outlined above are detailed in Section 3. In Section 4, we will elaborate on how we designed and executed this large-scale human annotation campaign. We will also share key insights and lessons learned that might prove helpful for other parties planning to set up annotation campaigns. diff --git a/tests/pdf_snapshots/pdf/sources/normal_4pages.pdf.md b/tests/pdf_snapshots/pdf/sources/normal_4pages.pdf.md index 8bc0e3f5..e9919264 100644 --- a/tests/pdf_snapshots/pdf/sources/normal_4pages.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/normal_4pages.pdf.md @@ -28,7 +28,7 @@ 1) 한국표준질병·사인분류상의'S80~Y84'에 해당하는 우발적인 외래의 사고 -2) 감염병의 예방 및 관리에 관한 법률 제2조 제2호에서 규정한 감염병 +1) 한국표준질병·사인분류상의'S80~Y84'에 해당하는 우발적인 외래의 사고 2) 감염병의 예방 및 관리에 관한 법률 제2조 제2호에서 규정한 감염병 diff --git a/tests/pdf_snapshots/pdf/sources/redp5110_sampled.pdf.md b/tests/pdf_snapshots/pdf/sources/redp5110_sampled.pdf.md index b6611569..1f4e7d6d 100644 --- a/tests/pdf_snapshots/pdf/sources/redp5110_sampled.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/redp5110_sampled.pdf.md @@ -1,5 +1,7 @@ of duties + + Leverage row permissions on the database Protect columns by defining column masks @@ -8,8 +10,6 @@ Protect columns by defining column masks ## Row and Column Access Control - - Hernando Bedoya Rob Bestgen Mike Cain Dan Cruikshank Jim Denton Doug Mack Tom McKinley @@ -229,6 +229,8 @@ VERI FY_GROUP_FOR_USER (CURRENT_USER,' MGR') VERI FY_GROUP_FOR_USER (CURRENT_USE The following function invocation returns a value of 0: +The following function invocation returns a value of 0: + CASE WHEN VERI FY_GROUP_FOR_USER (SESSION_USER,' HR',' EMP') = 1 TH EN EMPLOYEES. DATE_OF_BI RTH WHEN VERI FY_GROUP_FOR_USER (SESSION_USER,' MGR') = 1 AND SESS ION_USER = EMPLOYEES. USER_I D TH EN EMPLOYEES. DATE_OF_BI RTH WHEN VERI FY_GROUP_FOR_USER (SESSION_USER,' MGR') = 1 AND SESS ION_USER <> EMPLOYEES. USER_I D TH EN (9999 | |' -' | | MONTH (EMPLOYEES. DATE_OF_BIRTH) | |' -' | | DAY (EMPLOYEES. DATE_OF_BIRTH)) END ELSE NULL ENABLE; - 2. The other column to mask in this example is the TAX_ID information. In this example, the rules to enforce include the following ones: @@ -255,7 +257,7 @@ Now that you have created the row permission and the two column masks, RCAC must Example 3-10 Activating RCAC on the EMPLOYEES table - +/* Acti ve Row Access Control (permi ssi ons) */ /* Acti ve Col umn Access Control (masks) */ ALTER TABLE HR_SCHEMA. EMPLOYEES ACT I VATE ROW ACCESS CONTROL ACT I VATE COLUMN ACCESS CONTROL; - 2. Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from the main navigation pane of System i Navigator, click Schemas  HR_SCHEMA  Tables, right-click the EMPLOYEES table, and click Definition. diff --git a/tests/pdf_snapshots/pdf/sources/skipped_1page.pdf.md b/tests/pdf_snapshots/pdf/sources/skipped_1page.pdf.md index c9dd6ed2..1c891afa 100644 --- a/tests/pdf_snapshots/pdf/sources/skipped_1page.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/skipped_1page.pdf.md @@ -4,8 +4,6 @@ - -  diff --git a/tests/pdf_snapshots/pdf/sources/skipped_2pages.pdf.md b/tests/pdf_snapshots/pdf/sources/skipped_2pages.pdf.md index 0af33074..5afffb58 100644 --- a/tests/pdf_snapshots/pdf/sources/skipped_2pages.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/skipped_2pages.pdf.md @@ -8,8 +8,6 @@ - -  diff --git a/tests/pdf_snapshots/pdf/sources/table_mislabeled_as_picture.pdf.md b/tests/pdf_snapshots/pdf/sources/table_mislabeled_as_picture.pdf.md index e4758a57..4d2fd6e9 100644 --- a/tests/pdf_snapshots/pdf/sources/table_mislabeled_as_picture.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/table_mislabeled_as_picture.pdf.md @@ -73,7 +73,6 @@ | | „ Other (Please specify) _____________________________________________________ | | - 27. If your country allows legal aid services through university-based student law clinics, what type of legal aid services is a student authorized to undertake? (Please select all that apply) -- „ They can represent people in administrative or civil law hearings - 28. Are specialized legal aid services provided focusing on specific disadvantaged population groups? If yes, please indicate to whom these services are provided, and whether they are provided by State-funded legal aid, civil society organizations, or both. | | legal aid CSOs | diff --git a/tests/pdf_snapshots/scanned/sources/nemotron_multipage.pdf.md b/tests/pdf_snapshots/scanned/sources/nemotron_multipage.pdf.md index 4f5a0595..1d00f88f 100644 --- a/tests/pdf_snapshots/scanned/sources/nemotron_multipage.pdf.md +++ b/tests/pdf_snapshots/scanned/sources/nemotron_multipage.pdf.md @@ -1,9 +1,9 @@ Docling bundles PDF document conversion to JSON and Markdown in an easy self contained package -aeroseaeoe e o ona +aerosaeoe e o a -H W ep ps ws e. uu P po +H W ep 9 ps 1s e. uu P1 po -te oda nn na sm 88 ek da sdo bd p0 M +te od nn na sm 88 ek da sd bd p0 M -e 60 a K C a p +e 00 a K C a p diff --git a/tests/pdf_snapshots/scanned/sources/ocr_test_rotated_180.pdf.md b/tests/pdf_snapshots/scanned/sources/ocr_test_rotated_180.pdf.md index f6184e5e..b7787b6c 100644 --- a/tests/pdf_snapshots/scanned/sources/ocr_test_rotated_180.pdf.md +++ b/tests/pdf_snapshots/scanned/sources/ocr_test_rotated_180.pdf.md @@ -1 +1 @@ -aeroseaeoe e o ona +aerosaeoe e o a diff --git a/tests/pdf_snapshots/scanned/sources/ocr_test_rotated_270.pdf.md b/tests/pdf_snapshots/scanned/sources/ocr_test_rotated_270.pdf.md index 9ad4b33e..4d299e55 100644 --- a/tests/pdf_snapshots/scanned/sources/ocr_test_rotated_270.pdf.md +++ b/tests/pdf_snapshots/scanned/sources/ocr_test_rotated_270.pdf.md @@ -1 +1 @@ -H W ep ps ws e. uu P po +H W ep 9 ps 1s e. uu P1 po diff --git a/tests/pdf_snapshots/scanned/sources/ocr_test_rotated_90.pdf.md b/tests/pdf_snapshots/scanned/sources/ocr_test_rotated_90.pdf.md index ba425e59..49d188eb 100644 --- a/tests/pdf_snapshots/scanned/sources/ocr_test_rotated_90.pdf.md +++ b/tests/pdf_snapshots/scanned/sources/ocr_test_rotated_90.pdf.md @@ -1,3 +1,3 @@ -te oda nn na sm 88 ek da sdo bd p0 M +te od nn na sm 88 ek da sd bd p0 M -e 60 a K C a p +e 00 a K C a p From e583d7ba23222977008fba4d056d2868179e91d7 Mon Sep 17 00:00:00 2001 From: artiz Date: Mon, 29 Jun 2026 07:58:10 +0200 Subject: [PATCH 11/42] fix(ocr): make recognition deterministic (single-threaded ORT) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ORT's multi-threaded float-reduction order varies across runs, flipping the CTC argmax on low-confidence characters. This was latent until the sharper supersampled render pushed a noisy fax (sample_with_rotation_mismatch) to borderline confidence, making its snapshot non-deterministic (pdf_conformance 75/76 drift). Recognition inputs are tiny per-line crops, so single-threaded ORT costs negligible throughput and restores determinism — the snapshot baseline is unchanged (the committed output already matched the deterministic one). Co-Authored-By: Claude Opus 4.8 (1M context) --- crates/fleischwolf-pdf/src/ocr.rs | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/crates/fleischwolf-pdf/src/ocr.rs b/crates/fleischwolf-pdf/src/ocr.rs index b5f70707..83826f28 100644 --- a/crates/fleischwolf-pdf/src/ocr.rs +++ b/crates/fleischwolf-pdf/src/ocr.rs @@ -47,9 +47,14 @@ impl OcrModel { std::env::var("DOCLING_OCR_REC_ONNX").unwrap_or_else(|_| "models/ocr_rec.onnx".into()); let dict_path = std::env::var("DOCLING_OCR_DICT").unwrap_or_else(|_| "models/ppocr_keys_v1.txt".into()); + // Single-threaded: ORT's multi-threaded float-reduction order varies + // across runs, which flips the CTC argmax on low-confidence characters + // (e.g. noisy faxes) and makes the snapshot output non-deterministic. The + // recognition inputs are tiny per-line crops, so the throughput cost is + // negligible. let rec = Session::builder() .map_err(|e| format!("ocr: builder: {e}"))? - .with_intra_threads(crate::intra_threads()) + .with_intra_threads(1) .map_err(|e| format!("ocr: intra_threads: {e}"))? .commit_from_file(&rec_path) .map_err(|e| format!("ocr: load {rec_path}: {e}"))?; From 11703a2797098424192ab087738ad4f2f79078fb Mon Sep 17 00:00:00 2001 From: artiz Date: Mon, 29 Jun 2026 08:16:19 +0200 Subject: [PATCH 12/42] feat(markdown): compact `| - |` table format to match the groundtruth corpus MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The committed groundtruth corpus for every format (HTML/CSV/LaTeX/PDF/…) uses docling-core's older minimal table serializer — `| a | b |` cells, single-dash `| - | - |` separators, no width padding — while render_table replicated current docling-core's *padded* GitHub tables, diverging from the conformance reference on every table-bearing fixture. Switch render_table to the compact format so the library matches the committed fixtures byte-for-byte. This is a prerequisite for the PDF TableFormer work to produce conformant tables, and it realigns HTML/CSV/LaTeX tables with their groundtruth too. Regenerated 120 `expected/` regression fixtures (regression green) and updated the three inline backend table tests and MIGRATION.md §4. Co-Authored-By: Claude Opus 4.8 (1M context) --- MIGRATION.md | 12 +- crates/fleischwolf-core/src/markdown.rs | 57 +--- crates/fleischwolf/src/backend/csv.rs | 2 +- crates/fleischwolf/src/backend/html.rs | 2 +- crates/fleischwolf/src/backend/markdown.rs | 2 +- .../asciidoc/expected/asciidoc_01.asciidoc.md | 8 +- .../expected/asciidoc_01.asciidoc.strict.md | 8 +- .../asciidoc/expected/asciidoc_02.asciidoc.md | 60 ++-- .../expected/asciidoc_02.asciidoc.strict.md | 60 ++-- .../asciidoc/expected/asciidoc_04.asciidoc.md | 18 +- .../expected/asciidoc_04.asciidoc.strict.md | 18 +- .../csv/expected/csv-comma-in-cell.csv.md | 12 +- .../expected/csv-comma-in-cell.csv.strict.md | 12 +- .../tests/data/csv/expected/csv-comma.csv.md | 14 +- .../data/csv/expected/csv-comma.csv.strict.md | 14 +- .../expected/csv-inconsistent-header.csv.md | 12 +- .../csv-inconsistent-header.csv.strict.md | 12 +- .../tests/data/csv/expected/csv-pipe.csv.md | 14 +- .../data/csv/expected/csv-pipe.csv.strict.md | 14 +- .../data/csv/expected/csv-semicolon.csv.md | 14 +- .../csv/expected/csv-semicolon.csv.strict.md | 14 +- .../csv/expected/csv-single-column.csv.md | 12 +- .../expected/csv-single-column.csv.strict.md | 12 +- .../tests/data/csv/expected/csv-tab.csv.md | 14 +- .../data/csv/expected/csv-tab.csv.strict.md | 14 +- .../csv/expected/csv-too-few-columns.csv.md | 12 +- .../csv-too-few-columns.csv.strict.md | 12 +- .../csv/expected/csv-too-many-columns.csv.md | 12 +- .../csv-too-many-columns.csv.strict.md | 12 +- .../docx/expected/docx_checkboxes.docx.md | 8 +- .../expected/docx_checkboxes.docx.strict.md | 8 +- .../docx/expected/docx_rich_cells.docx.md | 46 +-- .../expected/docx_rich_cells.docx.strict.md | 46 +-- .../docx/expected/docx_rich_tables_01.docx.md | 100 +++--- .../docx_rich_tables_01.docx.strict.md | 100 +++--- .../expected/table_with_equations.docx.md | 4 +- .../table_with_equations.docx.strict.md | 4 +- .../data/docx/expected/tablecell.docx.md | 8 +- .../docx/expected/tablecell.docx.strict.md | 8 +- .../data/docx/expected/word_sample.docx.md | 10 +- .../docx/expected/word_sample.docx.strict.md | 10 +- .../data/docx/expected/word_tables.docx.md | 56 +-- .../docx/expected/word_tables.docx.strict.md | 56 +-- .../data/html/expected/example_03.html.md | 4 +- .../html/expected/example_03.html.strict.md | 4 +- .../data/html/expected/example_04.html.md | 8 +- .../html/expected/example_04.html.strict.md | 8 +- .../data/html/expected/example_05.html.md | 8 +- .../html/expected/example_05.html.strict.md | 8 +- .../data/html/expected/example_08.html.md | 42 +-- .../html/expected/example_08.html.strict.md | 42 +-- .../html/expected/html_heading_in_p.html.md | 14 +- .../expected/html_heading_in_p.html.strict.md | 14 +- .../html_inline_group_in_table_cell.html.md | 16 +- ..._inline_group_in_table_cell.html.strict.md | 16 +- .../expected/html_rich_table_cells.html.md | 44 +-- .../html_rich_table_cells.html.strict.md | 44 +-- .../html/expected/kvp_data_example.html.md | 10 +- .../expected/kvp_data_example.html.strict.md | 10 +- .../tests/data/html/expected/table_01.html.md | 4 +- .../html/expected/table_01.html.strict.md | 4 +- .../tests/data/html/expected/table_02.html.md | 4 +- .../html/expected/table_02.html.strict.md | 4 +- .../tests/data/html/expected/table_03.html.md | 4 +- .../html/expected/table_03.html.strict.md | 4 +- .../tests/data/html/expected/table_04.html.md | 4 +- .../html/expected/table_04.html.strict.md | 4 +- .../tests/data/html/expected/table_05.html.md | 4 +- .../html/expected/table_05.html.strict.md | 4 +- .../tests/data/html/expected/table_06.html.md | 4 +- .../html/expected/table_06.html.strict.md | 4 +- .../expected/table_with_heading_01.html.md | 4 +- .../table_with_heading_01.html.strict.md | 4 +- .../expected/table_with_heading_02.html.md | 6 +- .../table_with_heading_02.html.strict.md | 6 +- .../data/html/expected/wiki_duck.html.md | 34 +- .../html/expected/wiki_duck.html.strict.md | 34 +- .../expected/csv-comma.csv.json.md | 14 +- .../expected/csv-comma.csv.json.strict.md | 14 +- .../data/latex/expected/example_02.tex.md | 8 +- .../latex/expected/example_02.tex.strict.md | 8 +- .../tests/data/md/expected/duck.md.md | 10 +- .../tests/data/md/expected/duck.md.strict.md | 10 +- .../data/md/expected/ending_with_table.md.md | 12 +- .../expected/ending_with_table.md.strict.md | 12 +- .../data/md/expected/escaped_characters.md.md | 16 +- .../expected/escaped_characters.md.strict.md | 16 +- .../md/expected/inline_and_formatting.md.md | 6 +- .../inline_and_formatting.md.strict.md | 6 +- .../tests/data/md/expected/mixed.md.md | 12 +- .../tests/data/md/expected/mixed.md.strict.md | 12 +- .../expected/deepseek_example.md.md | 22 +- .../expected/deepseek_example.md.strict.md | 22 +- .../odf/expected/odf_presentation_02.odp.md | 10 +- .../odf_presentation_02.odp.strict.md | 10 +- .../expected/odf_table_with_title_01.ods.md | 18 +- .../odf_table_with_title_01.ods.strict.md | 18 +- .../data/odf/expected/text_document_02.odt.md | 6 +- .../expected/text_document_02.odt.strict.md | 6 +- .../data/odf/expected/text_document_03.odt.md | 36 +- .../expected/text_document_03.odt.strict.md | 36 +- .../pptx/expected/powerpoint_sample.pptx.md | 20 +- .../expected/powerpoint_sample.pptx.strict.md | 20 +- .../data/xbrl/expected/grve_10q_htm.xml.md | 22 +- .../xbrl/expected/grve_10q_htm.xml.strict.md | 22 +- .../data/xbrl/expected/mlac-20251231.xml.md | 322 +++++++++--------- .../xbrl/expected/mlac-20251231.xml.strict.md | 322 +++++++++--------- .../tests/data/xlsx/expected/xlsx_01.xlsx.md | 92 ++--- .../data/xlsx/expected/xlsx_01.xlsx.strict.md | 92 ++--- .../xlsx_02_sample_sales_data.xlsm.md | 44 +-- .../xlsx_02_sample_sales_data.xlsm.strict.md | 44 +-- .../xlsx/expected/xlsx_03_chartsheet.xlsx.md | 16 +- .../xlsx_03_chartsheet.xlsx.strict.md | 16 +- .../xlsx/expected/xlsx_04_inflated.xlsx.md | 92 ++--- .../expected/xlsx_04_inflated.xlsx.strict.md | 92 ++--- .../expected/xlsx_05_table_with_title.xlsx.md | 20 +- .../xlsx_05_table_with_title.xlsx.strict.md | 20 +- .../xlsx/expected/xlsx_06_edge_cases_.xlsx.md | 32 +- .../xlsx_06_edge_cases_.xlsx.strict.md | 32 +- .../expected/xlsx_07_gap_tolerance_.xlsx.md | 142 ++++---- .../xlsx_07_gap_tolerance_.xlsx.strict.md | 142 ++++---- .../expected/xlsx_08_one_cell_anchor.xlsx.md | 8 +- .../xlsx_08_one_cell_anchor.xlsx.strict.md | 8 +- .../data/xlsx/expected/xlsx_comments.xlsx.md | 24 +- .../expected/xlsx_comments.xlsx.strict.md | 24 +- 125 files changed, 1672 insertions(+), 1707 deletions(-) diff --git a/MIGRATION.md b/MIGRATION.md index 83c959bc..2030ea2b 100644 --- a/MIGRATION.md +++ b/MIGRATION.md @@ -135,11 +135,13 @@ These are deliberate or unavoidable divergences, not bugs. entity re-escaping); `strict` produces cleaner Markdown. docling has no such switch. All conformance numbers are measured in **legacy** mode. -4. **Tables match *current* docling, not the committed fixtures.** docling-core's - Markdown table serializer emits padded GitHub tables today; the repo's - committed groundtruth `.md` corpus predates that and uses a minimal `| - |` - format. `fleischwolf` matches the **current/live** output — so table-bearing - formats look correct against live docling and "wrong" against the stale `.md`. +4. **Tables use the committed groundtruth's compact `| - |` format.** The whole + committed groundtruth corpus (HTML, CSV, LaTeX, PDF, …) uses docling-core's + older minimal serializer (`| a | b |` cells, single-dash `| - | - |` + separators, no width padding). `fleischwolf` now emits that exact format so it + matches the conformance reference byte-for-byte, which is required for the PDF + table work (TableFormer) to land conformant output. (Current docling-core emits + *padded* GitHub tables; we deliberately target the committed fixtures instead.) 5. **The PDF pipeline is discriminative and partial.** Ported from docling's standard pipeline, with substitutions: diff --git a/crates/fleischwolf-core/src/markdown.rs b/crates/fleischwolf-core/src/markdown.rs index 8678dca1..d007b204 100644 --- a/crates/fleischwolf-core/src/markdown.rs +++ b/crates/fleischwolf-core/src/markdown.rs @@ -260,51 +260,15 @@ fn render_table(table: &Table) -> String { }) .collect(); - // Display width (Unicode scalar count — good enough for now). - let dw = |s: &str| s.chars().count(); - let data_rows = 1..grid.len(); - - // A column is right-aligned when it has data and every data cell is numeric. - let right: Vec = (0..num_cols) - .map(|c| { - !data_rows.is_empty() - && data_rows.clone().all(|r| { - let t = grid[r][c].trim(); - !t.is_empty() && t.parse::().is_ok() - }) - }) - .collect(); - - // Column width = max(header_width + MIN_PADDING(2), max data-cell width). - let width: Vec = (0..num_cols) - .map(|c| { - let mut w = dw(&grid[0][c]) + 2; - for r in data_rows.clone() { - w = w.max(dw(&grid[r][c])); - } - w - }) - .collect(); - - let fmt_cell = |s: &str, c: usize| -> String { - let pad = " ".repeat(width[c].saturating_sub(dw(s))); - let body = if right[c] { - format!("{pad}{s}") - } else { - format!("{s}{pad}") - }; - format!(" {body} ") - }; - let render_row = |r: usize| -> String { - let cells: Vec = (0..num_cols).map(|c| fmt_cell(&grid[r][c], c)).collect(); - format!("|{}|", cells.join("|")) - }; - + // Compact format, matching the committed groundtruth corpus (which predates + // docling-core's current padded GitHub serializer): cells joined by " | ", + // no width padding, single-dash separators (`| - | - |`). + let render_row = |r: usize| -> String { format!("| {} |", grid[r].join(" | ")) }; let mut lines = Vec::with_capacity(grid.len() + 1); lines.push(render_row(0)); - let sep: Vec = (0..num_cols).map(|c| "-".repeat(width[c] + 2)).collect(); - lines.push(format!("|{}|", sep.join("|"))); - for r in data_rows { + let sep: Vec<&str> = (0..num_cols).map(|_| "-").collect(); + lines.push(format!("| {} |", sep.join(" | "))); + for r in 1..grid.len() { lines.push(render_row(r)); } lines.join("\n") @@ -344,15 +308,14 @@ mod tests { } #[test] - fn renders_github_table() { + fn renders_compact_table() { let mut doc = DoclingDocument::new("t"); doc.push(Node::Table(Table { rows: vec![vec!["a".into(), "b".into()], vec!["1".into(), "2".into()]], })); let md = doc.export_to_markdown(); - // Matches tabulate(tablefmt="github"): padded columns, numeric cells - // right-aligned, separator of width+2 dashes. - assert_eq!(md, "| a | b |\n|-----|-----|\n| 1 | 2 |\n"); + // Compact format matching the committed groundtruth corpus. + assert_eq!(md, "| a | b |\n| - | - |\n| 1 | 2 |\n"); } #[test] diff --git a/crates/fleischwolf/src/backend/csv.rs b/crates/fleischwolf/src/backend/csv.rs index 4ba4f665..084aefdc 100644 --- a/crates/fleischwolf/src/backend/csv.rs +++ b/crates/fleischwolf/src/backend/csv.rs @@ -76,7 +76,7 @@ mod tests { let doc = convert(b"name,age\nAlice,30\nBob,25\n"); assert_eq!( doc.export_to_markdown(), - "| name | age |\n|--------|-------|\n| Alice | 30 |\n| Bob | 25 |\n" + "| name | age |\n| - | - |\n| Alice | 30 |\n| Bob | 25 |\n" ); } diff --git a/crates/fleischwolf/src/backend/html.rs b/crates/fleischwolf/src/backend/html.rs index 033fe42e..b73bc44c 100644 --- a/crates/fleischwolf/src/backend/html.rs +++ b/crates/fleischwolf/src/backend/html.rs @@ -970,7 +970,7 @@ mod tests { ); assert_eq!( doc.export_to_markdown(), - "| Name | Age |\n|--------|-------|\n| Ada | 36 |\n" + "| Name | Age |\n| - | - |\n| Ada | 36 |\n" ); } diff --git a/crates/fleischwolf/src/backend/markdown.rs b/crates/fleischwolf/src/backend/markdown.rs index cccaaeb0..4a0804b1 100644 --- a/crates/fleischwolf/src/backend/markdown.rs +++ b/crates/fleischwolf/src/backend/markdown.rs @@ -639,7 +639,7 @@ mod tests { let doc = convert("| **A** | B |\n|---|---|\n| x | y |\n"); assert_eq!( doc.export_to_markdown(), - "| A | B |\n|-----|-----|\n| x | y |\n" + "| A | B |\n| - | - |\n| x | y |\n" ); } diff --git a/crates/fleischwolf/tests/data/asciidoc/expected/asciidoc_01.asciidoc.md b/crates/fleischwolf/tests/data/asciidoc/expected/asciidoc_01.asciidoc.md index 691ecc0f..d3403feb 100644 --- a/crates/fleischwolf/tests/data/asciidoc/expected/asciidoc_01.asciidoc.md +++ b/crates/fleischwolf/tests/data/asciidoc/expected/asciidoc_01.asciidoc.md @@ -21,7 +21,7 @@ This is some introductory text in section 1.1. This is some text in section 2. -| Header 1 | Header 2 | | -|------------|------------|----| -| Value 1 | Value 2 | | -| Value 3 | Value 4 | | +| Header 1 | Header 2 | | +| - | - | - | +| Value 1 | Value 2 | | +| Value 3 | Value 4 | | diff --git a/crates/fleischwolf/tests/data/asciidoc/expected/asciidoc_01.asciidoc.strict.md b/crates/fleischwolf/tests/data/asciidoc/expected/asciidoc_01.asciidoc.strict.md index 691ecc0f..d3403feb 100644 --- a/crates/fleischwolf/tests/data/asciidoc/expected/asciidoc_01.asciidoc.strict.md +++ b/crates/fleischwolf/tests/data/asciidoc/expected/asciidoc_01.asciidoc.strict.md @@ -21,7 +21,7 @@ This is some introductory text in section 1.1. This is some text in section 2. -| Header 1 | Header 2 | | -|------------|------------|----| -| Value 1 | Value 2 | | -| Value 3 | Value 4 | | +| Header 1 | Header 2 | | +| - | - | - | +| Value 1 | Value 2 | | +| Value 3 | Value 4 | | diff --git a/crates/fleischwolf/tests/data/asciidoc/expected/asciidoc_02.asciidoc.md b/crates/fleischwolf/tests/data/asciidoc/expected/asciidoc_02.asciidoc.md index bbe03537..352634a0 100644 --- a/crates/fleischwolf/tests/data/asciidoc/expected/asciidoc_02.asciidoc.md +++ b/crates/fleischwolf/tests/data/asciidoc/expected/asciidoc_02.asciidoc.md @@ -27,51 +27,51 @@ bla bla bla bli bla ble ## Section 4: test tables -| Header 1 | Header 2 | | -|------------|------------|----| -| Value 1 | Value 2 | | -| Value 3 | Value 4 | | +| Header 1 | Header 2 | | +| - | - | - | +| Value 1 | Value 2 | | +| Value 3 | Value 4 | | Caption for the table 1 -| Header 1 | Header 2 | -|------------|------------| -| Value 1 | Value 2 | -| Value 3 | Value 4 | +| Header 1 | Header 2 | +| - | - | +| Value 1 | Value 2 | +| Value 3 | Value 4 | Caption for the table 2 -| Column 1 Heading | Column 2 Heading | Column 3 Heading | -|--------------------|--------------------|------------------------| -| Cell 1 | Cell 2 | Cell 3 | -| Cell 4 | Cell 5 colspan=2 | Cell spans two columns | +| Column 1 Heading | Column 2 Heading | Column 3 Heading | +| - | - | - | +| Cell 1 | Cell 2 | Cell 3 | +| Cell 4 | Cell 5 colspan=2 | Cell spans two columns | Caption for the table 3 -| Column 1 Heading | Column 2 Heading | Column 3 Heading | -|--------------------|--------------------|--------------------| -| Rowspan=2 | Cell 2 | Cell 3 | -| | Cell 5 | Cell 6 | +| Column 1 Heading | Column 2 Heading | Column 3 Heading | +| - | - | - | +| Rowspan=2 | Cell 2 | Cell 3 | +| | Cell 5 | Cell 6 | Caption for the table 4 -| Col 1 | Col 2 | Col 3 | Col 4 | -|---------------------|------------------------------------|---------|---------| -| Rowspan=2.Colspan=2 | Cell spanning 2 rows and 2 columns | Col 3 | Col 4 | -| | | Col 3 | Col 4 | -| Col 1 | Col 2 | Col 3 | Col 4 | +| Col 1 | Col 2 | Col 3 | Col 4 | +| - | - | - | - | +| Rowspan=2.Colspan=2 | Cell spanning 2 rows and 2 columns | Col 3 | Col 4 | +| | | Col 3 | Col 4 | +| Col 1 | Col 2 | Col 3 | Col 4 | Table 5 with multiple empty cells -| Column 1 Heading | Column 2 Heading | Column 3 Heading | | -|--------------------|--------------------|--------------------|----| -| Cell 1 | | Cell 3 | | -| Cell 4 | | | | -| Cell 7 | | | | -| Cell 10 | Cell 11 | Cell 12 | | -| | Cell 14 | Cell 15 | | -| | | | | -| Cell 19 | Cell 20 | Cell 21 | | +| Column 1 Heading | Column 2 Heading | Column 3 Heading | | +| - | - | - | - | +| Cell 1 | | Cell 3 | | +| Cell 4 | | | | +| Cell 7 | | | | +| Cell 10 | Cell 11 | Cell 12 | | +| | Cell 14 | Cell 15 | | +| | | | | +| Cell 19 | Cell 20 | Cell 21 | | #### SubSubSection 2.1.1 diff --git a/crates/fleischwolf/tests/data/asciidoc/expected/asciidoc_02.asciidoc.strict.md b/crates/fleischwolf/tests/data/asciidoc/expected/asciidoc_02.asciidoc.strict.md index bbe03537..352634a0 100644 --- a/crates/fleischwolf/tests/data/asciidoc/expected/asciidoc_02.asciidoc.strict.md +++ b/crates/fleischwolf/tests/data/asciidoc/expected/asciidoc_02.asciidoc.strict.md @@ -27,51 +27,51 @@ bla bla bla bli bla ble ## Section 4: test tables -| Header 1 | Header 2 | | -|------------|------------|----| -| Value 1 | Value 2 | | -| Value 3 | Value 4 | | +| Header 1 | Header 2 | | +| - | - | - | +| Value 1 | Value 2 | | +| Value 3 | Value 4 | | Caption for the table 1 -| Header 1 | Header 2 | -|------------|------------| -| Value 1 | Value 2 | -| Value 3 | Value 4 | +| Header 1 | Header 2 | +| - | - | +| Value 1 | Value 2 | +| Value 3 | Value 4 | Caption for the table 2 -| Column 1 Heading | Column 2 Heading | Column 3 Heading | -|--------------------|--------------------|------------------------| -| Cell 1 | Cell 2 | Cell 3 | -| Cell 4 | Cell 5 colspan=2 | Cell spans two columns | +| Column 1 Heading | Column 2 Heading | Column 3 Heading | +| - | - | - | +| Cell 1 | Cell 2 | Cell 3 | +| Cell 4 | Cell 5 colspan=2 | Cell spans two columns | Caption for the table 3 -| Column 1 Heading | Column 2 Heading | Column 3 Heading | -|--------------------|--------------------|--------------------| -| Rowspan=2 | Cell 2 | Cell 3 | -| | Cell 5 | Cell 6 | +| Column 1 Heading | Column 2 Heading | Column 3 Heading | +| - | - | - | +| Rowspan=2 | Cell 2 | Cell 3 | +| | Cell 5 | Cell 6 | Caption for the table 4 -| Col 1 | Col 2 | Col 3 | Col 4 | -|---------------------|------------------------------------|---------|---------| -| Rowspan=2.Colspan=2 | Cell spanning 2 rows and 2 columns | Col 3 | Col 4 | -| | | Col 3 | Col 4 | -| Col 1 | Col 2 | Col 3 | Col 4 | +| Col 1 | Col 2 | Col 3 | Col 4 | +| - | - | - | - | +| Rowspan=2.Colspan=2 | Cell spanning 2 rows and 2 columns | Col 3 | Col 4 | +| | | Col 3 | Col 4 | +| Col 1 | Col 2 | Col 3 | Col 4 | Table 5 with multiple empty cells -| Column 1 Heading | Column 2 Heading | Column 3 Heading | | -|--------------------|--------------------|--------------------|----| -| Cell 1 | | Cell 3 | | -| Cell 4 | | | | -| Cell 7 | | | | -| Cell 10 | Cell 11 | Cell 12 | | -| | Cell 14 | Cell 15 | | -| | | | | -| Cell 19 | Cell 20 | Cell 21 | | +| Column 1 Heading | Column 2 Heading | Column 3 Heading | | +| - | - | - | - | +| Cell 1 | | Cell 3 | | +| Cell 4 | | | | +| Cell 7 | | | | +| Cell 10 | Cell 11 | Cell 12 | | +| | Cell 14 | Cell 15 | | +| | | | | +| Cell 19 | Cell 20 | Cell 21 | | #### SubSubSection 2.1.1 diff --git a/crates/fleischwolf/tests/data/asciidoc/expected/asciidoc_04.asciidoc.md b/crates/fleischwolf/tests/data/asciidoc/expected/asciidoc_04.asciidoc.md index 7b770137..a11ba396 100644 --- a/crates/fleischwolf/tests/data/asciidoc/expected/asciidoc_04.asciidoc.md +++ b/crates/fleischwolf/tests/data/asciidoc/expected/asciidoc_04.asciidoc.md @@ -2,17 +2,17 @@ A table whose header row uses alignment and style specifiers on every cell. -| Field | Description | -|---------|---------------------| -| a | First column value | -| b | Second column value | +| Field | Description | +| - | - | +| a | First column value | +| b | Second column value | [%autowidth, cols="^.^40,<.^60"] A table with single-letter cells that collide with style operators. -| Code | Name | -|--------|-----------| -| s | Strong | -| h | Header | -| m | Monospace | +| Code | Name | +| - | - | +| s | Strong | +| h | Header | +| m | Monospace | diff --git a/crates/fleischwolf/tests/data/asciidoc/expected/asciidoc_04.asciidoc.strict.md b/crates/fleischwolf/tests/data/asciidoc/expected/asciidoc_04.asciidoc.strict.md index 7b770137..a11ba396 100644 --- a/crates/fleischwolf/tests/data/asciidoc/expected/asciidoc_04.asciidoc.strict.md +++ b/crates/fleischwolf/tests/data/asciidoc/expected/asciidoc_04.asciidoc.strict.md @@ -2,17 +2,17 @@ A table whose header row uses alignment and style specifiers on every cell. -| Field | Description | -|---------|---------------------| -| a | First column value | -| b | Second column value | +| Field | Description | +| - | - | +| a | First column value | +| b | Second column value | [%autowidth, cols="^.^40,<.^60"] A table with single-letter cells that collide with style operators. -| Code | Name | -|--------|-----------| -| s | Strong | -| h | Header | -| m | Monospace | +| Code | Name | +| - | - | +| s | Strong | +| h | Header | +| m | Monospace | diff --git a/crates/fleischwolf/tests/data/csv/expected/csv-comma-in-cell.csv.md b/crates/fleischwolf/tests/data/csv/expected/csv-comma-in-cell.csv.md index d21c9f84..d31acf48 100644 --- a/crates/fleischwolf/tests/data/csv/expected/csv-comma-in-cell.csv.md +++ b/crates/fleischwolf/tests/data/csv/expected/csv-comma-in-cell.csv.md @@ -1,6 +1,6 @@ -| 1 | 2 | 3 | 4 | -|-----|-----|-----|-----| -| a | b | c | d | -| a | , | c | d | -| a | b | c | d | -| a | b | c | d | +| 1 | 2 | 3 | 4 | +| - | - | - | - | +| a | b | c | d | +| a | , | c | d | +| a | b | c | d | +| a | b | c | d | diff --git a/crates/fleischwolf/tests/data/csv/expected/csv-comma-in-cell.csv.strict.md b/crates/fleischwolf/tests/data/csv/expected/csv-comma-in-cell.csv.strict.md index d21c9f84..d31acf48 100644 --- a/crates/fleischwolf/tests/data/csv/expected/csv-comma-in-cell.csv.strict.md +++ b/crates/fleischwolf/tests/data/csv/expected/csv-comma-in-cell.csv.strict.md @@ -1,6 +1,6 @@ -| 1 | 2 | 3 | 4 | -|-----|-----|-----|-----| -| a | b | c | d | -| a | , | c | d | -| a | b | c | d | -| a | b | c | d | +| 1 | 2 | 3 | 4 | +| - | - | - | - | +| a | b | c | d | +| a | , | c | d | +| a | b | c | d | +| a | b | c | d | diff --git a/crates/fleischwolf/tests/data/csv/expected/csv-comma.csv.md b/crates/fleischwolf/tests/data/csv/expected/csv-comma.csv.md index bf37c6e7..e7d4dfa3 100644 --- a/crates/fleischwolf/tests/data/csv/expected/csv-comma.csv.md +++ b/crates/fleischwolf/tests/data/csv/expected/csv-comma.csv.md @@ -1,7 +1,7 @@ -| Index | Customer Id | First Name | Last Name | Company | City | Country | Phone 1 | Phone 2 | Email | Subscription Date | Website | -|---------|-----------------|--------------|-------------|---------------------------------|-------------------|----------------------------|------------------------|-----------------------|-----------------------------|---------------------|-----------------------------| -| 1 | DD37Cf93aecA6Dc | Sheryl | Baxter | Rasmussen Group | East Leonard | Chile | 229.077.5154 | 397.884.0519x718 | zunigavanessa@smith.info | 2020-08-24 | http://www.stephenson.com/ | -| 2 | 1Ef7b82A4CAAD10 | Preston | Lozano, Dr | Vega-Gentry | East Jimmychester | Djibouti | 5153435776 | 686-620-1820x944 | vmata@colon.com | 2021-04-23 | http://www.hobbs.com/ | -| 3 | 6F94879bDAfE5a6 | Roy | Berry | Murillo-Perry | Isabelborough | Antigua and Barbuda | +1-539-402-0259 | (496)978-3969x58947 | beckycarr@hogan.com | 2020-03-25 | http://www.lawrence.com/ | -| 4 | 5Cef8BFA16c5e3c | Linda | Olsen | Dominguez, Mcmillan and Donovan | Bensonview | Dominican Republic | 001-808-617-6467x12895 | +1-813-324-8756 | stanleyblackwell@benson.org | 2020-06-02 | http://www.good-lyons.com/ | -| 5 | 053d585Ab6b3159 | Joanna | Bender | Martin, Lang and Andrade | West Priscilla | Slovakia (Slovak Republic) | 001-234-203-0635x76146 | 001-199-446-3860x3486 | colinalvarado@miles.net | 2021-04-17 | https://goodwin-ingram.com/ | +| Index | Customer Id | First Name | Last Name | Company | City | Country | Phone 1 | Phone 2 | Email | Subscription Date | Website | +| - | - | - | - | - | - | - | - | - | - | - | - | +| 1 | DD37Cf93aecA6Dc | Sheryl | Baxter | Rasmussen Group | East Leonard | Chile | 229.077.5154 | 397.884.0519x718 | zunigavanessa@smith.info | 2020-08-24 | http://www.stephenson.com/ | +| 2 | 1Ef7b82A4CAAD10 | Preston | Lozano, Dr | Vega-Gentry | East Jimmychester | Djibouti | 5153435776 | 686-620-1820x944 | vmata@colon.com | 2021-04-23 | http://www.hobbs.com/ | +| 3 | 6F94879bDAfE5a6 | Roy | Berry | Murillo-Perry | Isabelborough | Antigua and Barbuda | +1-539-402-0259 | (496)978-3969x58947 | beckycarr@hogan.com | 2020-03-25 | http://www.lawrence.com/ | +| 4 | 5Cef8BFA16c5e3c | Linda | Olsen | Dominguez, Mcmillan and Donovan | Bensonview | Dominican Republic | 001-808-617-6467x12895 | +1-813-324-8756 | stanleyblackwell@benson.org | 2020-06-02 | http://www.good-lyons.com/ | +| 5 | 053d585Ab6b3159 | Joanna | Bender | Martin, Lang and Andrade | West Priscilla | Slovakia (Slovak Republic) | 001-234-203-0635x76146 | 001-199-446-3860x3486 | colinalvarado@miles.net | 2021-04-17 | https://goodwin-ingram.com/ | diff --git a/crates/fleischwolf/tests/data/csv/expected/csv-comma.csv.strict.md b/crates/fleischwolf/tests/data/csv/expected/csv-comma.csv.strict.md index bf37c6e7..e7d4dfa3 100644 --- a/crates/fleischwolf/tests/data/csv/expected/csv-comma.csv.strict.md +++ b/crates/fleischwolf/tests/data/csv/expected/csv-comma.csv.strict.md @@ -1,7 +1,7 @@ -| Index | Customer Id | First Name | Last Name | Company | City | Country | Phone 1 | Phone 2 | Email | Subscription Date | Website | -|---------|-----------------|--------------|-------------|---------------------------------|-------------------|----------------------------|------------------------|-----------------------|-----------------------------|---------------------|-----------------------------| -| 1 | DD37Cf93aecA6Dc | Sheryl | Baxter | Rasmussen Group | East Leonard | Chile | 229.077.5154 | 397.884.0519x718 | zunigavanessa@smith.info | 2020-08-24 | http://www.stephenson.com/ | -| 2 | 1Ef7b82A4CAAD10 | Preston | Lozano, Dr | Vega-Gentry | East Jimmychester | Djibouti | 5153435776 | 686-620-1820x944 | vmata@colon.com | 2021-04-23 | http://www.hobbs.com/ | -| 3 | 6F94879bDAfE5a6 | Roy | Berry | Murillo-Perry | Isabelborough | Antigua and Barbuda | +1-539-402-0259 | (496)978-3969x58947 | beckycarr@hogan.com | 2020-03-25 | http://www.lawrence.com/ | -| 4 | 5Cef8BFA16c5e3c | Linda | Olsen | Dominguez, Mcmillan and Donovan | Bensonview | Dominican Republic | 001-808-617-6467x12895 | +1-813-324-8756 | stanleyblackwell@benson.org | 2020-06-02 | http://www.good-lyons.com/ | -| 5 | 053d585Ab6b3159 | Joanna | Bender | Martin, Lang and Andrade | West Priscilla | Slovakia (Slovak Republic) | 001-234-203-0635x76146 | 001-199-446-3860x3486 | colinalvarado@miles.net | 2021-04-17 | https://goodwin-ingram.com/ | +| Index | Customer Id | First Name | Last Name | Company | City | Country | Phone 1 | Phone 2 | Email | Subscription Date | Website | +| - | - | - | - | - | - | - | - | - | - | - | - | +| 1 | DD37Cf93aecA6Dc | Sheryl | Baxter | Rasmussen Group | East Leonard | Chile | 229.077.5154 | 397.884.0519x718 | zunigavanessa@smith.info | 2020-08-24 | http://www.stephenson.com/ | +| 2 | 1Ef7b82A4CAAD10 | Preston | Lozano, Dr | Vega-Gentry | East Jimmychester | Djibouti | 5153435776 | 686-620-1820x944 | vmata@colon.com | 2021-04-23 | http://www.hobbs.com/ | +| 3 | 6F94879bDAfE5a6 | Roy | Berry | Murillo-Perry | Isabelborough | Antigua and Barbuda | +1-539-402-0259 | (496)978-3969x58947 | beckycarr@hogan.com | 2020-03-25 | http://www.lawrence.com/ | +| 4 | 5Cef8BFA16c5e3c | Linda | Olsen | Dominguez, Mcmillan and Donovan | Bensonview | Dominican Republic | 001-808-617-6467x12895 | +1-813-324-8756 | stanleyblackwell@benson.org | 2020-06-02 | http://www.good-lyons.com/ | +| 5 | 053d585Ab6b3159 | Joanna | Bender | Martin, Lang and Andrade | West Priscilla | Slovakia (Slovak Republic) | 001-234-203-0635x76146 | 001-199-446-3860x3486 | colinalvarado@miles.net | 2021-04-17 | https://goodwin-ingram.com/ | diff --git a/crates/fleischwolf/tests/data/csv/expected/csv-inconsistent-header.csv.md b/crates/fleischwolf/tests/data/csv/expected/csv-inconsistent-header.csv.md index 711f900c..da72b244 100644 --- a/crates/fleischwolf/tests/data/csv/expected/csv-inconsistent-header.csv.md +++ b/crates/fleischwolf/tests/data/csv/expected/csv-inconsistent-header.csv.md @@ -1,6 +1,6 @@ -| 1 | 2 | 3 | | -|-----|-----|-----|----| -| a | b | c | d | -| a | b | c | d | -| a | b | c | d | -| a | b | c | d | +| 1 | 2 | 3 | | +| - | - | - | - | +| a | b | c | d | +| a | b | c | d | +| a | b | c | d | +| a | b | c | d | diff --git a/crates/fleischwolf/tests/data/csv/expected/csv-inconsistent-header.csv.strict.md b/crates/fleischwolf/tests/data/csv/expected/csv-inconsistent-header.csv.strict.md index 711f900c..da72b244 100644 --- a/crates/fleischwolf/tests/data/csv/expected/csv-inconsistent-header.csv.strict.md +++ b/crates/fleischwolf/tests/data/csv/expected/csv-inconsistent-header.csv.strict.md @@ -1,6 +1,6 @@ -| 1 | 2 | 3 | | -|-----|-----|-----|----| -| a | b | c | d | -| a | b | c | d | -| a | b | c | d | -| a | b | c | d | +| 1 | 2 | 3 | | +| - | - | - | - | +| a | b | c | d | +| a | b | c | d | +| a | b | c | d | +| a | b | c | d | diff --git a/crates/fleischwolf/tests/data/csv/expected/csv-pipe.csv.md b/crates/fleischwolf/tests/data/csv/expected/csv-pipe.csv.md index 02fd47ea..2db782bb 100644 --- a/crates/fleischwolf/tests/data/csv/expected/csv-pipe.csv.md +++ b/crates/fleischwolf/tests/data/csv/expected/csv-pipe.csv.md @@ -1,7 +1,7 @@ -| Index | Customer Id | First Name | Last Name | Company | City | Country | Phone 1 | Phone 2 | Email | Subscription Date | Website | -|---------|-----------------|--------------|-------------|-------------------------------------|-------------------|----------------------------|------------------------|-----------------------|-----------------------------|---------------------|-----------------------------| -| 1 | DD37Cf93aecA6Dc | Sheryl | Baxter | Rasmussen Group | East Leonard | Chile | 229.077.5154 | 397.884.0519x718 | zunigavanessa@smith.info | 2020-08-24 | http://www.stephenson.com/ | -| 2 | 1Ef7b82A4CAAD10 | Preston | Lozano | Vega-Gentry | East Jimmychester | Djibouti | 5153435776 | 686-620-1820x944 | vmata@colon.com | 2021-04-23 | http://www.hobbs.com/ | -| 3 | 6F94879bDAfE5a6 | Roy | Berry | Murillo-Perry | Isabelborough | Antigua and Barbuda | +1-539-402-0259 | (496)978-3969x58947 | beckycarr@hogan.com | 2020-03-25 | http://www.lawrence.com/ | -| 4 | 5Cef8BFA16c5e3c | Linda | Olsen | Dominguez|Mcmillan and Donovan | Bensonview | Dominican Republic | 001-808-617-6467x12895 | +1-813-324-8756 | stanleyblackwell@benson.org | 2020-06-02 | http://www.good-lyons.com/ | -| 5 | 053d585Ab6b3159 | Joanna | Bender | Martin|Lang and Andrade | West Priscilla | Slovakia (Slovak Republic) | 001-234-203-0635x76146 | 001-199-446-3860x3486 | colinalvarado@miles.net | 2021-04-17 | https://goodwin-ingram.com/ | +| Index | Customer Id | First Name | Last Name | Company | City | Country | Phone 1 | Phone 2 | Email | Subscription Date | Website | +| - | - | - | - | - | - | - | - | - | - | - | - | +| 1 | DD37Cf93aecA6Dc | Sheryl | Baxter | Rasmussen Group | East Leonard | Chile | 229.077.5154 | 397.884.0519x718 | zunigavanessa@smith.info | 2020-08-24 | http://www.stephenson.com/ | +| 2 | 1Ef7b82A4CAAD10 | Preston | Lozano | Vega-Gentry | East Jimmychester | Djibouti | 5153435776 | 686-620-1820x944 | vmata@colon.com | 2021-04-23 | http://www.hobbs.com/ | +| 3 | 6F94879bDAfE5a6 | Roy | Berry | Murillo-Perry | Isabelborough | Antigua and Barbuda | +1-539-402-0259 | (496)978-3969x58947 | beckycarr@hogan.com | 2020-03-25 | http://www.lawrence.com/ | +| 4 | 5Cef8BFA16c5e3c | Linda | Olsen | Dominguez|Mcmillan and Donovan | Bensonview | Dominican Republic | 001-808-617-6467x12895 | +1-813-324-8756 | stanleyblackwell@benson.org | 2020-06-02 | http://www.good-lyons.com/ | +| 5 | 053d585Ab6b3159 | Joanna | Bender | Martin|Lang and Andrade | West Priscilla | Slovakia (Slovak Republic) | 001-234-203-0635x76146 | 001-199-446-3860x3486 | colinalvarado@miles.net | 2021-04-17 | https://goodwin-ingram.com/ | diff --git a/crates/fleischwolf/tests/data/csv/expected/csv-pipe.csv.strict.md b/crates/fleischwolf/tests/data/csv/expected/csv-pipe.csv.strict.md index 02fd47ea..2db782bb 100644 --- a/crates/fleischwolf/tests/data/csv/expected/csv-pipe.csv.strict.md +++ b/crates/fleischwolf/tests/data/csv/expected/csv-pipe.csv.strict.md @@ -1,7 +1,7 @@ -| Index | Customer Id | First Name | Last Name | Company | City | Country | Phone 1 | Phone 2 | Email | Subscription Date | Website | -|---------|-----------------|--------------|-------------|-------------------------------------|-------------------|----------------------------|------------------------|-----------------------|-----------------------------|---------------------|-----------------------------| -| 1 | DD37Cf93aecA6Dc | Sheryl | Baxter | Rasmussen Group | East Leonard | Chile | 229.077.5154 | 397.884.0519x718 | zunigavanessa@smith.info | 2020-08-24 | http://www.stephenson.com/ | -| 2 | 1Ef7b82A4CAAD10 | Preston | Lozano | Vega-Gentry | East Jimmychester | Djibouti | 5153435776 | 686-620-1820x944 | vmata@colon.com | 2021-04-23 | http://www.hobbs.com/ | -| 3 | 6F94879bDAfE5a6 | Roy | Berry | Murillo-Perry | Isabelborough | Antigua and Barbuda | +1-539-402-0259 | (496)978-3969x58947 | beckycarr@hogan.com | 2020-03-25 | http://www.lawrence.com/ | -| 4 | 5Cef8BFA16c5e3c | Linda | Olsen | Dominguez|Mcmillan and Donovan | Bensonview | Dominican Republic | 001-808-617-6467x12895 | +1-813-324-8756 | stanleyblackwell@benson.org | 2020-06-02 | http://www.good-lyons.com/ | -| 5 | 053d585Ab6b3159 | Joanna | Bender | Martin|Lang and Andrade | West Priscilla | Slovakia (Slovak Republic) | 001-234-203-0635x76146 | 001-199-446-3860x3486 | colinalvarado@miles.net | 2021-04-17 | https://goodwin-ingram.com/ | +| Index | Customer Id | First Name | Last Name | Company | City | Country | Phone 1 | Phone 2 | Email | Subscription Date | Website | +| - | - | - | - | - | - | - | - | - | - | - | - | +| 1 | DD37Cf93aecA6Dc | Sheryl | Baxter | Rasmussen Group | East Leonard | Chile | 229.077.5154 | 397.884.0519x718 | zunigavanessa@smith.info | 2020-08-24 | http://www.stephenson.com/ | +| 2 | 1Ef7b82A4CAAD10 | Preston | Lozano | Vega-Gentry | East Jimmychester | Djibouti | 5153435776 | 686-620-1820x944 | vmata@colon.com | 2021-04-23 | http://www.hobbs.com/ | +| 3 | 6F94879bDAfE5a6 | Roy | Berry | Murillo-Perry | Isabelborough | Antigua and Barbuda | +1-539-402-0259 | (496)978-3969x58947 | beckycarr@hogan.com | 2020-03-25 | http://www.lawrence.com/ | +| 4 | 5Cef8BFA16c5e3c | Linda | Olsen | Dominguez|Mcmillan and Donovan | Bensonview | Dominican Republic | 001-808-617-6467x12895 | +1-813-324-8756 | stanleyblackwell@benson.org | 2020-06-02 | http://www.good-lyons.com/ | +| 5 | 053d585Ab6b3159 | Joanna | Bender | Martin|Lang and Andrade | West Priscilla | Slovakia (Slovak Republic) | 001-234-203-0635x76146 | 001-199-446-3860x3486 | colinalvarado@miles.net | 2021-04-17 | https://goodwin-ingram.com/ | diff --git a/crates/fleischwolf/tests/data/csv/expected/csv-semicolon.csv.md b/crates/fleischwolf/tests/data/csv/expected/csv-semicolon.csv.md index f1471593..ca51e6bf 100644 --- a/crates/fleischwolf/tests/data/csv/expected/csv-semicolon.csv.md +++ b/crates/fleischwolf/tests/data/csv/expected/csv-semicolon.csv.md @@ -1,7 +1,7 @@ -| Index | Customer Id | First Name | Last Name | Company | City | Country | Phone 1 | Phone 2 | Email | Subscription Date | Website | -|---------|-----------------|--------------|-------------|--------------------------------|-------------------|----------------------------|------------------------|-----------------------|-----------------------------|---------------------|-----------------------------| -| 1 | DD37Cf93aecA6Dc | Sheryl | Baxter | Rasmussen Group | East Leonard | Chile | 229.077.5154 | 397.884.0519x718 | zunigavanessa@smith.info | 2020-08-24 | http://www.stephenson.com/ | -| 2 | 1Ef7b82A4CAAD10 | Preston | Lozano | Vega-Gentry | East Jimmychester | Djibouti | 5153435776 | 686-620-1820x944 | vmata@colon.com | 2021-04-23 | http://www.hobbs.com/ | -| 3 | 6F94879bDAfE5a6 | Roy | Berry | Murillo-Perry | Isabelborough | Antigua and Barbuda | +1-539-402-0259 | (496)978-3969x58947 | beckycarr@hogan.com | 2020-03-25 | http://www.lawrence.com/ | -| 4 | 5Cef8BFA16c5e3c | Linda | Olsen | Dominguez;Mcmillan and Donovan | Bensonview | Dominican Republic | 001-808-617-6467x12895 | +1-813-324-8756 | stanleyblackwell@benson.org | 2020-06-02 | http://www.good-lyons.com/ | -| 5 | 053d585Ab6b3159 | Joanna | Bender | Martin;Lang and Andrade | West Priscilla | Slovakia (Slovak Republic) | 001-234-203-0635x76146 | 001-199-446-3860x3486 | colinalvarado@miles.net | 2021-04-17 | https://goodwin-ingram.com/ | +| Index | Customer Id | First Name | Last Name | Company | City | Country | Phone 1 | Phone 2 | Email | Subscription Date | Website | +| - | - | - | - | - | - | - | - | - | - | - | - | +| 1 | DD37Cf93aecA6Dc | Sheryl | Baxter | Rasmussen Group | East Leonard | Chile | 229.077.5154 | 397.884.0519x718 | zunigavanessa@smith.info | 2020-08-24 | http://www.stephenson.com/ | +| 2 | 1Ef7b82A4CAAD10 | Preston | Lozano | Vega-Gentry | East Jimmychester | Djibouti | 5153435776 | 686-620-1820x944 | vmata@colon.com | 2021-04-23 | http://www.hobbs.com/ | +| 3 | 6F94879bDAfE5a6 | Roy | Berry | Murillo-Perry | Isabelborough | Antigua and Barbuda | +1-539-402-0259 | (496)978-3969x58947 | beckycarr@hogan.com | 2020-03-25 | http://www.lawrence.com/ | +| 4 | 5Cef8BFA16c5e3c | Linda | Olsen | Dominguez;Mcmillan and Donovan | Bensonview | Dominican Republic | 001-808-617-6467x12895 | +1-813-324-8756 | stanleyblackwell@benson.org | 2020-06-02 | http://www.good-lyons.com/ | +| 5 | 053d585Ab6b3159 | Joanna | Bender | Martin;Lang and Andrade | West Priscilla | Slovakia (Slovak Republic) | 001-234-203-0635x76146 | 001-199-446-3860x3486 | colinalvarado@miles.net | 2021-04-17 | https://goodwin-ingram.com/ | diff --git a/crates/fleischwolf/tests/data/csv/expected/csv-semicolon.csv.strict.md b/crates/fleischwolf/tests/data/csv/expected/csv-semicolon.csv.strict.md index f1471593..ca51e6bf 100644 --- a/crates/fleischwolf/tests/data/csv/expected/csv-semicolon.csv.strict.md +++ b/crates/fleischwolf/tests/data/csv/expected/csv-semicolon.csv.strict.md @@ -1,7 +1,7 @@ -| Index | Customer Id | First Name | Last Name | Company | City | Country | Phone 1 | Phone 2 | Email | Subscription Date | Website | -|---------|-----------------|--------------|-------------|--------------------------------|-------------------|----------------------------|------------------------|-----------------------|-----------------------------|---------------------|-----------------------------| -| 1 | DD37Cf93aecA6Dc | Sheryl | Baxter | Rasmussen Group | East Leonard | Chile | 229.077.5154 | 397.884.0519x718 | zunigavanessa@smith.info | 2020-08-24 | http://www.stephenson.com/ | -| 2 | 1Ef7b82A4CAAD10 | Preston | Lozano | Vega-Gentry | East Jimmychester | Djibouti | 5153435776 | 686-620-1820x944 | vmata@colon.com | 2021-04-23 | http://www.hobbs.com/ | -| 3 | 6F94879bDAfE5a6 | Roy | Berry | Murillo-Perry | Isabelborough | Antigua and Barbuda | +1-539-402-0259 | (496)978-3969x58947 | beckycarr@hogan.com | 2020-03-25 | http://www.lawrence.com/ | -| 4 | 5Cef8BFA16c5e3c | Linda | Olsen | Dominguez;Mcmillan and Donovan | Bensonview | Dominican Republic | 001-808-617-6467x12895 | +1-813-324-8756 | stanleyblackwell@benson.org | 2020-06-02 | http://www.good-lyons.com/ | -| 5 | 053d585Ab6b3159 | Joanna | Bender | Martin;Lang and Andrade | West Priscilla | Slovakia (Slovak Republic) | 001-234-203-0635x76146 | 001-199-446-3860x3486 | colinalvarado@miles.net | 2021-04-17 | https://goodwin-ingram.com/ | +| Index | Customer Id | First Name | Last Name | Company | City | Country | Phone 1 | Phone 2 | Email | Subscription Date | Website | +| - | - | - | - | - | - | - | - | - | - | - | - | +| 1 | DD37Cf93aecA6Dc | Sheryl | Baxter | Rasmussen Group | East Leonard | Chile | 229.077.5154 | 397.884.0519x718 | zunigavanessa@smith.info | 2020-08-24 | http://www.stephenson.com/ | +| 2 | 1Ef7b82A4CAAD10 | Preston | Lozano | Vega-Gentry | East Jimmychester | Djibouti | 5153435776 | 686-620-1820x944 | vmata@colon.com | 2021-04-23 | http://www.hobbs.com/ | +| 3 | 6F94879bDAfE5a6 | Roy | Berry | Murillo-Perry | Isabelborough | Antigua and Barbuda | +1-539-402-0259 | (496)978-3969x58947 | beckycarr@hogan.com | 2020-03-25 | http://www.lawrence.com/ | +| 4 | 5Cef8BFA16c5e3c | Linda | Olsen | Dominguez;Mcmillan and Donovan | Bensonview | Dominican Republic | 001-808-617-6467x12895 | +1-813-324-8756 | stanleyblackwell@benson.org | 2020-06-02 | http://www.good-lyons.com/ | +| 5 | 053d585Ab6b3159 | Joanna | Bender | Martin;Lang and Andrade | West Priscilla | Slovakia (Slovak Republic) | 001-234-203-0635x76146 | 001-199-446-3860x3486 | colinalvarado@miles.net | 2021-04-17 | https://goodwin-ingram.com/ | diff --git a/crates/fleischwolf/tests/data/csv/expected/csv-single-column.csv.md b/crates/fleischwolf/tests/data/csv/expected/csv-single-column.csv.md index 131363aa..1e329f7c 100644 --- a/crates/fleischwolf/tests/data/csv/expected/csv-single-column.csv.md +++ b/crates/fleischwolf/tests/data/csv/expected/csv-single-column.csv.md @@ -1,8 +1,8 @@ -| Industry | -|--------------------| +| Industry | +| - | | Accounting/Finance | -| Automotive | -| Healthcare | +| Automotive | +| Healthcare | | Hospitality/Travel | -| Sales | -| Other | +| Sales | +| Other | diff --git a/crates/fleischwolf/tests/data/csv/expected/csv-single-column.csv.strict.md b/crates/fleischwolf/tests/data/csv/expected/csv-single-column.csv.strict.md index 131363aa..1e329f7c 100644 --- a/crates/fleischwolf/tests/data/csv/expected/csv-single-column.csv.strict.md +++ b/crates/fleischwolf/tests/data/csv/expected/csv-single-column.csv.strict.md @@ -1,8 +1,8 @@ -| Industry | -|--------------------| +| Industry | +| - | | Accounting/Finance | -| Automotive | -| Healthcare | +| Automotive | +| Healthcare | | Hospitality/Travel | -| Sales | -| Other | +| Sales | +| Other | diff --git a/crates/fleischwolf/tests/data/csv/expected/csv-tab.csv.md b/crates/fleischwolf/tests/data/csv/expected/csv-tab.csv.md index 5c8dfe5f..f6c456a2 100644 --- a/crates/fleischwolf/tests/data/csv/expected/csv-tab.csv.md +++ b/crates/fleischwolf/tests/data/csv/expected/csv-tab.csv.md @@ -1,7 +1,7 @@ -| Index | Customer Id | First Name | Last Name | Company | City | Country | Phone 1 | Phone 2 | Email | Subscription Date | Website | -|---------|-----------------|--------------|-------------|--------------------------------|-------------------|----------------------------|------------------------|-----------------------|-----------------------------|---------------------|-----------------------------| -| 1 | DD37Cf93aecA6Dc | Sheryl | Baxter | Rasmussen Group | East Leonard | Chile | 229.077.5154 | 397.884.0519x718 | zunigavanessa@smith.info | 2020-08-24 | http://www.stephenson.com/ | -| 2 | 1Ef7b82A4CAAD10 | Preston | Lozano | Vega-Gentry | East Jimmychester | Djibouti | 5153435776 | 686-620-1820x944 | vmata@colon.com | 2021-04-23 | http://www.hobbs.com/ | -| 3 | 6F94879bDAfE5a6 | Roy | Berry | Murillo-Perry | Isabelborough | Antigua and Barbuda | +1-539-402-0259 | (496)978-3969x58947 | beckycarr@hogan.com | 2020-03-25 | http://www.lawrence.com/ | -| 4 | 5Cef8BFA16c5e3c | Linda | Olsen | Dominguez Mcmillan and Donovan | Bensonview | Dominican Republic | 001-808-617-6467x12895 | +1-813-324-8756 | stanleyblackwell@benson.org | 2020-06-02 | http://www.good-lyons.com/ | -| 5 | 053d585Ab6b3159 | Joanna | Bender | Martin Lang and Andrade | West Priscilla | Slovakia (Slovak Republic) | 001-234-203-0635x76146 | 001-199-446-3860x3486 | colinalvarado@miles.net | 2021-04-17 | https://goodwin-ingram.com/ | +| Index | Customer Id | First Name | Last Name | Company | City | Country | Phone 1 | Phone 2 | Email | Subscription Date | Website | +| - | - | - | - | - | - | - | - | - | - | - | - | +| 1 | DD37Cf93aecA6Dc | Sheryl | Baxter | Rasmussen Group | East Leonard | Chile | 229.077.5154 | 397.884.0519x718 | zunigavanessa@smith.info | 2020-08-24 | http://www.stephenson.com/ | +| 2 | 1Ef7b82A4CAAD10 | Preston | Lozano | Vega-Gentry | East Jimmychester | Djibouti | 5153435776 | 686-620-1820x944 | vmata@colon.com | 2021-04-23 | http://www.hobbs.com/ | +| 3 | 6F94879bDAfE5a6 | Roy | Berry | Murillo-Perry | Isabelborough | Antigua and Barbuda | +1-539-402-0259 | (496)978-3969x58947 | beckycarr@hogan.com | 2020-03-25 | http://www.lawrence.com/ | +| 4 | 5Cef8BFA16c5e3c | Linda | Olsen | Dominguez Mcmillan and Donovan | Bensonview | Dominican Republic | 001-808-617-6467x12895 | +1-813-324-8756 | stanleyblackwell@benson.org | 2020-06-02 | http://www.good-lyons.com/ | +| 5 | 053d585Ab6b3159 | Joanna | Bender | Martin Lang and Andrade | West Priscilla | Slovakia (Slovak Republic) | 001-234-203-0635x76146 | 001-199-446-3860x3486 | colinalvarado@miles.net | 2021-04-17 | https://goodwin-ingram.com/ | diff --git a/crates/fleischwolf/tests/data/csv/expected/csv-tab.csv.strict.md b/crates/fleischwolf/tests/data/csv/expected/csv-tab.csv.strict.md index 5c8dfe5f..f6c456a2 100644 --- a/crates/fleischwolf/tests/data/csv/expected/csv-tab.csv.strict.md +++ b/crates/fleischwolf/tests/data/csv/expected/csv-tab.csv.strict.md @@ -1,7 +1,7 @@ -| Index | Customer Id | First Name | Last Name | Company | City | Country | Phone 1 | Phone 2 | Email | Subscription Date | Website | -|---------|-----------------|--------------|-------------|--------------------------------|-------------------|----------------------------|------------------------|-----------------------|-----------------------------|---------------------|-----------------------------| -| 1 | DD37Cf93aecA6Dc | Sheryl | Baxter | Rasmussen Group | East Leonard | Chile | 229.077.5154 | 397.884.0519x718 | zunigavanessa@smith.info | 2020-08-24 | http://www.stephenson.com/ | -| 2 | 1Ef7b82A4CAAD10 | Preston | Lozano | Vega-Gentry | East Jimmychester | Djibouti | 5153435776 | 686-620-1820x944 | vmata@colon.com | 2021-04-23 | http://www.hobbs.com/ | -| 3 | 6F94879bDAfE5a6 | Roy | Berry | Murillo-Perry | Isabelborough | Antigua and Barbuda | +1-539-402-0259 | (496)978-3969x58947 | beckycarr@hogan.com | 2020-03-25 | http://www.lawrence.com/ | -| 4 | 5Cef8BFA16c5e3c | Linda | Olsen | Dominguez Mcmillan and Donovan | Bensonview | Dominican Republic | 001-808-617-6467x12895 | +1-813-324-8756 | stanleyblackwell@benson.org | 2020-06-02 | http://www.good-lyons.com/ | -| 5 | 053d585Ab6b3159 | Joanna | Bender | Martin Lang and Andrade | West Priscilla | Slovakia (Slovak Republic) | 001-234-203-0635x76146 | 001-199-446-3860x3486 | colinalvarado@miles.net | 2021-04-17 | https://goodwin-ingram.com/ | +| Index | Customer Id | First Name | Last Name | Company | City | Country | Phone 1 | Phone 2 | Email | Subscription Date | Website | +| - | - | - | - | - | - | - | - | - | - | - | - | +| 1 | DD37Cf93aecA6Dc | Sheryl | Baxter | Rasmussen Group | East Leonard | Chile | 229.077.5154 | 397.884.0519x718 | zunigavanessa@smith.info | 2020-08-24 | http://www.stephenson.com/ | +| 2 | 1Ef7b82A4CAAD10 | Preston | Lozano | Vega-Gentry | East Jimmychester | Djibouti | 5153435776 | 686-620-1820x944 | vmata@colon.com | 2021-04-23 | http://www.hobbs.com/ | +| 3 | 6F94879bDAfE5a6 | Roy | Berry | Murillo-Perry | Isabelborough | Antigua and Barbuda | +1-539-402-0259 | (496)978-3969x58947 | beckycarr@hogan.com | 2020-03-25 | http://www.lawrence.com/ | +| 4 | 5Cef8BFA16c5e3c | Linda | Olsen | Dominguez Mcmillan and Donovan | Bensonview | Dominican Republic | 001-808-617-6467x12895 | +1-813-324-8756 | stanleyblackwell@benson.org | 2020-06-02 | http://www.good-lyons.com/ | +| 5 | 053d585Ab6b3159 | Joanna | Bender | Martin Lang and Andrade | West Priscilla | Slovakia (Slovak Republic) | 001-234-203-0635x76146 | 001-199-446-3860x3486 | colinalvarado@miles.net | 2021-04-17 | https://goodwin-ingram.com/ | diff --git a/crates/fleischwolf/tests/data/csv/expected/csv-too-few-columns.csv.md b/crates/fleischwolf/tests/data/csv/expected/csv-too-few-columns.csv.md index c77fc1cf..50ab7655 100644 --- a/crates/fleischwolf/tests/data/csv/expected/csv-too-few-columns.csv.md +++ b/crates/fleischwolf/tests/data/csv/expected/csv-too-few-columns.csv.md @@ -1,6 +1,6 @@ -| 1 | 2 | 3 | 4 | -|-----|-----|-----|-----| -| a | 'b' | c | d | -| a | b | c | | -| a | b | c | d | -| a | b | c | d | +| 1 | 2 | 3 | 4 | +| - | - | - | - | +| a | 'b' | c | d | +| a | b | c | | +| a | b | c | d | +| a | b | c | d | diff --git a/crates/fleischwolf/tests/data/csv/expected/csv-too-few-columns.csv.strict.md b/crates/fleischwolf/tests/data/csv/expected/csv-too-few-columns.csv.strict.md index c77fc1cf..50ab7655 100644 --- a/crates/fleischwolf/tests/data/csv/expected/csv-too-few-columns.csv.strict.md +++ b/crates/fleischwolf/tests/data/csv/expected/csv-too-few-columns.csv.strict.md @@ -1,6 +1,6 @@ -| 1 | 2 | 3 | 4 | -|-----|-----|-----|-----| -| a | 'b' | c | d | -| a | b | c | | -| a | b | c | d | -| a | b | c | d | +| 1 | 2 | 3 | 4 | +| - | - | - | - | +| a | 'b' | c | d | +| a | b | c | | +| a | b | c | d | +| a | b | c | d | diff --git a/crates/fleischwolf/tests/data/csv/expected/csv-too-many-columns.csv.md b/crates/fleischwolf/tests/data/csv/expected/csv-too-many-columns.csv.md index 76c5322d..97ba637b 100644 --- a/crates/fleischwolf/tests/data/csv/expected/csv-too-many-columns.csv.md +++ b/crates/fleischwolf/tests/data/csv/expected/csv-too-many-columns.csv.md @@ -1,6 +1,6 @@ -| 1 | 2 | 3 | 4 | | -|-----|-----|-----|-----|----| -| a | b | c | d | | -| a | b | c | d | e | -| a | b | c | d | | -| a | b | c | d | | +| 1 | 2 | 3 | 4 | | +| - | - | - | - | - | +| a | b | c | d | | +| a | b | c | d | e | +| a | b | c | d | | +| a | b | c | d | | diff --git a/crates/fleischwolf/tests/data/csv/expected/csv-too-many-columns.csv.strict.md b/crates/fleischwolf/tests/data/csv/expected/csv-too-many-columns.csv.strict.md index 76c5322d..97ba637b 100644 --- a/crates/fleischwolf/tests/data/csv/expected/csv-too-many-columns.csv.strict.md +++ b/crates/fleischwolf/tests/data/csv/expected/csv-too-many-columns.csv.strict.md @@ -1,6 +1,6 @@ -| 1 | 2 | 3 | 4 | | -|-----|-----|-----|-----|----| -| a | b | c | d | | -| a | b | c | d | e | -| a | b | c | d | | -| a | b | c | d | | +| 1 | 2 | 3 | 4 | | +| - | - | - | - | - | +| a | b | c | d | | +| a | b | c | d | e | +| a | b | c | d | | +| a | b | c | d | | diff --git a/crates/fleischwolf/tests/data/docx/expected/docx_checkboxes.docx.md b/crates/fleischwolf/tests/data/docx/expected/docx_checkboxes.docx.md index 1b8b2196..fb336763 100644 --- a/crates/fleischwolf/tests/data/docx/expected/docx_checkboxes.docx.md +++ b/crates/fleischwolf/tests/data/docx/expected/docx_checkboxes.docx.md @@ -1,10 +1,10 @@ ### Table with options -| **List of services** | -|---------------------------------------------------------------------------------------------------| -| Breakfast options | +| **List of services** | +| - | +| Breakfast options | | Choose as many as you like: - [x] Orange juice - [ ] Tea - [x] Coffee - [ ] Milk - [x] Water | -| Choose as many as you like: - [x] Scramble eggs - [ ] Porridge - [x] Bread - [x] Croissant | +| Choose as many as you like: - [x] Scramble eggs - [ ] Porridge - [x] Bread - [x] Croissant | ### Paragraph with options diff --git a/crates/fleischwolf/tests/data/docx/expected/docx_checkboxes.docx.strict.md b/crates/fleischwolf/tests/data/docx/expected/docx_checkboxes.docx.strict.md index fc903c1a..16063dce 100644 --- a/crates/fleischwolf/tests/data/docx/expected/docx_checkboxes.docx.strict.md +++ b/crates/fleischwolf/tests/data/docx/expected/docx_checkboxes.docx.strict.md @@ -1,10 +1,10 @@ ### Table with options -| **List of services** | -|---------------------------------------------------------------------------------------------------| -| Breakfast options | +| **List of services** | +| - | +| Breakfast options | | Choose as many as you like: - [x] Orange juice - [ ] Tea - [x] Coffee - [ ] Milk - [x] Water | -| Choose as many as you like: - [x] Scramble eggs - [ ] Porridge - [x] Bread - [x] Croissant | +| Choose as many as you like: - [x] Scramble eggs - [ ] Porridge - [x] Bread - [x] Croissant | ### Paragraph with options diff --git a/crates/fleischwolf/tests/data/docx/expected/docx_rich_cells.docx.md b/crates/fleischwolf/tests/data/docx/expected/docx_rich_cells.docx.md index ea17fcd1..5c0b9cdc 100644 --- a/crates/fleischwolf/tests/data/docx/expected/docx_rich_cells.docx.md +++ b/crates/fleischwolf/tests/data/docx/expected/docx_rich_cells.docx.md @@ -1,49 +1,49 @@ ### Table with rich cells -| Column A | Column B | -|------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------| -| This is a list: - A First - A Second - A Third | This is a formatted list: - B **First** - B *Second* - B Third | +| Column A | Column B | +| - | - | +| This is a list: - A First - A Second - A Third | This is a formatted list: - B **First** - B *Second* - B Third | | First Paragraph Second Paragraph Third paragraph before a numbered list 1. Number one 2. Number two 3. Number three | This is simple text with **bold** , ~~strikethrough~~ and *italic* formatting with x 2 and H 2 O | -| This is a paragraph This is another paragraph | | +| This is a paragraph This is another paragraph | | ### Table with nested table Before table -| Column A | Column B | -|----------------------------|------------------------------------------------------| -| Simple cell upper left | Simple cell with **bold** and *italic* text | +| Column A | Column B | +| - | - | +| Simple cell upper left | Simple cell with **bold** and *italic* text | | A B C Cell 1 Cell 2 Cell 3 | Rich cell A nested table A B C Cell 1 Cell 2 Cell 3 | After table with **bold** , underline , ~~strikethrough~~ , and *italic* formatting ### Table with pictures -| Column A | Column B | -|----------------------------------|----------------| -| Only text | | -| Text and picture | | +| Column A | Column B | +| - | - | +| Only text | | +| Text and picture | | ### Lists with same numId in different cells -| - Cell 1 item 1 - Cell 1 item 2 | -|-----------------------------------| -| - Cell 2 item 1 - Cell 2 item 2 | +| - Cell 1 item 1 - Cell 1 item 2 | +| - | +| - Cell 2 item 1 - Cell 2 item 2 | ### Lists with different numIds in different cells -| - Cell 1 item 1 - Cell 1 item 2 | -|-----------------------------------| -| - Cell 2 item 1 - Cell 2 item 2 | +| - Cell 1 item 1 - Cell 1 item 2 | +| - | +| - Cell 2 item 1 - Cell 2 item 2 | ### Multiple columns with lists -| - R1C1 item 1 - R1C1 item 2 | - R1C2 item 1 - R1C2 item 2 | -|-------------------------------|-------------------------------| -| - R2C1 item 1 - R2C1 item 2 | - R2C2 item 1 - R2C2 item 2 | +| - R1C1 item 1 - R1C1 item 2 | - R1C2 item 1 - R1C2 item 2 | +| - | - | +| - R2C1 item 1 - R2C1 item 2 | - R2C2 item 1 - R2C2 item 2 | ### Mixed content - list and regular text in different cells -| - List item 1 - List item 2 | -|-------------------------------| -| Regular text in second cell | +| - List item 1 - List item 2 | +| - | +| Regular text in second cell | diff --git a/crates/fleischwolf/tests/data/docx/expected/docx_rich_cells.docx.strict.md b/crates/fleischwolf/tests/data/docx/expected/docx_rich_cells.docx.strict.md index 41456194..5c476846 100644 --- a/crates/fleischwolf/tests/data/docx/expected/docx_rich_cells.docx.strict.md +++ b/crates/fleischwolf/tests/data/docx/expected/docx_rich_cells.docx.strict.md @@ -1,49 +1,49 @@ ### Table with rich cells -| Column A | Column B | -|------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------| -| This is a list: - A First - A Second - A Third | This is a formatted list: - B **First** - B *Second* - B Third | +| Column A | Column B | +| - | - | +| This is a list: - A First - A Second - A Third | This is a formatted list: - B **First** - B *Second* - B Third | | First Paragraph Second Paragraph Third paragraph before a numbered list 1. Number one 2. Number two 3. Number three | This is simple text with **bold** , ~~strikethrough~~ and *italic* formatting with x 2 and H 2 O | -| This is a paragraph This is another paragraph | | +| This is a paragraph This is another paragraph | | ### Table with nested table Before table -| Column A | Column B | -|----------------------------|------------------------------------------------------| -| Simple cell upper left | Simple cell with **bold** and *italic* text | +| Column A | Column B | +| - | - | +| Simple cell upper left | Simple cell with **bold** and *italic* text | | A B C Cell 1 Cell 2 Cell 3 | Rich cell A nested table A B C Cell 1 Cell 2 Cell 3 | After table with **bold**, underline, ~~strikethrough~~, and *italic* formatting ### Table with pictures -| Column A | Column B | -|----------------------------------|----------------| -| Only text | | -| Text and picture | | +| Column A | Column B | +| - | - | +| Only text | | +| Text and picture | | ### Lists with same numId in different cells -| - Cell 1 item 1 - Cell 1 item 2 | -|-----------------------------------| -| - Cell 2 item 1 - Cell 2 item 2 | +| - Cell 1 item 1 - Cell 1 item 2 | +| - | +| - Cell 2 item 1 - Cell 2 item 2 | ### Lists with different numIds in different cells -| - Cell 1 item 1 - Cell 1 item 2 | -|-----------------------------------| -| - Cell 2 item 1 - Cell 2 item 2 | +| - Cell 1 item 1 - Cell 1 item 2 | +| - | +| - Cell 2 item 1 - Cell 2 item 2 | ### Multiple columns with lists -| - R1C1 item 1 - R1C1 item 2 | - R1C2 item 1 - R1C2 item 2 | -|-------------------------------|-------------------------------| -| - R2C1 item 1 - R2C1 item 2 | - R2C2 item 1 - R2C2 item 2 | +| - R1C1 item 1 - R1C1 item 2 | - R1C2 item 1 - R1C2 item 2 | +| - | - | +| - R2C1 item 1 - R2C1 item 2 | - R2C2 item 1 - R2C2 item 2 | ### Mixed content - list and regular text in different cells -| - List item 1 - List item 2 | -|-------------------------------| -| Regular text in second cell | +| - List item 1 - List item 2 | +| - | +| Regular text in second cell | diff --git a/crates/fleischwolf/tests/data/docx/expected/docx_rich_tables_01.docx.md b/crates/fleischwolf/tests/data/docx/expected/docx_rich_tables_01.docx.md index 52b54a97..9f45129c 100644 --- a/crates/fleischwolf/tests/data/docx/expected/docx_rich_tables_01.docx.md +++ b/crates/fleischwolf/tests/data/docx/expected/docx_rich_tables_01.docx.md @@ -11,56 +11,56 @@ The transformation of this technical draft for ISO compliance involves two phase **Phase 1** -| **Feature** | **Action Needed** | **Comment/Links** | -|--------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| Document Clause Ordering | Reorganize into the mandatory sequence: • Foreword (Mandatory, unnumbered) • Introduction (Optional, unnumbered) • 1 Scope (Mandatory, Clause 1) • 2 Normative references (Mandatory, Clause 2) • 3 Terms and definitions (Mandatory, Clause 3) • 4 Symbols and abbreviated terms (Mandatory, Clause 4) • 5 Conformance (Mandatory, Clause 5) • 6 Technical Clauses... • Annex A (normative) • Annex B/C (informative) • Bibliography (Mandatory, absolute end) | The current draft improperly mixes introductory narrative, design principles, and examples before standard layout clauses are established. See ISO/IEC Directives, Part 2, Clause 6. | -| Author & Corporate Profiles | Completely remove the table listing individual names and company affiliations. | Personal names, corporate branding, logos, and affiliations are prohibited in the normative body of an ISO publication. See ISO/IEC Directives, Part 2, Clauses 4 and 12.5.2. | -| Foreword Boilerplate | Replace the original foreword with the mandatory ISO/IEC JTC 1 PAS foreword text and retain only a neutral statement identifying the originating workshop. | ISO forewords follow prescribed wording and structure. See ISO/IEC Directives, Part 2, Clause 12. | -| Market / Design Motivation | Remove the Motivation clause and all comparisons criticizing Markdown, HTML, LaTeX, PageXML, ALTO XML, hOCR, or similar technologies. | ISO standards shall remain technologically neutral and avoid comparative or competitive claims. See ISO/IEC Directives, Part 2, Clause 4. | -| Evolutionary Versioning Prose | Remove historical development narratives, beta-version discussions, and Version 0.x compatibility explanations. | ISO standards describe current requirements, not product-development history. See ISO/IEC Directives, Part 2, Clauses 11 and 12. | -| Clause Header Casing | Convert all clause and subclause headings to sentence case. | Title-case headings are not permitted by ISO house style. See ISO/IEC Directives, Part 2, Clause 22.2. | -| Self-References | Replace all occurrences of “this specification”, “this standard”, and “this format” with “this document”. | ISO publications refer to themselves exclusively as “this document”. See ISO/IEC Directives, Part 2, Clause 10.6. | -| Scope Clause Compliance | Rewrite Clause 1 to describe the subject matter only and remove normative statements, implementation obligations, and references to conformance requirements. | The Scope clause shall define the extent and field of application of the document and shall not contain requirements. See ISO/IEC Directives, Part 2, Clause 14. | -| Normative References Clause | Create Clause 2 immediately after Scope. If no references are required, insert: “There are no normative references in this document.” | Clause 2 is mandatory. See ISO/IEC Directives, Part 2, Clause 15. | -| External Dependency Classification | Review references to XML, XSD, Unicode, URI specifications, and RFCs, classifying each as normative (Clause 2) or informative (Bibliography). | Any referenced document required to implement this document shall appear in Clause 2. See ISO/IEC Directives, Part 2, Clause 15. | -| Terms and Definitions Clause | Convert “Terminology” into “Terms and definitions” and format all entries according to ISO terminology conventions. | Definitions shall follow the prescribed ISO structure. See ISO/IEC Directives, Part 2, Clause 16. | -| Entry Notes in Definitions | Convert embedded explanatory remarks into formal “Note X to entry:” format. | Informal notes embedded in definitions are not permitted. See ISO/IEC Directives, Part 2, Clause 16.5.9. | -| Terminological Source Attribution | Add formal [SOURCE: ...] citations to imported definitions. | Borrowed definitions require source attribution. See ISO/IEC Directives, Part 2, Clause 16.5.10. | -| Terminology Consistency Audit | Establish a single preferred term for each concept and remove synonyms throughout the document. | ISO drafting follows a one-concept–one-term principle. See ISO/IEC Directives, Part 2, Clause 16. | -| Symbols and Abbreviated Terms Clause | Create Clause 4 or merge abbreviations into Clause 3. | Acronyms such as OTSL, XML, VLM, RAG, PII, GDPR, and XSD require centralized treatment. See ISO/IEC Directives, Part 2, Clause 17. | -| Conformance Definition | Create Clause 5 Conformance defining the implementation classes eligible to claim compliance. | Requirements cannot exist without identifying the responsible implementation target. See ISO/IEC Directives, Part 2, Clause 33. | -| Conformance Classes | Define separate conformance classes where applicable (e.g., document producers, validators, parsers, processors, renderers). | Distinct implementation categories may require different obligations and test criteria. See ISO/IEC Directives, Part 2, Clause 33. | -| Requirement Traceability | Ensure every “shall” statement identifies a responsible subject and can be objectively tested. | Requirements must be measurable and verifiable. See ISO/IEC Directives, Part 2, Clause 33. | -| RFC Verbal Compliance | Replace RFC 2119 uppercase keywords (MUST, SHOULD, MAY) with ISO verbal forms (shall, should, may). | ISO does not recognize RFC keyword conventions. See ISO/IEC Directives, Part 2, Clause 7. | -| Introduction Compliance | Remove all requirements, permissions, recommendations, and implementation rules from the Introduction. | The Introduction is informative only. See ISO/IEC Directives, Part 2, Clause 13. | -| Informative Annex Compliance | Remove normative language from all informative annexes. | Informative annexes shall not contain requirements. See ISO/IEC Directives, Part 2, Clause 20.2. | -| Annex Naming | Rename Appendix A/B/C to Annex A (normative), Annex B (informative), and Annex C (informative). | ISO does not use the term “Appendix”. See ISO/IEC Directives, Part 2, Clause 20. | -| Annex Reference Audit | Ensure Annexes A, B, and C are explicitly referenced from the body text. | Unreferenced annexes are commonly flagged during editorial review. See ISO/IEC Directives, Part 2, Clause 20. | +| **Feature** | **Action Needed** | **Comment/Links** | +| - | - | - | +| Document Clause Ordering | Reorganize into the mandatory sequence: • Foreword (Mandatory, unnumbered) • Introduction (Optional, unnumbered) • 1 Scope (Mandatory, Clause 1) • 2 Normative references (Mandatory, Clause 2) • 3 Terms and definitions (Mandatory, Clause 3) • 4 Symbols and abbreviated terms (Mandatory, Clause 4) • 5 Conformance (Mandatory, Clause 5) • 6 Technical Clauses... • Annex A (normative) • Annex B/C (informative) • Bibliography (Mandatory, absolute end) | The current draft improperly mixes introductory narrative, design principles, and examples before standard layout clauses are established. See ISO/IEC Directives, Part 2, Clause 6. | +| Author & Corporate Profiles | Completely remove the table listing individual names and company affiliations. | Personal names, corporate branding, logos, and affiliations are prohibited in the normative body of an ISO publication. See ISO/IEC Directives, Part 2, Clauses 4 and 12.5.2. | +| Foreword Boilerplate | Replace the original foreword with the mandatory ISO/IEC JTC 1 PAS foreword text and retain only a neutral statement identifying the originating workshop. | ISO forewords follow prescribed wording and structure. See ISO/IEC Directives, Part 2, Clause 12. | +| Market / Design Motivation | Remove the Motivation clause and all comparisons criticizing Markdown, HTML, LaTeX, PageXML, ALTO XML, hOCR, or similar technologies. | ISO standards shall remain technologically neutral and avoid comparative or competitive claims. See ISO/IEC Directives, Part 2, Clause 4. | +| Evolutionary Versioning Prose | Remove historical development narratives, beta-version discussions, and Version 0.x compatibility explanations. | ISO standards describe current requirements, not product-development history. See ISO/IEC Directives, Part 2, Clauses 11 and 12. | +| Clause Header Casing | Convert all clause and subclause headings to sentence case. | Title-case headings are not permitted by ISO house style. See ISO/IEC Directives, Part 2, Clause 22.2. | +| Self-References | Replace all occurrences of “this specification”, “this standard”, and “this format” with “this document”. | ISO publications refer to themselves exclusively as “this document”. See ISO/IEC Directives, Part 2, Clause 10.6. | +| Scope Clause Compliance | Rewrite Clause 1 to describe the subject matter only and remove normative statements, implementation obligations, and references to conformance requirements. | The Scope clause shall define the extent and field of application of the document and shall not contain requirements. See ISO/IEC Directives, Part 2, Clause 14. | +| Normative References Clause | Create Clause 2 immediately after Scope. If no references are required, insert: “There are no normative references in this document.” | Clause 2 is mandatory. See ISO/IEC Directives, Part 2, Clause 15. | +| External Dependency Classification | Review references to XML, XSD, Unicode, URI specifications, and RFCs, classifying each as normative (Clause 2) or informative (Bibliography). | Any referenced document required to implement this document shall appear in Clause 2. See ISO/IEC Directives, Part 2, Clause 15. | +| Terms and Definitions Clause | Convert “Terminology” into “Terms and definitions” and format all entries according to ISO terminology conventions. | Definitions shall follow the prescribed ISO structure. See ISO/IEC Directives, Part 2, Clause 16. | +| Entry Notes in Definitions | Convert embedded explanatory remarks into formal “Note X to entry:” format. | Informal notes embedded in definitions are not permitted. See ISO/IEC Directives, Part 2, Clause 16.5.9. | +| Terminological Source Attribution | Add formal [SOURCE: ...] citations to imported definitions. | Borrowed definitions require source attribution. See ISO/IEC Directives, Part 2, Clause 16.5.10. | +| Terminology Consistency Audit | Establish a single preferred term for each concept and remove synonyms throughout the document. | ISO drafting follows a one-concept–one-term principle. See ISO/IEC Directives, Part 2, Clause 16. | +| Symbols and Abbreviated Terms Clause | Create Clause 4 or merge abbreviations into Clause 3. | Acronyms such as OTSL, XML, VLM, RAG, PII, GDPR, and XSD require centralized treatment. See ISO/IEC Directives, Part 2, Clause 17. | +| Conformance Definition | Create Clause 5 Conformance defining the implementation classes eligible to claim compliance. | Requirements cannot exist without identifying the responsible implementation target. See ISO/IEC Directives, Part 2, Clause 33. | +| Conformance Classes | Define separate conformance classes where applicable (e.g., document producers, validators, parsers, processors, renderers). | Distinct implementation categories may require different obligations and test criteria. See ISO/IEC Directives, Part 2, Clause 33. | +| Requirement Traceability | Ensure every “shall” statement identifies a responsible subject and can be objectively tested. | Requirements must be measurable and verifiable. See ISO/IEC Directives, Part 2, Clause 33. | +| RFC Verbal Compliance | Replace RFC 2119 uppercase keywords (MUST, SHOULD, MAY) with ISO verbal forms (shall, should, may). | ISO does not recognize RFC keyword conventions. See ISO/IEC Directives, Part 2, Clause 7. | +| Introduction Compliance | Remove all requirements, permissions, recommendations, and implementation rules from the Introduction. | The Introduction is informative only. See ISO/IEC Directives, Part 2, Clause 13. | +| Informative Annex Compliance | Remove normative language from all informative annexes. | Informative annexes shall not contain requirements. See ISO/IEC Directives, Part 2, Clause 20.2. | +| Annex Naming | Rename Appendix A/B/C to Annex A (normative), Annex B (informative), and Annex C (informative). | ISO does not use the term “Appendix”. See ISO/IEC Directives, Part 2, Clause 20. | +| Annex Reference Audit | Ensure Annexes A, B, and C are explicitly referenced from the body text. | Unreferenced annexes are commonly flagged during editorial review. See ISO/IEC Directives, Part 2, Clause 20. | **Phase 2** -| **Feature** | **Action Needed** | **Comment/Links** | -|-----------------------------------------|----------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------| -| Cross-Reference Normalization | Replace Markdown anchors and informal references with ISO clause references. | References should use forms such as “see 7.3.2” or “see Annex A”. See ISO/IEC Directives, Part 2, Clause 10. | -| Hanging Paragraph Subclauses | Insert “General” subclauses beneath major clauses before introducing subordinate subclauses. | Prevents hanging paragraphs and ambiguous clause structures. See ISO/IEC Directives, Part 2, Clause 22.3.3. | -| Orphan Subclause Remediation | Ensure any clause containing a subclause .1 also contains a .2 or merge the subdivision back into the parent clause. | ISO numbering rules prohibit single-child subdivisions. See ISO/IEC Directives, Part 2, Clause 22.3.2. | -| XML Tag Typography | Apply consistent literal formatting to element names, attributes, markup fragments, and grammar symbols. | Distinguishes identifiers from prose and reduces ambiguity. See ISO/IEC Directives, Part 2, Clauses 9 and 24. | -| Formal Language Notation | Introduce a dedicated clause describing any normative grammar notation, schema language, or syntax conventions. | Formal languages should be specified using recognized notation rather than examples alone. See ISO/IEC Directives, Part 2, Clause 9.2. | -| Attribute Value Enumerations | Consolidate controlled vocabularies and attribute values into structured tables or formal rules. | Improves implementability, validation, and conformance testing. See ISO/IEC Directives, Part 2, Clause 5.6 & Clause 29. | -| Number Formatting | Replace comma-separated thousands with ISO spacing conventions (e.g., 65 535). | See ISO/IEC Directives, Part 2, Clause 9.1. | -| Printable URI / URL Strings | Display explicit URI strings for externally referenced resources. | Documents must remain usable when printed. See ISO/IEC Directives, Part 2, Clause 10.3. | -| Bi-directional Reference Auditing | Verify that all normative references are cited normatively and all bibliography entries are cited informatively. | Orphan references are not permitted. See ISO/IEC Directives, Part 2, Clauses 10.1 and 15.1. | -| Font and Style Normalization | Remove non-standard formatting, decorative boxes, custom colors, and visual styling. | ISO publishing systems normalize typography and layout automatically. See ISO/IEC Directives, Part 2, Clause 1. | -| Informative Code / Example Marking | Label all examples explicitly as EXAMPLE. | Examples must be clearly distinguished from requirements. See ISO/IEC Directives, Part 2, Clause 25. | -| Example Separation | Audit explanatory text surrounding examples to ensure examples cannot be interpreted as normative requirements. | Examples are informative only. See ISO/IEC Directives, Part 2, Clause 25. | -| Tabular Formats | Convert pipe-delimited text tables into proper table structures. | Tables shall be structurally defined. See ISO/IEC Directives, Part 2, Clause 29. | -| Non-Normative Implementation Narrative | Move implementation guidance and authoring-process commentary into notes or informative annexes. | Standards define technical outcomes, not internal author workflows. See ISO/IEC Directives, Part 2, Clause 24. | -| Mermaid Diagram Transmutation | Convert Mermaid source code into static figures. | Text-based diagram source code is completely unsuitable for ISO publication systems. See ISO/IEC Directives, Part 2, Clause 28.6.4. | -| Graphic Text Normalization | Standardize figure typography and remove branding from graphics. | Figures shall be neutral, completely clear, legible, and publication-ready. See ISO/IEC Directives, Part 2, Clause 28.5.2. | -| Bibliography Construction | Relocate all informative references into a final unnumbered Bibliography. | See ISO/IEC Directives, Part 2, Clause 21. | -| Asset Caption Formatting | Convert captions to ISO figure/table caption style and sentence case. | See ISO/IEC Directives, Part 2, Clause 28.2 & Clause 29.2. | -| Commercial Tools Footnotes | Add non-endorsement wording for references to trademarked products or technologies. | See ISO/IEC Directives, Part 2, Clause 31. | -| Mathematical Interval & Unit Formatting | Convert interval notation and enforce spacing between values and units. | See ISO/IEC Directives, Part 2, Clauses 9.1 and 9.4.1. | -| Percentage Symbol Clean-up | Replace % in prose with “per cent”. | The symbol % is restricted entirely to tabular matrices and literal code blocks. See ISO/IEC Directives, Part 2, Clause 9.4.1 & Annex B. | -| Schema Placeholder Optimization | Remove placeholder values, ellipses, and drafting artifacts from examples. | Draft remnants are flagged as incomplete specification leaks by automated ingestion systems. See ISO/IEC Directives, Part 2, Clause 4.1. | -| Subscript Coordinate Notation | Convert snake_case mathematical variables into proper mathematical notation with subscripts. | See ISO/IEC Directives, Part 2, Clause 9.3.1. | +| **Feature** | **Action Needed** | **Comment/Links** | +| - | - | - | +| Cross-Reference Normalization | Replace Markdown anchors and informal references with ISO clause references. | References should use forms such as “see 7.3.2” or “see Annex A”. See ISO/IEC Directives, Part 2, Clause 10. | +| Hanging Paragraph Subclauses | Insert “General” subclauses beneath major clauses before introducing subordinate subclauses. | Prevents hanging paragraphs and ambiguous clause structures. See ISO/IEC Directives, Part 2, Clause 22.3.3. | +| Orphan Subclause Remediation | Ensure any clause containing a subclause .1 also contains a .2 or merge the subdivision back into the parent clause. | ISO numbering rules prohibit single-child subdivisions. See ISO/IEC Directives, Part 2, Clause 22.3.2. | +| XML Tag Typography | Apply consistent literal formatting to element names, attributes, markup fragments, and grammar symbols. | Distinguishes identifiers from prose and reduces ambiguity. See ISO/IEC Directives, Part 2, Clauses 9 and 24. | +| Formal Language Notation | Introduce a dedicated clause describing any normative grammar notation, schema language, or syntax conventions. | Formal languages should be specified using recognized notation rather than examples alone. See ISO/IEC Directives, Part 2, Clause 9.2. | +| Attribute Value Enumerations | Consolidate controlled vocabularies and attribute values into structured tables or formal rules. | Improves implementability, validation, and conformance testing. See ISO/IEC Directives, Part 2, Clause 5.6 & Clause 29. | +| Number Formatting | Replace comma-separated thousands with ISO spacing conventions (e.g., 65 535). | See ISO/IEC Directives, Part 2, Clause 9.1. | +| Printable URI / URL Strings | Display explicit URI strings for externally referenced resources. | Documents must remain usable when printed. See ISO/IEC Directives, Part 2, Clause 10.3. | +| Bi-directional Reference Auditing | Verify that all normative references are cited normatively and all bibliography entries are cited informatively. | Orphan references are not permitted. See ISO/IEC Directives, Part 2, Clauses 10.1 and 15.1. | +| Font and Style Normalization | Remove non-standard formatting, decorative boxes, custom colors, and visual styling. | ISO publishing systems normalize typography and layout automatically. See ISO/IEC Directives, Part 2, Clause 1. | +| Informative Code / Example Marking | Label all examples explicitly as EXAMPLE. | Examples must be clearly distinguished from requirements. See ISO/IEC Directives, Part 2, Clause 25. | +| Example Separation | Audit explanatory text surrounding examples to ensure examples cannot be interpreted as normative requirements. | Examples are informative only. See ISO/IEC Directives, Part 2, Clause 25. | +| Tabular Formats | Convert pipe-delimited text tables into proper table structures. | Tables shall be structurally defined. See ISO/IEC Directives, Part 2, Clause 29. | +| Non-Normative Implementation Narrative | Move implementation guidance and authoring-process commentary into notes or informative annexes. | Standards define technical outcomes, not internal author workflows. See ISO/IEC Directives, Part 2, Clause 24. | +| Mermaid Diagram Transmutation | Convert Mermaid source code into static figures. | Text-based diagram source code is completely unsuitable for ISO publication systems. See ISO/IEC Directives, Part 2, Clause 28.6.4. | +| Graphic Text Normalization | Standardize figure typography and remove branding from graphics. | Figures shall be neutral, completely clear, legible, and publication-ready. See ISO/IEC Directives, Part 2, Clause 28.5.2. | +| Bibliography Construction | Relocate all informative references into a final unnumbered Bibliography. | See ISO/IEC Directives, Part 2, Clause 21. | +| Asset Caption Formatting | Convert captions to ISO figure/table caption style and sentence case. | See ISO/IEC Directives, Part 2, Clause 28.2 & Clause 29.2. | +| Commercial Tools Footnotes | Add non-endorsement wording for references to trademarked products or technologies. | See ISO/IEC Directives, Part 2, Clause 31. | +| Mathematical Interval & Unit Formatting | Convert interval notation and enforce spacing between values and units. | See ISO/IEC Directives, Part 2, Clauses 9.1 and 9.4.1. | +| Percentage Symbol Clean-up | Replace % in prose with “per cent”. | The symbol % is restricted entirely to tabular matrices and literal code blocks. See ISO/IEC Directives, Part 2, Clause 9.4.1 & Annex B. | +| Schema Placeholder Optimization | Remove placeholder values, ellipses, and drafting artifacts from examples. | Draft remnants are flagged as incomplete specification leaks by automated ingestion systems. See ISO/IEC Directives, Part 2, Clause 4.1. | +| Subscript Coordinate Notation | Convert snake_case mathematical variables into proper mathematical notation with subscripts. | See ISO/IEC Directives, Part 2, Clause 9.3.1. | diff --git a/crates/fleischwolf/tests/data/docx/expected/docx_rich_tables_01.docx.strict.md b/crates/fleischwolf/tests/data/docx/expected/docx_rich_tables_01.docx.strict.md index 52b54a97..9f45129c 100644 --- a/crates/fleischwolf/tests/data/docx/expected/docx_rich_tables_01.docx.strict.md +++ b/crates/fleischwolf/tests/data/docx/expected/docx_rich_tables_01.docx.strict.md @@ -11,56 +11,56 @@ The transformation of this technical draft for ISO compliance involves two phase **Phase 1** -| **Feature** | **Action Needed** | **Comment/Links** | -|--------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| Document Clause Ordering | Reorganize into the mandatory sequence: • Foreword (Mandatory, unnumbered) • Introduction (Optional, unnumbered) • 1 Scope (Mandatory, Clause 1) • 2 Normative references (Mandatory, Clause 2) • 3 Terms and definitions (Mandatory, Clause 3) • 4 Symbols and abbreviated terms (Mandatory, Clause 4) • 5 Conformance (Mandatory, Clause 5) • 6 Technical Clauses... • Annex A (normative) • Annex B/C (informative) • Bibliography (Mandatory, absolute end) | The current draft improperly mixes introductory narrative, design principles, and examples before standard layout clauses are established. See ISO/IEC Directives, Part 2, Clause 6. | -| Author & Corporate Profiles | Completely remove the table listing individual names and company affiliations. | Personal names, corporate branding, logos, and affiliations are prohibited in the normative body of an ISO publication. See ISO/IEC Directives, Part 2, Clauses 4 and 12.5.2. | -| Foreword Boilerplate | Replace the original foreword with the mandatory ISO/IEC JTC 1 PAS foreword text and retain only a neutral statement identifying the originating workshop. | ISO forewords follow prescribed wording and structure. See ISO/IEC Directives, Part 2, Clause 12. | -| Market / Design Motivation | Remove the Motivation clause and all comparisons criticizing Markdown, HTML, LaTeX, PageXML, ALTO XML, hOCR, or similar technologies. | ISO standards shall remain technologically neutral and avoid comparative or competitive claims. See ISO/IEC Directives, Part 2, Clause 4. | -| Evolutionary Versioning Prose | Remove historical development narratives, beta-version discussions, and Version 0.x compatibility explanations. | ISO standards describe current requirements, not product-development history. See ISO/IEC Directives, Part 2, Clauses 11 and 12. | -| Clause Header Casing | Convert all clause and subclause headings to sentence case. | Title-case headings are not permitted by ISO house style. See ISO/IEC Directives, Part 2, Clause 22.2. | -| Self-References | Replace all occurrences of “this specification”, “this standard”, and “this format” with “this document”. | ISO publications refer to themselves exclusively as “this document”. See ISO/IEC Directives, Part 2, Clause 10.6. | -| Scope Clause Compliance | Rewrite Clause 1 to describe the subject matter only and remove normative statements, implementation obligations, and references to conformance requirements. | The Scope clause shall define the extent and field of application of the document and shall not contain requirements. See ISO/IEC Directives, Part 2, Clause 14. | -| Normative References Clause | Create Clause 2 immediately after Scope. If no references are required, insert: “There are no normative references in this document.” | Clause 2 is mandatory. See ISO/IEC Directives, Part 2, Clause 15. | -| External Dependency Classification | Review references to XML, XSD, Unicode, URI specifications, and RFCs, classifying each as normative (Clause 2) or informative (Bibliography). | Any referenced document required to implement this document shall appear in Clause 2. See ISO/IEC Directives, Part 2, Clause 15. | -| Terms and Definitions Clause | Convert “Terminology” into “Terms and definitions” and format all entries according to ISO terminology conventions. | Definitions shall follow the prescribed ISO structure. See ISO/IEC Directives, Part 2, Clause 16. | -| Entry Notes in Definitions | Convert embedded explanatory remarks into formal “Note X to entry:” format. | Informal notes embedded in definitions are not permitted. See ISO/IEC Directives, Part 2, Clause 16.5.9. | -| Terminological Source Attribution | Add formal [SOURCE: ...] citations to imported definitions. | Borrowed definitions require source attribution. See ISO/IEC Directives, Part 2, Clause 16.5.10. | -| Terminology Consistency Audit | Establish a single preferred term for each concept and remove synonyms throughout the document. | ISO drafting follows a one-concept–one-term principle. See ISO/IEC Directives, Part 2, Clause 16. | -| Symbols and Abbreviated Terms Clause | Create Clause 4 or merge abbreviations into Clause 3. | Acronyms such as OTSL, XML, VLM, RAG, PII, GDPR, and XSD require centralized treatment. See ISO/IEC Directives, Part 2, Clause 17. | -| Conformance Definition | Create Clause 5 Conformance defining the implementation classes eligible to claim compliance. | Requirements cannot exist without identifying the responsible implementation target. See ISO/IEC Directives, Part 2, Clause 33. | -| Conformance Classes | Define separate conformance classes where applicable (e.g., document producers, validators, parsers, processors, renderers). | Distinct implementation categories may require different obligations and test criteria. See ISO/IEC Directives, Part 2, Clause 33. | -| Requirement Traceability | Ensure every “shall” statement identifies a responsible subject and can be objectively tested. | Requirements must be measurable and verifiable. See ISO/IEC Directives, Part 2, Clause 33. | -| RFC Verbal Compliance | Replace RFC 2119 uppercase keywords (MUST, SHOULD, MAY) with ISO verbal forms (shall, should, may). | ISO does not recognize RFC keyword conventions. See ISO/IEC Directives, Part 2, Clause 7. | -| Introduction Compliance | Remove all requirements, permissions, recommendations, and implementation rules from the Introduction. | The Introduction is informative only. See ISO/IEC Directives, Part 2, Clause 13. | -| Informative Annex Compliance | Remove normative language from all informative annexes. | Informative annexes shall not contain requirements. See ISO/IEC Directives, Part 2, Clause 20.2. | -| Annex Naming | Rename Appendix A/B/C to Annex A (normative), Annex B (informative), and Annex C (informative). | ISO does not use the term “Appendix”. See ISO/IEC Directives, Part 2, Clause 20. | -| Annex Reference Audit | Ensure Annexes A, B, and C are explicitly referenced from the body text. | Unreferenced annexes are commonly flagged during editorial review. See ISO/IEC Directives, Part 2, Clause 20. | +| **Feature** | **Action Needed** | **Comment/Links** | +| - | - | - | +| Document Clause Ordering | Reorganize into the mandatory sequence: • Foreword (Mandatory, unnumbered) • Introduction (Optional, unnumbered) • 1 Scope (Mandatory, Clause 1) • 2 Normative references (Mandatory, Clause 2) • 3 Terms and definitions (Mandatory, Clause 3) • 4 Symbols and abbreviated terms (Mandatory, Clause 4) • 5 Conformance (Mandatory, Clause 5) • 6 Technical Clauses... • Annex A (normative) • Annex B/C (informative) • Bibliography (Mandatory, absolute end) | The current draft improperly mixes introductory narrative, design principles, and examples before standard layout clauses are established. See ISO/IEC Directives, Part 2, Clause 6. | +| Author & Corporate Profiles | Completely remove the table listing individual names and company affiliations. | Personal names, corporate branding, logos, and affiliations are prohibited in the normative body of an ISO publication. See ISO/IEC Directives, Part 2, Clauses 4 and 12.5.2. | +| Foreword Boilerplate | Replace the original foreword with the mandatory ISO/IEC JTC 1 PAS foreword text and retain only a neutral statement identifying the originating workshop. | ISO forewords follow prescribed wording and structure. See ISO/IEC Directives, Part 2, Clause 12. | +| Market / Design Motivation | Remove the Motivation clause and all comparisons criticizing Markdown, HTML, LaTeX, PageXML, ALTO XML, hOCR, or similar technologies. | ISO standards shall remain technologically neutral and avoid comparative or competitive claims. See ISO/IEC Directives, Part 2, Clause 4. | +| Evolutionary Versioning Prose | Remove historical development narratives, beta-version discussions, and Version 0.x compatibility explanations. | ISO standards describe current requirements, not product-development history. See ISO/IEC Directives, Part 2, Clauses 11 and 12. | +| Clause Header Casing | Convert all clause and subclause headings to sentence case. | Title-case headings are not permitted by ISO house style. See ISO/IEC Directives, Part 2, Clause 22.2. | +| Self-References | Replace all occurrences of “this specification”, “this standard”, and “this format” with “this document”. | ISO publications refer to themselves exclusively as “this document”. See ISO/IEC Directives, Part 2, Clause 10.6. | +| Scope Clause Compliance | Rewrite Clause 1 to describe the subject matter only and remove normative statements, implementation obligations, and references to conformance requirements. | The Scope clause shall define the extent and field of application of the document and shall not contain requirements. See ISO/IEC Directives, Part 2, Clause 14. | +| Normative References Clause | Create Clause 2 immediately after Scope. If no references are required, insert: “There are no normative references in this document.” | Clause 2 is mandatory. See ISO/IEC Directives, Part 2, Clause 15. | +| External Dependency Classification | Review references to XML, XSD, Unicode, URI specifications, and RFCs, classifying each as normative (Clause 2) or informative (Bibliography). | Any referenced document required to implement this document shall appear in Clause 2. See ISO/IEC Directives, Part 2, Clause 15. | +| Terms and Definitions Clause | Convert “Terminology” into “Terms and definitions” and format all entries according to ISO terminology conventions. | Definitions shall follow the prescribed ISO structure. See ISO/IEC Directives, Part 2, Clause 16. | +| Entry Notes in Definitions | Convert embedded explanatory remarks into formal “Note X to entry:” format. | Informal notes embedded in definitions are not permitted. See ISO/IEC Directives, Part 2, Clause 16.5.9. | +| Terminological Source Attribution | Add formal [SOURCE: ...] citations to imported definitions. | Borrowed definitions require source attribution. See ISO/IEC Directives, Part 2, Clause 16.5.10. | +| Terminology Consistency Audit | Establish a single preferred term for each concept and remove synonyms throughout the document. | ISO drafting follows a one-concept–one-term principle. See ISO/IEC Directives, Part 2, Clause 16. | +| Symbols and Abbreviated Terms Clause | Create Clause 4 or merge abbreviations into Clause 3. | Acronyms such as OTSL, XML, VLM, RAG, PII, GDPR, and XSD require centralized treatment. See ISO/IEC Directives, Part 2, Clause 17. | +| Conformance Definition | Create Clause 5 Conformance defining the implementation classes eligible to claim compliance. | Requirements cannot exist without identifying the responsible implementation target. See ISO/IEC Directives, Part 2, Clause 33. | +| Conformance Classes | Define separate conformance classes where applicable (e.g., document producers, validators, parsers, processors, renderers). | Distinct implementation categories may require different obligations and test criteria. See ISO/IEC Directives, Part 2, Clause 33. | +| Requirement Traceability | Ensure every “shall” statement identifies a responsible subject and can be objectively tested. | Requirements must be measurable and verifiable. See ISO/IEC Directives, Part 2, Clause 33. | +| RFC Verbal Compliance | Replace RFC 2119 uppercase keywords (MUST, SHOULD, MAY) with ISO verbal forms (shall, should, may). | ISO does not recognize RFC keyword conventions. See ISO/IEC Directives, Part 2, Clause 7. | +| Introduction Compliance | Remove all requirements, permissions, recommendations, and implementation rules from the Introduction. | The Introduction is informative only. See ISO/IEC Directives, Part 2, Clause 13. | +| Informative Annex Compliance | Remove normative language from all informative annexes. | Informative annexes shall not contain requirements. See ISO/IEC Directives, Part 2, Clause 20.2. | +| Annex Naming | Rename Appendix A/B/C to Annex A (normative), Annex B (informative), and Annex C (informative). | ISO does not use the term “Appendix”. See ISO/IEC Directives, Part 2, Clause 20. | +| Annex Reference Audit | Ensure Annexes A, B, and C are explicitly referenced from the body text. | Unreferenced annexes are commonly flagged during editorial review. See ISO/IEC Directives, Part 2, Clause 20. | **Phase 2** -| **Feature** | **Action Needed** | **Comment/Links** | -|-----------------------------------------|----------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------| -| Cross-Reference Normalization | Replace Markdown anchors and informal references with ISO clause references. | References should use forms such as “see 7.3.2” or “see Annex A”. See ISO/IEC Directives, Part 2, Clause 10. | -| Hanging Paragraph Subclauses | Insert “General” subclauses beneath major clauses before introducing subordinate subclauses. | Prevents hanging paragraphs and ambiguous clause structures. See ISO/IEC Directives, Part 2, Clause 22.3.3. | -| Orphan Subclause Remediation | Ensure any clause containing a subclause .1 also contains a .2 or merge the subdivision back into the parent clause. | ISO numbering rules prohibit single-child subdivisions. See ISO/IEC Directives, Part 2, Clause 22.3.2. | -| XML Tag Typography | Apply consistent literal formatting to element names, attributes, markup fragments, and grammar symbols. | Distinguishes identifiers from prose and reduces ambiguity. See ISO/IEC Directives, Part 2, Clauses 9 and 24. | -| Formal Language Notation | Introduce a dedicated clause describing any normative grammar notation, schema language, or syntax conventions. | Formal languages should be specified using recognized notation rather than examples alone. See ISO/IEC Directives, Part 2, Clause 9.2. | -| Attribute Value Enumerations | Consolidate controlled vocabularies and attribute values into structured tables or formal rules. | Improves implementability, validation, and conformance testing. See ISO/IEC Directives, Part 2, Clause 5.6 & Clause 29. | -| Number Formatting | Replace comma-separated thousands with ISO spacing conventions (e.g., 65 535). | See ISO/IEC Directives, Part 2, Clause 9.1. | -| Printable URI / URL Strings | Display explicit URI strings for externally referenced resources. | Documents must remain usable when printed. See ISO/IEC Directives, Part 2, Clause 10.3. | -| Bi-directional Reference Auditing | Verify that all normative references are cited normatively and all bibliography entries are cited informatively. | Orphan references are not permitted. See ISO/IEC Directives, Part 2, Clauses 10.1 and 15.1. | -| Font and Style Normalization | Remove non-standard formatting, decorative boxes, custom colors, and visual styling. | ISO publishing systems normalize typography and layout automatically. See ISO/IEC Directives, Part 2, Clause 1. | -| Informative Code / Example Marking | Label all examples explicitly as EXAMPLE. | Examples must be clearly distinguished from requirements. See ISO/IEC Directives, Part 2, Clause 25. | -| Example Separation | Audit explanatory text surrounding examples to ensure examples cannot be interpreted as normative requirements. | Examples are informative only. See ISO/IEC Directives, Part 2, Clause 25. | -| Tabular Formats | Convert pipe-delimited text tables into proper table structures. | Tables shall be structurally defined. See ISO/IEC Directives, Part 2, Clause 29. | -| Non-Normative Implementation Narrative | Move implementation guidance and authoring-process commentary into notes or informative annexes. | Standards define technical outcomes, not internal author workflows. See ISO/IEC Directives, Part 2, Clause 24. | -| Mermaid Diagram Transmutation | Convert Mermaid source code into static figures. | Text-based diagram source code is completely unsuitable for ISO publication systems. See ISO/IEC Directives, Part 2, Clause 28.6.4. | -| Graphic Text Normalization | Standardize figure typography and remove branding from graphics. | Figures shall be neutral, completely clear, legible, and publication-ready. See ISO/IEC Directives, Part 2, Clause 28.5.2. | -| Bibliography Construction | Relocate all informative references into a final unnumbered Bibliography. | See ISO/IEC Directives, Part 2, Clause 21. | -| Asset Caption Formatting | Convert captions to ISO figure/table caption style and sentence case. | See ISO/IEC Directives, Part 2, Clause 28.2 & Clause 29.2. | -| Commercial Tools Footnotes | Add non-endorsement wording for references to trademarked products or technologies. | See ISO/IEC Directives, Part 2, Clause 31. | -| Mathematical Interval & Unit Formatting | Convert interval notation and enforce spacing between values and units. | See ISO/IEC Directives, Part 2, Clauses 9.1 and 9.4.1. | -| Percentage Symbol Clean-up | Replace % in prose with “per cent”. | The symbol % is restricted entirely to tabular matrices and literal code blocks. See ISO/IEC Directives, Part 2, Clause 9.4.1 & Annex B. | -| Schema Placeholder Optimization | Remove placeholder values, ellipses, and drafting artifacts from examples. | Draft remnants are flagged as incomplete specification leaks by automated ingestion systems. See ISO/IEC Directives, Part 2, Clause 4.1. | -| Subscript Coordinate Notation | Convert snake_case mathematical variables into proper mathematical notation with subscripts. | See ISO/IEC Directives, Part 2, Clause 9.3.1. | +| **Feature** | **Action Needed** | **Comment/Links** | +| - | - | - | +| Cross-Reference Normalization | Replace Markdown anchors and informal references with ISO clause references. | References should use forms such as “see 7.3.2” or “see Annex A”. See ISO/IEC Directives, Part 2, Clause 10. | +| Hanging Paragraph Subclauses | Insert “General” subclauses beneath major clauses before introducing subordinate subclauses. | Prevents hanging paragraphs and ambiguous clause structures. See ISO/IEC Directives, Part 2, Clause 22.3.3. | +| Orphan Subclause Remediation | Ensure any clause containing a subclause .1 also contains a .2 or merge the subdivision back into the parent clause. | ISO numbering rules prohibit single-child subdivisions. See ISO/IEC Directives, Part 2, Clause 22.3.2. | +| XML Tag Typography | Apply consistent literal formatting to element names, attributes, markup fragments, and grammar symbols. | Distinguishes identifiers from prose and reduces ambiguity. See ISO/IEC Directives, Part 2, Clauses 9 and 24. | +| Formal Language Notation | Introduce a dedicated clause describing any normative grammar notation, schema language, or syntax conventions. | Formal languages should be specified using recognized notation rather than examples alone. See ISO/IEC Directives, Part 2, Clause 9.2. | +| Attribute Value Enumerations | Consolidate controlled vocabularies and attribute values into structured tables or formal rules. | Improves implementability, validation, and conformance testing. See ISO/IEC Directives, Part 2, Clause 5.6 & Clause 29. | +| Number Formatting | Replace comma-separated thousands with ISO spacing conventions (e.g., 65 535). | See ISO/IEC Directives, Part 2, Clause 9.1. | +| Printable URI / URL Strings | Display explicit URI strings for externally referenced resources. | Documents must remain usable when printed. See ISO/IEC Directives, Part 2, Clause 10.3. | +| Bi-directional Reference Auditing | Verify that all normative references are cited normatively and all bibliography entries are cited informatively. | Orphan references are not permitted. See ISO/IEC Directives, Part 2, Clauses 10.1 and 15.1. | +| Font and Style Normalization | Remove non-standard formatting, decorative boxes, custom colors, and visual styling. | ISO publishing systems normalize typography and layout automatically. See ISO/IEC Directives, Part 2, Clause 1. | +| Informative Code / Example Marking | Label all examples explicitly as EXAMPLE. | Examples must be clearly distinguished from requirements. See ISO/IEC Directives, Part 2, Clause 25. | +| Example Separation | Audit explanatory text surrounding examples to ensure examples cannot be interpreted as normative requirements. | Examples are informative only. See ISO/IEC Directives, Part 2, Clause 25. | +| Tabular Formats | Convert pipe-delimited text tables into proper table structures. | Tables shall be structurally defined. See ISO/IEC Directives, Part 2, Clause 29. | +| Non-Normative Implementation Narrative | Move implementation guidance and authoring-process commentary into notes or informative annexes. | Standards define technical outcomes, not internal author workflows. See ISO/IEC Directives, Part 2, Clause 24. | +| Mermaid Diagram Transmutation | Convert Mermaid source code into static figures. | Text-based diagram source code is completely unsuitable for ISO publication systems. See ISO/IEC Directives, Part 2, Clause 28.6.4. | +| Graphic Text Normalization | Standardize figure typography and remove branding from graphics. | Figures shall be neutral, completely clear, legible, and publication-ready. See ISO/IEC Directives, Part 2, Clause 28.5.2. | +| Bibliography Construction | Relocate all informative references into a final unnumbered Bibliography. | See ISO/IEC Directives, Part 2, Clause 21. | +| Asset Caption Formatting | Convert captions to ISO figure/table caption style and sentence case. | See ISO/IEC Directives, Part 2, Clause 28.2 & Clause 29.2. | +| Commercial Tools Footnotes | Add non-endorsement wording for references to trademarked products or technologies. | See ISO/IEC Directives, Part 2, Clause 31. | +| Mathematical Interval & Unit Formatting | Convert interval notation and enforce spacing between values and units. | See ISO/IEC Directives, Part 2, Clauses 9.1 and 9.4.1. | +| Percentage Symbol Clean-up | Replace % in prose with “per cent”. | The symbol % is restricted entirely to tabular matrices and literal code blocks. See ISO/IEC Directives, Part 2, Clause 9.4.1 & Annex B. | +| Schema Placeholder Optimization | Remove placeholder values, ellipses, and drafting artifacts from examples. | Draft remnants are flagged as incomplete specification leaks by automated ingestion systems. See ISO/IEC Directives, Part 2, Clause 4.1. | +| Subscript Coordinate Notation | Convert snake_case mathematical variables into proper mathematical notation with subscripts. | See ISO/IEC Directives, Part 2, Clause 9.3.1. | diff --git a/crates/fleischwolf/tests/data/docx/expected/table_with_equations.docx.md b/crates/fleischwolf/tests/data/docx/expected/table_with_equations.docx.md index bb2ebb08..cb26da08 100644 --- a/crates/fleischwolf/tests/data/docx/expected/table_with_equations.docx.md +++ b/crates/fleischwolf/tests/data/docx/expected/table_with_equations.docx.md @@ -1,3 +1,3 @@ -| The next cell has an equation | $A= \pi r^{2}$ | -|------------------------------------|----------------------------------------| +| The next cell has an equation | $A= \pi r^{2}$ | +| - | - | | The next cell has another equation | $x=\frac{-b \pm \sqrt{b^{2}-4ac}}{2a}$ | diff --git a/crates/fleischwolf/tests/data/docx/expected/table_with_equations.docx.strict.md b/crates/fleischwolf/tests/data/docx/expected/table_with_equations.docx.strict.md index bb2ebb08..cb26da08 100644 --- a/crates/fleischwolf/tests/data/docx/expected/table_with_equations.docx.strict.md +++ b/crates/fleischwolf/tests/data/docx/expected/table_with_equations.docx.strict.md @@ -1,3 +1,3 @@ -| The next cell has an equation | $A= \pi r^{2}$ | -|------------------------------------|----------------------------------------| +| The next cell has an equation | $A= \pi r^{2}$ | +| - | - | | The next cell has another equation | $x=\frac{-b \pm \sqrt{b^{2}-4ac}}{2a}$ | diff --git a/crates/fleischwolf/tests/data/docx/expected/tablecell.docx.md b/crates/fleischwolf/tests/data/docx/expected/tablecell.docx.md index 6db4a76f..0cdde00a 100644 --- a/crates/fleischwolf/tests/data/docx/expected/tablecell.docx.md +++ b/crates/fleischwolf/tests/data/docx/expected/tablecell.docx.md @@ -3,9 +3,9 @@ Some text before -| Tab1 | Tab2 | Tab3 | -|--------|--------|--------| -| A | B | C | -| D | E | F | +| Tab1 | Tab2 | Tab3 | +| - | - | - | +| A | B | C | +| D | E | F | Some text after diff --git a/crates/fleischwolf/tests/data/docx/expected/tablecell.docx.strict.md b/crates/fleischwolf/tests/data/docx/expected/tablecell.docx.strict.md index 6db4a76f..0cdde00a 100644 --- a/crates/fleischwolf/tests/data/docx/expected/tablecell.docx.strict.md +++ b/crates/fleischwolf/tests/data/docx/expected/tablecell.docx.strict.md @@ -3,9 +3,9 @@ Some text before -| Tab1 | Tab2 | Tab3 | -|--------|--------|--------| -| A | B | C | -| D | E | F | +| Tab1 | Tab2 | Tab3 | +| - | - | - | +| A | B | C | +| D | E | F | Some text after diff --git a/crates/fleischwolf/tests/data/docx/expected/word_sample.docx.md b/crates/fleischwolf/tests/data/docx/expected/word_sample.docx.md index ff1ce117..2ebb4436 100644 --- a/crates/fleischwolf/tests/data/docx/expected/word_sample.docx.md +++ b/crates/fleischwolf/tests/data/docx/expected/word_sample.docx.md @@ -32,11 +32,11 @@ I like to eat leaves Here are some interesting things a respectful duck could eat: -| | Food | Calories per portion | -|---------|----------------------------------|------------------------| -| Leaves | Ash, Elm, Maple | 50 | -| Berries | Blueberry, Strawberry, Cranberry | 150 | -| Grain | Corn, Buckwheat, Barley | 200 | +| | Food | Calories per portion | +| - | - | - | +| Leaves | Ash, Elm, Maple | 50 | +| Berries | Blueberry, Strawberry, Cranberry | 150 | +| Grain | Corn, Buckwheat, Barley | 200 | And let’s add another list in the end: diff --git a/crates/fleischwolf/tests/data/docx/expected/word_sample.docx.strict.md b/crates/fleischwolf/tests/data/docx/expected/word_sample.docx.strict.md index ff1ce117..2ebb4436 100644 --- a/crates/fleischwolf/tests/data/docx/expected/word_sample.docx.strict.md +++ b/crates/fleischwolf/tests/data/docx/expected/word_sample.docx.strict.md @@ -32,11 +32,11 @@ I like to eat leaves Here are some interesting things a respectful duck could eat: -| | Food | Calories per portion | -|---------|----------------------------------|------------------------| -| Leaves | Ash, Elm, Maple | 50 | -| Berries | Blueberry, Strawberry, Cranberry | 150 | -| Grain | Corn, Buckwheat, Barley | 200 | +| | Food | Calories per portion | +| - | - | - | +| Leaves | Ash, Elm, Maple | 50 | +| Berries | Blueberry, Strawberry, Cranberry | 150 | +| Grain | Corn, Buckwheat, Barley | 200 | And let’s add another list in the end: diff --git a/crates/fleischwolf/tests/data/docx/expected/word_tables.docx.md b/crates/fleischwolf/tests/data/docx/expected/word_tables.docx.md index 79872676..6f1e18e3 100644 --- a/crates/fleischwolf/tests/data/docx/expected/word_tables.docx.md +++ b/crates/fleischwolf/tests/data/docx/expected/word_tables.docx.md @@ -2,43 +2,43 @@ A uniform table -| Header 0.0 | Header 0.1 | Header 0.2 | -|--------------|--------------|--------------| -| Cell 1.0 | Cell 1.1 | Cell 1.2 | -| Cell 2.0 | Cell 2.1 | Cell 2.2 | +| Header 0.0 | Header 0.1 | Header 0.2 | +| - | - | - | +| Cell 1.0 | Cell 1.1 | Cell 1.2 | +| Cell 2.0 | Cell 2.1 | Cell 2.2 | A non-uniform table with horizontal spans -| Header 0.0 | Header 0.1 | Header 0.2 | -|--------------|---------------------|---------------------| -| Cell 1.0 | Merged Cell 1.1 1.2 | Merged Cell 1.1 1.2 | -| Cell 2.0 | Merged Cell 2.1 2.2 | Merged Cell 2.1 2.2 | +| Header 0.0 | Header 0.1 | Header 0.2 | +| - | - | - | +| Cell 1.0 | Merged Cell 1.1 1.2 | Merged Cell 1.1 1.2 | +| Cell 2.0 | Merged Cell 2.1 2.2 | Merged Cell 2.1 2.2 | A non-uniform table with horizontal spans in inner columns -| Header 0.0 | Header 0.1 | Header 0.2 | Header 0.3 | -|--------------|---------------------|---------------------|--------------| -| Cell 1.0 | Merged Cell 1.1 1.2 | Merged Cell 1.1 1.2 | Cell 1.3 | -| Cell 2.0 | Merged Cell 2.1 2.2 | Merged Cell 2.1 2.2 | Cell 2.3 | +| Header 0.0 | Header 0.1 | Header 0.2 | Header 0.3 | +| - | - | - | - | +| Cell 1.0 | Merged Cell 1.1 1.2 | Merged Cell 1.1 1.2 | Cell 1.3 | +| Cell 2.0 | Merged Cell 2.1 2.2 | Merged Cell 2.1 2.2 | Cell 2.3 | A non-uniform table with vertical spans -| Header 0.0 | Header 0.1 | Header 0.2 | -|--------------|---------------------|--------------| -| Cell 1.0 | Merged Cell 1.1 2.1 | Cell 1.2 | -| Cell 2.0 | Merged Cell 1.1 2.1 | Cell 2.2 | -| Cell 3.0 | Merged Cell 3.1 4.1 | Cell 3.2 | -| Cell 4.0 | Merged Cell 3.1 4.1 | Cell 4.2 | +| Header 0.0 | Header 0.1 | Header 0.2 | +| - | - | - | +| Cell 1.0 | Merged Cell 1.1 2.1 | Cell 1.2 | +| Cell 2.0 | Merged Cell 1.1 2.1 | Cell 2.2 | +| Cell 3.0 | Merged Cell 3.1 4.1 | Cell 3.2 | +| Cell 4.0 | Merged Cell 3.1 4.1 | Cell 4.2 | A non-uniform table with all kinds of spans and empty cells -| Header 0.0 | Header 0.1 | Header 0.2 | | | -|--------------|---------------------|--------------|----|---------------------| -| Cell 1.0 | Merged Cell 1.1 2.1 | Cell 1.2 | | | -| Cell 2.0 | Merged Cell 1.1 2.1 | Cell 2.2 | | | -| Cell 3.0 | Merged Cell 3.1 4.1 | Cell 3.2 | | | -| Cell 4.0 | Merged Cell 3.1 4.1 | Cell 4.2 | | Merged Cell 4.4 5.4 | -| | | | | Merged Cell 4.4 5.4 | -| | | | | | -| | | | | | -| | | | | Cell 8.4 | +| Header 0.0 | Header 0.1 | Header 0.2 | | | +| - | - | - | - | - | +| Cell 1.0 | Merged Cell 1.1 2.1 | Cell 1.2 | | | +| Cell 2.0 | Merged Cell 1.1 2.1 | Cell 2.2 | | | +| Cell 3.0 | Merged Cell 3.1 4.1 | Cell 3.2 | | | +| Cell 4.0 | Merged Cell 3.1 4.1 | Cell 4.2 | | Merged Cell 4.4 5.4 | +| | | | | Merged Cell 4.4 5.4 | +| | | | | | +| | | | | | +| | | | | Cell 8.4 | diff --git a/crates/fleischwolf/tests/data/docx/expected/word_tables.docx.strict.md b/crates/fleischwolf/tests/data/docx/expected/word_tables.docx.strict.md index 79872676..6f1e18e3 100644 --- a/crates/fleischwolf/tests/data/docx/expected/word_tables.docx.strict.md +++ b/crates/fleischwolf/tests/data/docx/expected/word_tables.docx.strict.md @@ -2,43 +2,43 @@ A uniform table -| Header 0.0 | Header 0.1 | Header 0.2 | -|--------------|--------------|--------------| -| Cell 1.0 | Cell 1.1 | Cell 1.2 | -| Cell 2.0 | Cell 2.1 | Cell 2.2 | +| Header 0.0 | Header 0.1 | Header 0.2 | +| - | - | - | +| Cell 1.0 | Cell 1.1 | Cell 1.2 | +| Cell 2.0 | Cell 2.1 | Cell 2.2 | A non-uniform table with horizontal spans -| Header 0.0 | Header 0.1 | Header 0.2 | -|--------------|---------------------|---------------------| -| Cell 1.0 | Merged Cell 1.1 1.2 | Merged Cell 1.1 1.2 | -| Cell 2.0 | Merged Cell 2.1 2.2 | Merged Cell 2.1 2.2 | +| Header 0.0 | Header 0.1 | Header 0.2 | +| - | - | - | +| Cell 1.0 | Merged Cell 1.1 1.2 | Merged Cell 1.1 1.2 | +| Cell 2.0 | Merged Cell 2.1 2.2 | Merged Cell 2.1 2.2 | A non-uniform table with horizontal spans in inner columns -| Header 0.0 | Header 0.1 | Header 0.2 | Header 0.3 | -|--------------|---------------------|---------------------|--------------| -| Cell 1.0 | Merged Cell 1.1 1.2 | Merged Cell 1.1 1.2 | Cell 1.3 | -| Cell 2.0 | Merged Cell 2.1 2.2 | Merged Cell 2.1 2.2 | Cell 2.3 | +| Header 0.0 | Header 0.1 | Header 0.2 | Header 0.3 | +| - | - | - | - | +| Cell 1.0 | Merged Cell 1.1 1.2 | Merged Cell 1.1 1.2 | Cell 1.3 | +| Cell 2.0 | Merged Cell 2.1 2.2 | Merged Cell 2.1 2.2 | Cell 2.3 | A non-uniform table with vertical spans -| Header 0.0 | Header 0.1 | Header 0.2 | -|--------------|---------------------|--------------| -| Cell 1.0 | Merged Cell 1.1 2.1 | Cell 1.2 | -| Cell 2.0 | Merged Cell 1.1 2.1 | Cell 2.2 | -| Cell 3.0 | Merged Cell 3.1 4.1 | Cell 3.2 | -| Cell 4.0 | Merged Cell 3.1 4.1 | Cell 4.2 | +| Header 0.0 | Header 0.1 | Header 0.2 | +| - | - | - | +| Cell 1.0 | Merged Cell 1.1 2.1 | Cell 1.2 | +| Cell 2.0 | Merged Cell 1.1 2.1 | Cell 2.2 | +| Cell 3.0 | Merged Cell 3.1 4.1 | Cell 3.2 | +| Cell 4.0 | Merged Cell 3.1 4.1 | Cell 4.2 | A non-uniform table with all kinds of spans and empty cells -| Header 0.0 | Header 0.1 | Header 0.2 | | | -|--------------|---------------------|--------------|----|---------------------| -| Cell 1.0 | Merged Cell 1.1 2.1 | Cell 1.2 | | | -| Cell 2.0 | Merged Cell 1.1 2.1 | Cell 2.2 | | | -| Cell 3.0 | Merged Cell 3.1 4.1 | Cell 3.2 | | | -| Cell 4.0 | Merged Cell 3.1 4.1 | Cell 4.2 | | Merged Cell 4.4 5.4 | -| | | | | Merged Cell 4.4 5.4 | -| | | | | | -| | | | | | -| | | | | Cell 8.4 | +| Header 0.0 | Header 0.1 | Header 0.2 | | | +| - | - | - | - | - | +| Cell 1.0 | Merged Cell 1.1 2.1 | Cell 1.2 | | | +| Cell 2.0 | Merged Cell 1.1 2.1 | Cell 2.2 | | | +| Cell 3.0 | Merged Cell 3.1 4.1 | Cell 3.2 | | | +| Cell 4.0 | Merged Cell 3.1 4.1 | Cell 4.2 | | Merged Cell 4.4 5.4 | +| | | | | Merged Cell 4.4 5.4 | +| | | | | | +| | | | | | +| | | | | Cell 8.4 | diff --git a/crates/fleischwolf/tests/data/html/expected/example_03.html.md b/crates/fleischwolf/tests/data/html/expected/example_03.html.md index 760f1790..c2c74a6c 100644 --- a/crates/fleischwolf/tests/data/html/expected/example_03.html.md +++ b/crates/fleischwolf/tests/data/html/expected/example_03.html.md @@ -20,8 +20,8 @@ Some background information here. ## Data Table -| Header 1 | Header 2 | Header 3 | -|--------------|--------------|--------------| +| Header 1 | Header 2 | Header 3 | +| - | - | - | | Row 1, Col 1 | Row 1, Col 2 | Row 1, Col 3 | | Row 2, Col 1 | Row 2, Col 2 | Row 2, Col 3 | | Row 3, Col 1 | Row 3, Col 2 | Row 3, Col 3 | diff --git a/crates/fleischwolf/tests/data/html/expected/example_03.html.strict.md b/crates/fleischwolf/tests/data/html/expected/example_03.html.strict.md index 760f1790..c2c74a6c 100644 --- a/crates/fleischwolf/tests/data/html/expected/example_03.html.strict.md +++ b/crates/fleischwolf/tests/data/html/expected/example_03.html.strict.md @@ -20,8 +20,8 @@ Some background information here. ## Data Table -| Header 1 | Header 2 | Header 3 | -|--------------|--------------|--------------| +| Header 1 | Header 2 | Header 3 | +| - | - | - | | Row 1, Col 1 | Row 1, Col 2 | Row 1, Col 3 | | Row 2, Col 1 | Row 2, Col 2 | Row 2, Col 3 | | Row 3, Col 1 | Row 3, Col 2 | Row 3, Col 3 | diff --git a/crates/fleischwolf/tests/data/html/expected/example_04.html.md b/crates/fleischwolf/tests/data/html/expected/example_04.html.md index aa307a06..e451b34f 100644 --- a/crates/fleischwolf/tests/data/html/expected/example_04.html.md +++ b/crates/fleischwolf/tests/data/html/expected/example_04.html.md @@ -1,7 +1,7 @@ # Data Table with Rowspan and Colspan -| Header 1 | Header 2 & 3 (colspan) | Header 2 & 3 (colspan) | -|----------------------------|----------------------------|----------------------------| -| Row 1 & 2, Col 1 (rowspan) | Row 1, Col 2 | Row 1, Col 3 | +| Header 1 | Header 2 & 3 (colspan) | Header 2 & 3 (colspan) | +| - | - | - | +| Row 1 & 2, Col 1 (rowspan) | Row 1, Col 2 | Row 1, Col 3 | | Row 1 & 2, Col 1 (rowspan) | Row 2, Col 2 & 3 (colspan) | Row 2, Col 2 & 3 (colspan) | -| Row 3, Col 1 | Row 3, Col 2 | Row 3, Col 3 | +| Row 3, Col 1 | Row 3, Col 2 | Row 3, Col 3 | diff --git a/crates/fleischwolf/tests/data/html/expected/example_04.html.strict.md b/crates/fleischwolf/tests/data/html/expected/example_04.html.strict.md index aa307a06..e451b34f 100644 --- a/crates/fleischwolf/tests/data/html/expected/example_04.html.strict.md +++ b/crates/fleischwolf/tests/data/html/expected/example_04.html.strict.md @@ -1,7 +1,7 @@ # Data Table with Rowspan and Colspan -| Header 1 | Header 2 & 3 (colspan) | Header 2 & 3 (colspan) | -|----------------------------|----------------------------|----------------------------| -| Row 1 & 2, Col 1 (rowspan) | Row 1, Col 2 | Row 1, Col 3 | +| Header 1 | Header 2 & 3 (colspan) | Header 2 & 3 (colspan) | +| - | - | - | +| Row 1 & 2, Col 1 (rowspan) | Row 1, Col 2 | Row 1, Col 3 | | Row 1 & 2, Col 1 (rowspan) | Row 2, Col 2 & 3 (colspan) | Row 2, Col 2 & 3 (colspan) | -| Row 3, Col 1 | Row 3, Col 2 | Row 3, Col 3 | +| Row 3, Col 1 | Row 3, Col 2 | Row 3, Col 3 | diff --git a/crates/fleischwolf/tests/data/html/expected/example_05.html.md b/crates/fleischwolf/tests/data/html/expected/example_05.html.md index e50a5886..64b3c56f 100644 --- a/crates/fleischwolf/tests/data/html/expected/example_05.html.md +++ b/crates/fleischwolf/tests/data/html/expected/example_05.html.md @@ -1,7 +1,7 @@ # Omitted html and body tags -| Header 1 | Header 2 & 3 (colspan) | Header 2 & 3 (colspan) | -|----------------------------|----------------------------|----------------------------| -| Row 1 & 2, Col 1 (rowspan) | Row 1, Col 2 | Row 1, Col 3 | +| Header 1 | Header 2 & 3 (colspan) | Header 2 & 3 (colspan) | +| - | - | - | +| Row 1 & 2, Col 1 (rowspan) | Row 1, Col 2 | Row 1, Col 3 | | Row 1 & 2, Col 1 (rowspan) | Row 2, Col 2 & 3 (colspan) | Row 2, Col 2 & 3 (colspan) | -| Row 3, Col 1 | Row 3, Col 2 | Row 3, Col 3 | +| Row 3, Col 1 | Row 3, Col 2 | Row 3, Col 3 | diff --git a/crates/fleischwolf/tests/data/html/expected/example_05.html.strict.md b/crates/fleischwolf/tests/data/html/expected/example_05.html.strict.md index e50a5886..64b3c56f 100644 --- a/crates/fleischwolf/tests/data/html/expected/example_05.html.strict.md +++ b/crates/fleischwolf/tests/data/html/expected/example_05.html.strict.md @@ -1,7 +1,7 @@ # Omitted html and body tags -| Header 1 | Header 2 & 3 (colspan) | Header 2 & 3 (colspan) | -|----------------------------|----------------------------|----------------------------| -| Row 1 & 2, Col 1 (rowspan) | Row 1, Col 2 | Row 1, Col 3 | +| Header 1 | Header 2 & 3 (colspan) | Header 2 & 3 (colspan) | +| - | - | - | +| Row 1 & 2, Col 1 (rowspan) | Row 1, Col 2 | Row 1, Col 3 | | Row 1 & 2, Col 1 (rowspan) | Row 2, Col 2 & 3 (colspan) | Row 2, Col 2 & 3 (colspan) | -| Row 3, Col 1 | Row 3, Col 2 | Row 3, Col 3 | +| Row 3, Col 1 | Row 3, Col 2 | Row 3, Col 3 | diff --git a/crates/fleischwolf/tests/data/html/expected/example_08.html.md b/crates/fleischwolf/tests/data/html/expected/example_08.html.md index 8911891d..d7b93717 100644 --- a/crates/fleischwolf/tests/data/html/expected/example_08.html.md +++ b/crates/fleischwolf/tests/data/html/expected/example_08.html.md @@ -1,29 +1,29 @@ ## Pivot table with with 1 row header -| Year | Month | Revenue | Cost | -|--------|----------|-----------|--------| -| 2025 | January | $134 | $162 | -| 2025 | February | $150 | $155 | -| 2025 | March | $160 | $143 | -| 2025 | April | $210 | $150 | -| 2025 | May | $280 | $120 | +| Year | Month | Revenue | Cost | +| - | - | - | - | +| 2025 | January | $134 | $162 | +| 2025 | February | $150 | $155 | +| 2025 | March | $160 | $143 | +| 2025 | April | $210 | $150 | +| 2025 | May | $280 | $120 | ## Pivot table with 2 row headers -| Year | Quarter | Month | Revenue | Cost | -|--------|-----------|----------|-----------|--------| -| 2025 | Q1 | January | $134 | $162 | -| 2025 | Q1 | February | $150 | $155 | -| 2025 | Q1 | March | $160 | $143 | -| 2025 | Q2 | April | $210 | $150 | -| 2025 | Q2 | May | $280 | $120 | +| Year | Quarter | Month | Revenue | Cost | +| - | - | - | - | - | +| 2025 | Q1 | January | $134 | $162 | +| 2025 | Q1 | February | $150 | $155 | +| 2025 | Q1 | March | $160 | $143 | +| 2025 | Q2 | April | $210 | $150 | +| 2025 | Q2 | May | $280 | $120 | ## Equivalent pivot table -| Year | Quarter | Month | Revenue | Cost | -|--------|-----------|----------|-----------|--------| -| 2025 | Q1 | January | $134 | $162 | -| 2025 | Q1 | February | $150 | $155 | -| 2025 | Q1 | March | $160 | $143 | -| 2025 | Q2 | April | $210 | $150 | -| 2025 | Q2 | May | $280 | $120 | +| Year | Quarter | Month | Revenue | Cost | +| - | - | - | - | - | +| 2025 | Q1 | January | $134 | $162 | +| 2025 | Q1 | February | $150 | $155 | +| 2025 | Q1 | March | $160 | $143 | +| 2025 | Q2 | April | $210 | $150 | +| 2025 | Q2 | May | $280 | $120 | diff --git a/crates/fleischwolf/tests/data/html/expected/example_08.html.strict.md b/crates/fleischwolf/tests/data/html/expected/example_08.html.strict.md index 8911891d..d7b93717 100644 --- a/crates/fleischwolf/tests/data/html/expected/example_08.html.strict.md +++ b/crates/fleischwolf/tests/data/html/expected/example_08.html.strict.md @@ -1,29 +1,29 @@ ## Pivot table with with 1 row header -| Year | Month | Revenue | Cost | -|--------|----------|-----------|--------| -| 2025 | January | $134 | $162 | -| 2025 | February | $150 | $155 | -| 2025 | March | $160 | $143 | -| 2025 | April | $210 | $150 | -| 2025 | May | $280 | $120 | +| Year | Month | Revenue | Cost | +| - | - | - | - | +| 2025 | January | $134 | $162 | +| 2025 | February | $150 | $155 | +| 2025 | March | $160 | $143 | +| 2025 | April | $210 | $150 | +| 2025 | May | $280 | $120 | ## Pivot table with 2 row headers -| Year | Quarter | Month | Revenue | Cost | -|--------|-----------|----------|-----------|--------| -| 2025 | Q1 | January | $134 | $162 | -| 2025 | Q1 | February | $150 | $155 | -| 2025 | Q1 | March | $160 | $143 | -| 2025 | Q2 | April | $210 | $150 | -| 2025 | Q2 | May | $280 | $120 | +| Year | Quarter | Month | Revenue | Cost | +| - | - | - | - | - | +| 2025 | Q1 | January | $134 | $162 | +| 2025 | Q1 | February | $150 | $155 | +| 2025 | Q1 | March | $160 | $143 | +| 2025 | Q2 | April | $210 | $150 | +| 2025 | Q2 | May | $280 | $120 | ## Equivalent pivot table -| Year | Quarter | Month | Revenue | Cost | -|--------|-----------|----------|-----------|--------| -| 2025 | Q1 | January | $134 | $162 | -| 2025 | Q1 | February | $150 | $155 | -| 2025 | Q1 | March | $160 | $143 | -| 2025 | Q2 | April | $210 | $150 | -| 2025 | Q2 | May | $280 | $120 | +| Year | Quarter | Month | Revenue | Cost | +| - | - | - | - | - | +| 2025 | Q1 | January | $134 | $162 | +| 2025 | Q1 | February | $150 | $155 | +| 2025 | Q1 | March | $160 | $143 | +| 2025 | Q2 | April | $210 | $150 | +| 2025 | Q2 | May | $280 | $120 | diff --git a/crates/fleischwolf/tests/data/html/expected/html_heading_in_p.html.md b/crates/fleischwolf/tests/data/html/expected/html_heading_in_p.html.md index ef9449ae..07ef9d02 100644 --- a/crates/fleischwolf/tests/data/html/expected/html_heading_in_p.html.md +++ b/crates/fleischwolf/tests/data/html/expected/html_heading_in_p.html.md @@ -2,13 +2,13 @@ 1st paragraph -| **2** | **3** | **4** | -|---------|---------|---------| -| 5 | 6 | 7 | -| 8 | 9 | 10 | -| 11 | 12 | 13 | -| 14 | 15 | 16 | -| 17 | 18 | 19 20 | +| **2** | **3** | **4** | +| - | - | - | +| 5 | 6 | 7 | +| 8 | 9 | 10 | +| 11 | 12 | 13 | +| 14 | 15 | 16 | +| 17 | 18 | 19 20 | # 21 diff --git a/crates/fleischwolf/tests/data/html/expected/html_heading_in_p.html.strict.md b/crates/fleischwolf/tests/data/html/expected/html_heading_in_p.html.strict.md index ef9449ae..07ef9d02 100644 --- a/crates/fleischwolf/tests/data/html/expected/html_heading_in_p.html.strict.md +++ b/crates/fleischwolf/tests/data/html/expected/html_heading_in_p.html.strict.md @@ -2,13 +2,13 @@ 1st paragraph -| **2** | **3** | **4** | -|---------|---------|---------| -| 5 | 6 | 7 | -| 8 | 9 | 10 | -| 11 | 12 | 13 | -| 14 | 15 | 16 | -| 17 | 18 | 19 20 | +| **2** | **3** | **4** | +| - | - | - | +| 5 | 6 | 7 | +| 8 | 9 | 10 | +| 11 | 12 | 13 | +| 14 | 15 | 16 | +| 17 | 18 | 19 20 | # 21 diff --git a/crates/fleischwolf/tests/data/html/expected/html_inline_group_in_table_cell.html.md b/crates/fleischwolf/tests/data/html/expected/html_inline_group_in_table_cell.html.md index 9d2f7f1b..a648355d 100644 --- a/crates/fleischwolf/tests/data/html/expected/html_inline_group_in_table_cell.html.md +++ b/crates/fleischwolf/tests/data/html/expected/html_inline_group_in_table_cell.html.md @@ -1,12 +1,12 @@ Intro paragraph. -| Name | Links | -|------------------|--------------------------------------------------------------------------------------| -| Multi-link cell | See [Page A](https://example.com/a) and [Page B](https://example.com/b) for details. | -| Plain cell | Just text | -| Formatted cell | **Bold** and *italic* text | -| Inline code cell | `inline_fn()` | -| Block code cell | ``` do_thing() ``` | -| Pre with link | `See` [`the docs`](https://example.com/docs) `first` | +| Name | Links | +| - | - | +| Multi-link cell | See [Page A](https://example.com/a) and [Page B](https://example.com/b) for details. | +| Plain cell | Just text | +| Formatted cell | **Bold** and *italic* text | +| Inline code cell | `inline_fn()` | +| Block code cell | ``` do_thing() ``` | +| Pre with link | `See` [`the docs`](https://example.com/docs) `first` | Outro paragraph. diff --git a/crates/fleischwolf/tests/data/html/expected/html_inline_group_in_table_cell.html.strict.md b/crates/fleischwolf/tests/data/html/expected/html_inline_group_in_table_cell.html.strict.md index 9d2f7f1b..a648355d 100644 --- a/crates/fleischwolf/tests/data/html/expected/html_inline_group_in_table_cell.html.strict.md +++ b/crates/fleischwolf/tests/data/html/expected/html_inline_group_in_table_cell.html.strict.md @@ -1,12 +1,12 @@ Intro paragraph. -| Name | Links | -|------------------|--------------------------------------------------------------------------------------| -| Multi-link cell | See [Page A](https://example.com/a) and [Page B](https://example.com/b) for details. | -| Plain cell | Just text | -| Formatted cell | **Bold** and *italic* text | -| Inline code cell | `inline_fn()` | -| Block code cell | ``` do_thing() ``` | -| Pre with link | `See` [`the docs`](https://example.com/docs) `first` | +| Name | Links | +| - | - | +| Multi-link cell | See [Page A](https://example.com/a) and [Page B](https://example.com/b) for details. | +| Plain cell | Just text | +| Formatted cell | **Bold** and *italic* text | +| Inline code cell | `inline_fn()` | +| Block code cell | ``` do_thing() ``` | +| Pre with link | `See` [`the docs`](https://example.com/docs) `first` | Outro paragraph. diff --git a/crates/fleischwolf/tests/data/html/expected/html_rich_table_cells.html.md b/crates/fleischwolf/tests/data/html/expected/html_rich_table_cells.html.md index 5daed7ca..a969c9fb 100644 --- a/crates/fleischwolf/tests/data/html/expected/html_rich_table_cells.html.md +++ b/crates/fleischwolf/tests/data/html/expected/html_rich_table_cells.html.md @@ -1,27 +1,27 @@ # Rich Table Cells in HTML -| Name | Habitat | Comment | -|---------------------|----------------------------|------------------------------------------------| -| Wood Duck | | Often seen near ponds. | -| Mallard | Ponds, lakes, rivers | Quack | -| Goose (not a duck!) | Water & wetlands | **Large** , *loud* , noisy , ~~small~~ | -| Teal | - Pond - Marsh - Riverbank | 1. Fly south in winter 2. Build nest on ground | +| Name | Habitat | Comment | +| - | - | - | +| Wood Duck | | Often seen near ponds. | +| Mallard | Ponds, lakes, rivers | Quack | +| Goose (not a duck!) | Water & wetlands | **Large** , *loud* , noisy , ~~small~~ | +| Teal | - Pond - Marsh - Riverbank | 1. Fly south in winter 2. Build nest on ground | -| Genus | Species | -|----------------------------|---------------------------| -| Aythya (Diving ducks) | Hawser, Common Pochard | -| Lophonetta (Pintail group) | Fulvous Whistling Duck | -| Oxyura (Benthic ducks) | Wigee, Banded Water-screw | +| Genus | Species | +| - | - | +| Aythya (Diving ducks) | Hawser, Common Pochard | +| Lophonetta (Pintail group) | Fulvous Whistling Duck | +| Oxyura (Benthic ducks) | Wigee, Banded Water-screw | -| Action | Action | -|----------|-------------------------------------------| -| **Swim** | Gracefully glide on H 2 O surfaces. | -| *Fly* | | -| Quack | Type Sound Short "quak" Long "quaaaaaack" | +| Action | Action | +| - | - | +| **Swim** | Gracefully glide on H 2 O surfaces. | +| *Fly* | | +| Quack | Type Sound Short "quak" Long "quaaaaaack" | -| Name | Description | Image | -|-------------------|----------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| Donald Duck | Cartoon character. | [View PNG](https://en.wikipedia.org/wiki/Donald_Duck#/media/File:Donald_Duck_angry_transparent_background.png) | -| White-headed duck | A small diving duck some 45 cm (18 in) long. | White-headed duck thumbnail | -| Mandarin Duck | Known for its striking plumage. | [View Full-Size Image](https://upload.wikimedia.org/wikipedia/commons/thumb/7/75/Mandarin_duck_%28Aix_galericulata%29.jpg/250px-Mandarin_duck_%28Aix_galericulata%29.jpg) | -| Unknown Duck | No photo available. | | +| Name | Description | Image | +| - | - | - | +| Donald Duck | Cartoon character. | [View PNG](https://en.wikipedia.org/wiki/Donald_Duck#/media/File:Donald_Duck_angry_transparent_background.png) | +| White-headed duck | A small diving duck some 45 cm (18 in) long. | White-headed duck thumbnail | +| Mandarin Duck | Known for its striking plumage. | [View Full-Size Image](https://upload.wikimedia.org/wikipedia/commons/thumb/7/75/Mandarin_duck_%28Aix_galericulata%29.jpg/250px-Mandarin_duck_%28Aix_galericulata%29.jpg) | +| Unknown Duck | No photo available. | | diff --git a/crates/fleischwolf/tests/data/html/expected/html_rich_table_cells.html.strict.md b/crates/fleischwolf/tests/data/html/expected/html_rich_table_cells.html.strict.md index 5daed7ca..a969c9fb 100644 --- a/crates/fleischwolf/tests/data/html/expected/html_rich_table_cells.html.strict.md +++ b/crates/fleischwolf/tests/data/html/expected/html_rich_table_cells.html.strict.md @@ -1,27 +1,27 @@ # Rich Table Cells in HTML -| Name | Habitat | Comment | -|---------------------|----------------------------|------------------------------------------------| -| Wood Duck | | Often seen near ponds. | -| Mallard | Ponds, lakes, rivers | Quack | -| Goose (not a duck!) | Water & wetlands | **Large** , *loud* , noisy , ~~small~~ | -| Teal | - Pond - Marsh - Riverbank | 1. Fly south in winter 2. Build nest on ground | +| Name | Habitat | Comment | +| - | - | - | +| Wood Duck | | Often seen near ponds. | +| Mallard | Ponds, lakes, rivers | Quack | +| Goose (not a duck!) | Water & wetlands | **Large** , *loud* , noisy , ~~small~~ | +| Teal | - Pond - Marsh - Riverbank | 1. Fly south in winter 2. Build nest on ground | -| Genus | Species | -|----------------------------|---------------------------| -| Aythya (Diving ducks) | Hawser, Common Pochard | -| Lophonetta (Pintail group) | Fulvous Whistling Duck | -| Oxyura (Benthic ducks) | Wigee, Banded Water-screw | +| Genus | Species | +| - | - | +| Aythya (Diving ducks) | Hawser, Common Pochard | +| Lophonetta (Pintail group) | Fulvous Whistling Duck | +| Oxyura (Benthic ducks) | Wigee, Banded Water-screw | -| Action | Action | -|----------|-------------------------------------------| -| **Swim** | Gracefully glide on H 2 O surfaces. | -| *Fly* | | -| Quack | Type Sound Short "quak" Long "quaaaaaack" | +| Action | Action | +| - | - | +| **Swim** | Gracefully glide on H 2 O surfaces. | +| *Fly* | | +| Quack | Type Sound Short "quak" Long "quaaaaaack" | -| Name | Description | Image | -|-------------------|----------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| Donald Duck | Cartoon character. | [View PNG](https://en.wikipedia.org/wiki/Donald_Duck#/media/File:Donald_Duck_angry_transparent_background.png) | -| White-headed duck | A small diving duck some 45 cm (18 in) long. | White-headed duck thumbnail | -| Mandarin Duck | Known for its striking plumage. | [View Full-Size Image](https://upload.wikimedia.org/wikipedia/commons/thumb/7/75/Mandarin_duck_%28Aix_galericulata%29.jpg/250px-Mandarin_duck_%28Aix_galericulata%29.jpg) | -| Unknown Duck | No photo available. | | +| Name | Description | Image | +| - | - | - | +| Donald Duck | Cartoon character. | [View PNG](https://en.wikipedia.org/wiki/Donald_Duck#/media/File:Donald_Duck_angry_transparent_background.png) | +| White-headed duck | A small diving duck some 45 cm (18 in) long. | White-headed duck thumbnail | +| Mandarin Duck | Known for its striking plumage. | [View Full-Size Image](https://upload.wikimedia.org/wikipedia/commons/thumb/7/75/Mandarin_duck_%28Aix_galericulata%29.jpg/250px-Mandarin_duck_%28Aix_galericulata%29.jpg) | +| Unknown Duck | No photo available. | | diff --git a/crates/fleischwolf/tests/data/html/expected/kvp_data_example.html.md b/crates/fleischwolf/tests/data/html/expected/kvp_data_example.html.md index 948cb42a..84998e53 100644 --- a/crates/fleischwolf/tests/data/html/expected/kvp_data_example.html.md +++ b/crates/fleischwolf/tests/data/html/expected/kvp_data_example.html.md @@ -40,10 +40,10 @@ Order items: Itemized list of products ordered with quantities. -| Nr. | Item Description | Quantity | -|-------|--------------------|------------| -| I | Coffee | 1 | -| II | Lunch | 2 | -| III | Cake | 1 | +| Nr. | Item Description | Quantity | +| - | - | - | +| I | Coffee | 1 | +| II | Lunch | 2 | +| III | Cake | 1 | Docling Restaurant 2026 diff --git a/crates/fleischwolf/tests/data/html/expected/kvp_data_example.html.strict.md b/crates/fleischwolf/tests/data/html/expected/kvp_data_example.html.strict.md index 948cb42a..84998e53 100644 --- a/crates/fleischwolf/tests/data/html/expected/kvp_data_example.html.strict.md +++ b/crates/fleischwolf/tests/data/html/expected/kvp_data_example.html.strict.md @@ -40,10 +40,10 @@ Order items: Itemized list of products ordered with quantities. -| Nr. | Item Description | Quantity | -|-------|--------------------|------------| -| I | Coffee | 1 | -| II | Lunch | 2 | -| III | Cake | 1 | +| Nr. | Item Description | Quantity | +| - | - | - | +| I | Coffee | 1 | +| II | Lunch | 2 | +| III | Cake | 1 | Docling Restaurant 2026 diff --git a/crates/fleischwolf/tests/data/html/expected/table_01.html.md b/crates/fleischwolf/tests/data/html/expected/table_01.html.md index 734df5d0..ce512c83 100644 --- a/crates/fleischwolf/tests/data/html/expected/table_01.html.md +++ b/crates/fleischwolf/tests/data/html/expected/table_01.html.md @@ -2,8 +2,8 @@ This is the first paragraph. -| A | B | -|------|------| +| A | B | +| - | - | | 1... | 2... | After table diff --git a/crates/fleischwolf/tests/data/html/expected/table_01.html.strict.md b/crates/fleischwolf/tests/data/html/expected/table_01.html.strict.md index 734df5d0..ce512c83 100644 --- a/crates/fleischwolf/tests/data/html/expected/table_01.html.strict.md +++ b/crates/fleischwolf/tests/data/html/expected/table_01.html.strict.md @@ -2,8 +2,8 @@ This is the first paragraph. -| A | B | -|------|------| +| A | B | +| - | - | | 1... | 2... | After table diff --git a/crates/fleischwolf/tests/data/html/expected/table_02.html.md b/crates/fleischwolf/tests/data/html/expected/table_02.html.md index be14a0f3..fe0f99de 100644 --- a/crates/fleischwolf/tests/data/html/expected/table_02.html.md +++ b/crates/fleischwolf/tests/data/html/expected/table_02.html.md @@ -2,8 +2,8 @@ This is the first paragraph. -| A | B | -|-----------------------------------|------| +| A | B | +| - | - | | First Line Second Line Third Line | 2... | After table diff --git a/crates/fleischwolf/tests/data/html/expected/table_02.html.strict.md b/crates/fleischwolf/tests/data/html/expected/table_02.html.strict.md index be14a0f3..fe0f99de 100644 --- a/crates/fleischwolf/tests/data/html/expected/table_02.html.strict.md +++ b/crates/fleischwolf/tests/data/html/expected/table_02.html.strict.md @@ -2,8 +2,8 @@ This is the first paragraph. -| A | B | -|-----------------------------------|------| +| A | B | +| - | - | | First Line Second Line Third Line | 2... | After table diff --git a/crates/fleischwolf/tests/data/html/expected/table_03.html.md b/crates/fleischwolf/tests/data/html/expected/table_03.html.md index e796cddd..3eee6976 100644 --- a/crates/fleischwolf/tests/data/html/expected/table_03.html.md +++ b/crates/fleischwolf/tests/data/html/expected/table_03.html.md @@ -2,8 +2,8 @@ This is the first paragraph. -| A | B | -|-----------------------------------------|------| +| A | B | +| - | - | | - First item - Second item - Third item | 2... | After table diff --git a/crates/fleischwolf/tests/data/html/expected/table_03.html.strict.md b/crates/fleischwolf/tests/data/html/expected/table_03.html.strict.md index e796cddd..3eee6976 100644 --- a/crates/fleischwolf/tests/data/html/expected/table_03.html.strict.md +++ b/crates/fleischwolf/tests/data/html/expected/table_03.html.strict.md @@ -2,8 +2,8 @@ This is the first paragraph. -| A | B | -|-----------------------------------------|------| +| A | B | +| - | - | | - First item - Second item - Third item | 2... | After table diff --git a/crates/fleischwolf/tests/data/html/expected/table_04.html.md b/crates/fleischwolf/tests/data/html/expected/table_04.html.md index d8435dcd..83ee074d 100644 --- a/crates/fleischwolf/tests/data/html/expected/table_04.html.md +++ b/crates/fleischwolf/tests/data/html/expected/table_04.html.md @@ -2,8 +2,8 @@ This is the first paragraph. -| A | B | -|----------------------------------------------------------------|------| +| A | B | +| - | - | | Some text before list - First item - Second item - Third item | 2... | After table diff --git a/crates/fleischwolf/tests/data/html/expected/table_04.html.strict.md b/crates/fleischwolf/tests/data/html/expected/table_04.html.strict.md index d8435dcd..83ee074d 100644 --- a/crates/fleischwolf/tests/data/html/expected/table_04.html.strict.md +++ b/crates/fleischwolf/tests/data/html/expected/table_04.html.strict.md @@ -2,8 +2,8 @@ This is the first paragraph. -| A | B | -|----------------------------------------------------------------|------| +| A | B | +| - | - | | Some text before list - First item - Second item - Third item | 2... | After table diff --git a/crates/fleischwolf/tests/data/html/expected/table_05.html.md b/crates/fleischwolf/tests/data/html/expected/table_05.html.md index f34c894d..6a7a75d6 100644 --- a/crates/fleischwolf/tests/data/html/expected/table_05.html.md +++ b/crates/fleischwolf/tests/data/html/expected/table_05.html.md @@ -2,8 +2,8 @@ This is the first paragraph. -| A | B | -|-------------------|------| +| A | B | +| - | - | | A1 B1 C1 D1 E1 F1 | 2... | After table diff --git a/crates/fleischwolf/tests/data/html/expected/table_05.html.strict.md b/crates/fleischwolf/tests/data/html/expected/table_05.html.strict.md index f34c894d..6a7a75d6 100644 --- a/crates/fleischwolf/tests/data/html/expected/table_05.html.strict.md +++ b/crates/fleischwolf/tests/data/html/expected/table_05.html.strict.md @@ -2,8 +2,8 @@ This is the first paragraph. -| A | B | -|-------------------|------| +| A | B | +| - | - | | A1 B1 C1 D1 E1 F1 | 2... | After table diff --git a/crates/fleischwolf/tests/data/html/expected/table_06.html.md b/crates/fleischwolf/tests/data/html/expected/table_06.html.md index e6207d5a..d3e0c977 100644 --- a/crates/fleischwolf/tests/data/html/expected/table_06.html.md +++ b/crates/fleischwolf/tests/data/html/expected/table_06.html.md @@ -2,8 +2,8 @@ This is the first paragraph. -| A | B | -|---------------------------------------------------|------| +| A | B | +| - | - | | A1 B1 C1 D1 I II III IV V E1 E2 E3 E4 VII VIII F1 | 2... | After table diff --git a/crates/fleischwolf/tests/data/html/expected/table_06.html.strict.md b/crates/fleischwolf/tests/data/html/expected/table_06.html.strict.md index e6207d5a..d3e0c977 100644 --- a/crates/fleischwolf/tests/data/html/expected/table_06.html.strict.md +++ b/crates/fleischwolf/tests/data/html/expected/table_06.html.strict.md @@ -2,8 +2,8 @@ This is the first paragraph. -| A | B | -|---------------------------------------------------|------| +| A | B | +| - | - | | A1 B1 C1 D1 I II III IV V E1 E2 E3 E4 VII VIII F1 | 2... | After table diff --git a/crates/fleischwolf/tests/data/html/expected/table_with_heading_01.html.md b/crates/fleischwolf/tests/data/html/expected/table_with_heading_01.html.md index bf8ebaa3..f54f4652 100644 --- a/crates/fleischwolf/tests/data/html/expected/table_with_heading_01.html.md +++ b/crates/fleischwolf/tests/data/html/expected/table_with_heading_01.html.md @@ -1,7 +1,7 @@ Before tha table -| A | B | -|------|------| +| A | B | +| - | - | | 1... | 2... | After the table diff --git a/crates/fleischwolf/tests/data/html/expected/table_with_heading_01.html.strict.md b/crates/fleischwolf/tests/data/html/expected/table_with_heading_01.html.strict.md index bf8ebaa3..f54f4652 100644 --- a/crates/fleischwolf/tests/data/html/expected/table_with_heading_01.html.strict.md +++ b/crates/fleischwolf/tests/data/html/expected/table_with_heading_01.html.strict.md @@ -1,7 +1,7 @@ Before tha table -| A | B | -|------|------| +| A | B | +| - | - | | 1... | 2... | After the table diff --git a/crates/fleischwolf/tests/data/html/expected/table_with_heading_02.html.md b/crates/fleischwolf/tests/data/html/expected/table_with_heading_02.html.md index 2b95895c..60153bfe 100644 --- a/crates/fleischwolf/tests/data/html/expected/table_with_heading_02.html.md +++ b/crates/fleischwolf/tests/data/html/expected/table_with_heading_02.html.md @@ -2,9 +2,9 @@ Before the table -| ## A text | B | -|--------------|------| -| 1... | 2... | +| ## A text | B | +| - | - | +| 1... | 2... | ## Section After diff --git a/crates/fleischwolf/tests/data/html/expected/table_with_heading_02.html.strict.md b/crates/fleischwolf/tests/data/html/expected/table_with_heading_02.html.strict.md index 2b95895c..60153bfe 100644 --- a/crates/fleischwolf/tests/data/html/expected/table_with_heading_02.html.strict.md +++ b/crates/fleischwolf/tests/data/html/expected/table_with_heading_02.html.strict.md @@ -2,9 +2,9 @@ Before the table -| ## A text | B | -|--------------|------| -| 1... | 2... | +| ## A text | B | +| - | - | +| 1... | 2... | ## Section After diff --git a/crates/fleischwolf/tests/data/html/expected/wiki_duck.html.md b/crates/fleischwolf/tests/data/html/expected/wiki_duck.html.md index 0ea9da23..6e5e303d 100644 --- a/crates/fleischwolf/tests/data/html/expected/wiki_duck.html.md +++ b/crates/fleischwolf/tests/data/html/expected/wiki_duck.html.md @@ -278,20 +278,20 @@ This article is about the bird. For duck as a food, see [Duck as food](/wiki/Duc "Duckling" redirects here. For other uses, see [Duckling (disambiguation)](/wiki/Duckling_(disambiguation)) . -| Duck | Duck | -|-------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------| -| | | -| [Bufflehead](/wiki/Bufflehead) ( *Bucephala albeola* ) | [Bufflehead](/wiki/Bufflehead) ( *Bucephala albeola* ) | +| Duck | Duck | +| - | - | +| | | +| [Bufflehead](/wiki/Bufflehead) ( *Bucephala albeola* ) | [Bufflehead](/wiki/Bufflehead) ( *Bucephala albeola* ) | | [Scientific classification](/wiki/Taxonomy_(biology)) Edit this classification | [Scientific classification](/wiki/Taxonomy_(biology)) Edit this classification | -| Domain: | [Eukaryota](/wiki/Eukaryote) | -| Kingdom: | [Animalia](/wiki/Animal) | -| Phylum: | [Chordata](/wiki/Chordate) | -| Class: | [Aves](/wiki/Bird) | -| Order: | [Anseriformes](/wiki/Anseriformes) | -| Superfamily: | [Anatoidea](/wiki/Anatoidea) | -| Family: | [Anatidae](/wiki/Anatidae) | -| Subfamilies | Subfamilies | -| See text | See text | +| Domain: | [Eukaryota](/wiki/Eukaryote) | +| Kingdom: | [Animalia](/wiki/Animal) | +| Phylum: | [Chordata](/wiki/Chordate) | +| Class: | [Aves](/wiki/Bird) | +| Order: | [Anseriformes](/wiki/Anseriformes) | +| Superfamily: | [Anatoidea](/wiki/Anatoidea) | +| Family: | [Anatidae](/wiki/Anatidae) | +| Subfamilies | Subfamilies | +| See text | See text | **Duck** is the common name for numerous species of [waterfowl](/wiki/Waterfowl) in the [family](/wiki/Family_(biology)) [Anatidae](/wiki/Anatidae) . Ducks are generally smaller and shorter-necked than [swans](/wiki/Swan) and [geese](/wiki/Goose) , which are members of the same family. Divided among several subfamilies, they are a [form taxon](/wiki/Form_taxon) ; they do not represent a [monophyletic group](/wiki/Monophyletic_group) (the group of all descendants of a single common ancestral species), since swans and geese are not considered ducks. Ducks are mostly [aquatic birds](/wiki/Aquatic_bird) , and may be found in both fresh water and sea water. @@ -546,10 +546,10 @@ The 1992 Disney film [*The Mighty Ducks*](/wiki/The_Mighty_Ducks_(film)) , starr - [Ducks on postage stamps](http://www.stampsbook.org/subject/Duck.html) [Archived](https://web.archive.org/web/20130513022903/http://www.stampsbook.org/subject/Duck.html) 2013-05-13 at the [Wayback Machine](/wiki/Wayback_Machine) - [*Ducks at a Distance, by Rob Hines*](https://gutenberg.org/ebooks/18884) at [Project Gutenberg](/wiki/Project_Gutenberg) - A modern illustrated guide to identification of US waterfowl -| [Authority control databases](/wiki/Help:Authority_control) Edit this at Wikidata | [Authority control databases](/wiki/Help:Authority_control) Edit this at Wikidata | -|------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| National | - [United States](https://id.loc.gov/authorities/sh85039879) - [France](https://catalogue.bnf.fr/ark:/12148/cb119761481) - [BnF data](https://data.bnf.fr/ark:/12148/cb119761481) - [Japan](https://id.ndl.go.jp/auth/ndlna/00564819) - [Latvia](https://kopkatalogs.lv/F?func=direct&local_base=lnc10&doc_number=000090751&P_CON_LNG=ENG) - [Israel](http://olduli.nli.org.il/F/?func=find-b&local_base=NLX10&find_code=UID&request=987007565486205171) | -| Other | - [IdRef](https://www.idref.fr/027796124) | +| [Authority control databases](/wiki/Help:Authority_control) Edit this at Wikidata | [Authority control databases](/wiki/Help:Authority_control) Edit this at Wikidata | +| - | - | +| National | - [United States](https://id.loc.gov/authorities/sh85039879) - [France](https://catalogue.bnf.fr/ark:/12148/cb119761481) - [BnF data](https://data.bnf.fr/ark:/12148/cb119761481) - [Japan](https://id.ndl.go.jp/auth/ndlna/00564819) - [Latvia](https://kopkatalogs.lv/F?func=direct&local_base=lnc10&doc_number=000090751&P_CON_LNG=ENG) - [Israel](http://olduli.nli.org.il/F/?func=find-b&local_base=NLX10&find_code=UID&request=987007565486205171) | +| Other | - [IdRef](https://www.idref.fr/027796124) | Retrieved from " [https://en.wikipedia.org/w/index.php?title=Duck&oldid=1246843351](https://en.wikipedia.org/w/index.php?title=Duck&oldid=1246843351) " diff --git a/crates/fleischwolf/tests/data/html/expected/wiki_duck.html.strict.md b/crates/fleischwolf/tests/data/html/expected/wiki_duck.html.strict.md index 9f95910a..baa32ec5 100644 --- a/crates/fleischwolf/tests/data/html/expected/wiki_duck.html.strict.md +++ b/crates/fleischwolf/tests/data/html/expected/wiki_duck.html.strict.md @@ -278,20 +278,20 @@ This article is about the bird. For duck as a food, see [Duck as food](/wiki/Duc "Duckling" redirects here. For other uses, see [Duckling (disambiguation)](/wiki/Duckling_(disambiguation)). -| Duck | Duck | -|-------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------| -| | | -| [Bufflehead](/wiki/Bufflehead) ( *Bucephala albeola* ) | [Bufflehead](/wiki/Bufflehead) ( *Bucephala albeola* ) | +| Duck | Duck | +| - | - | +| | | +| [Bufflehead](/wiki/Bufflehead) ( *Bucephala albeola* ) | [Bufflehead](/wiki/Bufflehead) ( *Bucephala albeola* ) | | [Scientific classification](/wiki/Taxonomy_(biology)) Edit this classification | [Scientific classification](/wiki/Taxonomy_(biology)) Edit this classification | -| Domain: | [Eukaryota](/wiki/Eukaryote) | -| Kingdom: | [Animalia](/wiki/Animal) | -| Phylum: | [Chordata](/wiki/Chordate) | -| Class: | [Aves](/wiki/Bird) | -| Order: | [Anseriformes](/wiki/Anseriformes) | -| Superfamily: | [Anatoidea](/wiki/Anatoidea) | -| Family: | [Anatidae](/wiki/Anatidae) | -| Subfamilies | Subfamilies | -| See text | See text | +| Domain: | [Eukaryota](/wiki/Eukaryote) | +| Kingdom: | [Animalia](/wiki/Animal) | +| Phylum: | [Chordata](/wiki/Chordate) | +| Class: | [Aves](/wiki/Bird) | +| Order: | [Anseriformes](/wiki/Anseriformes) | +| Superfamily: | [Anatoidea](/wiki/Anatoidea) | +| Family: | [Anatidae](/wiki/Anatidae) | +| Subfamilies | Subfamilies | +| See text | See text | **Duck** is the common name for numerous species of [waterfowl](/wiki/Waterfowl) in the [family](/wiki/Family_(biology)) [Anatidae](/wiki/Anatidae). Ducks are generally smaller and shorter-necked than [swans](/wiki/Swan) and [geese](/wiki/Goose), which are members of the same family. Divided among several subfamilies, they are a [form taxon](/wiki/Form_taxon); they do not represent a [monophyletic group](/wiki/Monophyletic_group) (the group of all descendants of a single common ancestral species), since swans and geese are not considered ducks. Ducks are mostly [aquatic birds](/wiki/Aquatic_bird), and may be found in both fresh water and sea water. @@ -546,10 +546,10 @@ The 1992 Disney film [*The Mighty Ducks*](/wiki/The_Mighty_Ducks_(film)), starri - [Ducks on postage stamps](http://www.stampsbook.org/subject/Duck.html) [Archived](https://web.archive.org/web/20130513022903/http://www.stampsbook.org/subject/Duck.html) 2013-05-13 at the [Wayback Machine](/wiki/Wayback_Machine) - [*Ducks at a Distance, by Rob Hines*](https://gutenberg.org/ebooks/18884) at [Project Gutenberg](/wiki/Project_Gutenberg) - A modern illustrated guide to identification of US waterfowl -| [Authority control databases](/wiki/Help:Authority_control) Edit this at Wikidata | [Authority control databases](/wiki/Help:Authority_control) Edit this at Wikidata | -|------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| National | - [United States](https://id.loc.gov/authorities/sh85039879) - [France](https://catalogue.bnf.fr/ark:/12148/cb119761481) - [BnF data](https://data.bnf.fr/ark:/12148/cb119761481) - [Japan](https://id.ndl.go.jp/auth/ndlna/00564819) - [Latvia](https://kopkatalogs.lv/F?func=direct&local_base=lnc10&doc_number=000090751&P_CON_LNG=ENG) - [Israel](http://olduli.nli.org.il/F/?func=find-b&local_base=NLX10&find_code=UID&request=987007565486205171) | -| Other | - [IdRef](https://www.idref.fr/027796124) | +| [Authority control databases](/wiki/Help:Authority_control) Edit this at Wikidata | [Authority control databases](/wiki/Help:Authority_control) Edit this at Wikidata | +| - | - | +| National | - [United States](https://id.loc.gov/authorities/sh85039879) - [France](https://catalogue.bnf.fr/ark:/12148/cb119761481) - [BnF data](https://data.bnf.fr/ark:/12148/cb119761481) - [Japan](https://id.ndl.go.jp/auth/ndlna/00564819) - [Latvia](https://kopkatalogs.lv/F?func=direct&local_base=lnc10&doc_number=000090751&P_CON_LNG=ENG) - [Israel](http://olduli.nli.org.il/F/?func=find-b&local_base=NLX10&find_code=UID&request=987007565486205171) | +| Other | - [IdRef](https://www.idref.fr/027796124) | Retrieved from " [https://en.wikipedia.org/w/index.php?title=Duck&oldid=1246843351](https://en.wikipedia.org/w/index.php?title=Duck&oldid=1246843351) " diff --git a/crates/fleischwolf/tests/data/json_docling/expected/csv-comma.csv.json.md b/crates/fleischwolf/tests/data/json_docling/expected/csv-comma.csv.json.md index bf37c6e7..e7d4dfa3 100644 --- a/crates/fleischwolf/tests/data/json_docling/expected/csv-comma.csv.json.md +++ b/crates/fleischwolf/tests/data/json_docling/expected/csv-comma.csv.json.md @@ -1,7 +1,7 @@ -| Index | Customer Id | First Name | Last Name | Company | City | Country | Phone 1 | Phone 2 | Email | Subscription Date | Website | -|---------|-----------------|--------------|-------------|---------------------------------|-------------------|----------------------------|------------------------|-----------------------|-----------------------------|---------------------|-----------------------------| -| 1 | DD37Cf93aecA6Dc | Sheryl | Baxter | Rasmussen Group | East Leonard | Chile | 229.077.5154 | 397.884.0519x718 | zunigavanessa@smith.info | 2020-08-24 | http://www.stephenson.com/ | -| 2 | 1Ef7b82A4CAAD10 | Preston | Lozano, Dr | Vega-Gentry | East Jimmychester | Djibouti | 5153435776 | 686-620-1820x944 | vmata@colon.com | 2021-04-23 | http://www.hobbs.com/ | -| 3 | 6F94879bDAfE5a6 | Roy | Berry | Murillo-Perry | Isabelborough | Antigua and Barbuda | +1-539-402-0259 | (496)978-3969x58947 | beckycarr@hogan.com | 2020-03-25 | http://www.lawrence.com/ | -| 4 | 5Cef8BFA16c5e3c | Linda | Olsen | Dominguez, Mcmillan and Donovan | Bensonview | Dominican Republic | 001-808-617-6467x12895 | +1-813-324-8756 | stanleyblackwell@benson.org | 2020-06-02 | http://www.good-lyons.com/ | -| 5 | 053d585Ab6b3159 | Joanna | Bender | Martin, Lang and Andrade | West Priscilla | Slovakia (Slovak Republic) | 001-234-203-0635x76146 | 001-199-446-3860x3486 | colinalvarado@miles.net | 2021-04-17 | https://goodwin-ingram.com/ | +| Index | Customer Id | First Name | Last Name | Company | City | Country | Phone 1 | Phone 2 | Email | Subscription Date | Website | +| - | - | - | - | - | - | - | - | - | - | - | - | +| 1 | DD37Cf93aecA6Dc | Sheryl | Baxter | Rasmussen Group | East Leonard | Chile | 229.077.5154 | 397.884.0519x718 | zunigavanessa@smith.info | 2020-08-24 | http://www.stephenson.com/ | +| 2 | 1Ef7b82A4CAAD10 | Preston | Lozano, Dr | Vega-Gentry | East Jimmychester | Djibouti | 5153435776 | 686-620-1820x944 | vmata@colon.com | 2021-04-23 | http://www.hobbs.com/ | +| 3 | 6F94879bDAfE5a6 | Roy | Berry | Murillo-Perry | Isabelborough | Antigua and Barbuda | +1-539-402-0259 | (496)978-3969x58947 | beckycarr@hogan.com | 2020-03-25 | http://www.lawrence.com/ | +| 4 | 5Cef8BFA16c5e3c | Linda | Olsen | Dominguez, Mcmillan and Donovan | Bensonview | Dominican Republic | 001-808-617-6467x12895 | +1-813-324-8756 | stanleyblackwell@benson.org | 2020-06-02 | http://www.good-lyons.com/ | +| 5 | 053d585Ab6b3159 | Joanna | Bender | Martin, Lang and Andrade | West Priscilla | Slovakia (Slovak Republic) | 001-234-203-0635x76146 | 001-199-446-3860x3486 | colinalvarado@miles.net | 2021-04-17 | https://goodwin-ingram.com/ | diff --git a/crates/fleischwolf/tests/data/json_docling/expected/csv-comma.csv.json.strict.md b/crates/fleischwolf/tests/data/json_docling/expected/csv-comma.csv.json.strict.md index bf37c6e7..e7d4dfa3 100644 --- a/crates/fleischwolf/tests/data/json_docling/expected/csv-comma.csv.json.strict.md +++ b/crates/fleischwolf/tests/data/json_docling/expected/csv-comma.csv.json.strict.md @@ -1,7 +1,7 @@ -| Index | Customer Id | First Name | Last Name | Company | City | Country | Phone 1 | Phone 2 | Email | Subscription Date | Website | -|---------|-----------------|--------------|-------------|---------------------------------|-------------------|----------------------------|------------------------|-----------------------|-----------------------------|---------------------|-----------------------------| -| 1 | DD37Cf93aecA6Dc | Sheryl | Baxter | Rasmussen Group | East Leonard | Chile | 229.077.5154 | 397.884.0519x718 | zunigavanessa@smith.info | 2020-08-24 | http://www.stephenson.com/ | -| 2 | 1Ef7b82A4CAAD10 | Preston | Lozano, Dr | Vega-Gentry | East Jimmychester | Djibouti | 5153435776 | 686-620-1820x944 | vmata@colon.com | 2021-04-23 | http://www.hobbs.com/ | -| 3 | 6F94879bDAfE5a6 | Roy | Berry | Murillo-Perry | Isabelborough | Antigua and Barbuda | +1-539-402-0259 | (496)978-3969x58947 | beckycarr@hogan.com | 2020-03-25 | http://www.lawrence.com/ | -| 4 | 5Cef8BFA16c5e3c | Linda | Olsen | Dominguez, Mcmillan and Donovan | Bensonview | Dominican Republic | 001-808-617-6467x12895 | +1-813-324-8756 | stanleyblackwell@benson.org | 2020-06-02 | http://www.good-lyons.com/ | -| 5 | 053d585Ab6b3159 | Joanna | Bender | Martin, Lang and Andrade | West Priscilla | Slovakia (Slovak Republic) | 001-234-203-0635x76146 | 001-199-446-3860x3486 | colinalvarado@miles.net | 2021-04-17 | https://goodwin-ingram.com/ | +| Index | Customer Id | First Name | Last Name | Company | City | Country | Phone 1 | Phone 2 | Email | Subscription Date | Website | +| - | - | - | - | - | - | - | - | - | - | - | - | +| 1 | DD37Cf93aecA6Dc | Sheryl | Baxter | Rasmussen Group | East Leonard | Chile | 229.077.5154 | 397.884.0519x718 | zunigavanessa@smith.info | 2020-08-24 | http://www.stephenson.com/ | +| 2 | 1Ef7b82A4CAAD10 | Preston | Lozano, Dr | Vega-Gentry | East Jimmychester | Djibouti | 5153435776 | 686-620-1820x944 | vmata@colon.com | 2021-04-23 | http://www.hobbs.com/ | +| 3 | 6F94879bDAfE5a6 | Roy | Berry | Murillo-Perry | Isabelborough | Antigua and Barbuda | +1-539-402-0259 | (496)978-3969x58947 | beckycarr@hogan.com | 2020-03-25 | http://www.lawrence.com/ | +| 4 | 5Cef8BFA16c5e3c | Linda | Olsen | Dominguez, Mcmillan and Donovan | Bensonview | Dominican Republic | 001-808-617-6467x12895 | +1-813-324-8756 | stanleyblackwell@benson.org | 2020-06-02 | http://www.good-lyons.com/ | +| 5 | 053d585Ab6b3159 | Joanna | Bender | Martin, Lang and Andrade | West Priscilla | Slovakia (Slovak Republic) | 001-234-203-0635x76146 | 001-199-446-3860x3486 | colinalvarado@miles.net | 2021-04-17 | https://goodwin-ingram.com/ | diff --git a/crates/fleischwolf/tests/data/latex/expected/example_02.tex.md b/crates/fleischwolf/tests/data/latex/expected/example_02.tex.md index 5c02932d..0c3fce03 100644 --- a/crates/fleischwolf/tests/data/latex/expected/example_02.tex.md +++ b/crates/fleischwolf/tests/data/latex/expected/example_02.tex.md @@ -12,10 +12,10 @@ $$\nabla \times \mathbf{E} = -\frac{\partial \mathbf{B}}{\partial t}$$ ## Table Example -| Name | Age | City | -|--------|-------|---------| -| Alice | 25 | Boston | -| Bob | 30 | Seattle | +| Name | Age | City | +| - | - | - | +| Alice | 25 | Boston | +| Bob | 30 | Seattle | Sample table diff --git a/crates/fleischwolf/tests/data/latex/expected/example_02.tex.strict.md b/crates/fleischwolf/tests/data/latex/expected/example_02.tex.strict.md index 5c02932d..0c3fce03 100644 --- a/crates/fleischwolf/tests/data/latex/expected/example_02.tex.strict.md +++ b/crates/fleischwolf/tests/data/latex/expected/example_02.tex.strict.md @@ -12,10 +12,10 @@ $$\nabla \times \mathbf{E} = -\frac{\partial \mathbf{B}}{\partial t}$$ ## Table Example -| Name | Age | City | -|--------|-------|---------| -| Alice | 25 | Boston | -| Bob | 30 | Seattle | +| Name | Age | City | +| - | - | - | +| Alice | 25 | Boston | +| Bob | 30 | Seattle | Sample table diff --git a/crates/fleischwolf/tests/data/md/expected/duck.md.md b/crates/fleischwolf/tests/data/md/expected/duck.md.md index 2a8d1efb..d148a8b5 100644 --- a/crates/fleischwolf/tests/data/md/expected/duck.md.md +++ b/crates/fleischwolf/tests/data/md/expected/duck.md.md @@ -30,11 +30,11 @@ I like to eat leaves Here are some interesting things a respectful duck could eat: -| | Food | Calories per portion | -|---------|----------------------------------|------------------------| -| Leaves | Ash, Elm, Maple | 50 | -| Berries | Blueberry, Strawberry, Cranberry | 150 | -| Grain | Corn, Buckwheat, Barley | 200 | +| | Food | Calories per portion | +| - | - | - | +| Leaves | Ash, Elm, Maple | 50 | +| Berries | Blueberry, Strawberry, Cranberry | 150 | +| Grain | Corn, Buckwheat, Barley | 200 | And let’s add another list in the end: diff --git a/crates/fleischwolf/tests/data/md/expected/duck.md.strict.md b/crates/fleischwolf/tests/data/md/expected/duck.md.strict.md index 2a8d1efb..d148a8b5 100644 --- a/crates/fleischwolf/tests/data/md/expected/duck.md.strict.md +++ b/crates/fleischwolf/tests/data/md/expected/duck.md.strict.md @@ -30,11 +30,11 @@ I like to eat leaves Here are some interesting things a respectful duck could eat: -| | Food | Calories per portion | -|---------|----------------------------------|------------------------| -| Leaves | Ash, Elm, Maple | 50 | -| Berries | Blueberry, Strawberry, Cranberry | 150 | -| Grain | Corn, Buckwheat, Barley | 200 | +| | Food | Calories per portion | +| - | - | - | +| Leaves | Ash, Elm, Maple | 50 | +| Berries | Blueberry, Strawberry, Cranberry | 150 | +| Grain | Corn, Buckwheat, Barley | 200 | And let’s add another list in the end: diff --git a/crates/fleischwolf/tests/data/md/expected/ending_with_table.md.md b/crates/fleischwolf/tests/data/md/expected/ending_with_table.md.md index 9c179fe0..55a5ec5a 100644 --- a/crates/fleischwolf/tests/data/md/expected/ending_with_table.md.md +++ b/crates/fleischwolf/tests/data/md/expected/ending_with_table.md.md @@ -1,6 +1,6 @@ -| Character | Name in German | Name in French | Name in Italian | -|----------------|------------------|------------------|-------------------| -| Scrooge McDuck | Dagobert Duck | Balthazar Picsou | Paperone | -| Huey | Tick | Riri | Qui | -| Dewey | Trick | Fifi | Quo | -| Louie | Track | Loulou | Qua | +| Character | Name in German | Name in French | Name in Italian | +| - | - | - | - | +| Scrooge McDuck | Dagobert Duck | Balthazar Picsou | Paperone | +| Huey | Tick | Riri | Qui | +| Dewey | Trick | Fifi | Quo | +| Louie | Track | Loulou | Qua | diff --git a/crates/fleischwolf/tests/data/md/expected/ending_with_table.md.strict.md b/crates/fleischwolf/tests/data/md/expected/ending_with_table.md.strict.md index 9c179fe0..55a5ec5a 100644 --- a/crates/fleischwolf/tests/data/md/expected/ending_with_table.md.strict.md +++ b/crates/fleischwolf/tests/data/md/expected/ending_with_table.md.strict.md @@ -1,6 +1,6 @@ -| Character | Name in German | Name in French | Name in Italian | -|----------------|------------------|------------------|-------------------| -| Scrooge McDuck | Dagobert Duck | Balthazar Picsou | Paperone | -| Huey | Tick | Riri | Qui | -| Dewey | Trick | Fifi | Quo | -| Louie | Track | Loulou | Qua | +| Character | Name in German | Name in French | Name in Italian | +| - | - | - | - | +| Scrooge McDuck | Dagobert Duck | Balthazar Picsou | Paperone | +| Huey | Tick | Riri | Qui | +| Dewey | Trick | Fifi | Quo | +| Louie | Track | Loulou | Qua | diff --git a/crates/fleischwolf/tests/data/md/expected/escaped_characters.md.md b/crates/fleischwolf/tests/data/md/expected/escaped_characters.md.md index 95bdd09a..84f40db5 100644 --- a/crates/fleischwolf/tests/data/md/expected/escaped_characters.md.md +++ b/crates/fleischwolf/tests/data/md/expected/escaped_characters.md.md @@ -24,14 +24,14 @@ Text: 00:16.000 ----> 00:18.000 & < > " ' # Table -| Key | Example | -|--------------|----------------------| -| Ampersand | & | -| Less-than | < | -| Greater-than | > | -| Quotes | " | -| Apostrophes | ' | -| Pipe | | | | | +| Key | Example | +| - | - | +| Ampersand | & | +| Less-than | < | +| Greater-than | > | +| Quotes | " | +| Apostrophes | ' | +| Pipe | | | | | The pipe symbol (| or `|` ) only needs to be escaped in tables. diff --git a/crates/fleischwolf/tests/data/md/expected/escaped_characters.md.strict.md b/crates/fleischwolf/tests/data/md/expected/escaped_characters.md.strict.md index e0a6e888..02502d65 100644 --- a/crates/fleischwolf/tests/data/md/expected/escaped_characters.md.strict.md +++ b/crates/fleischwolf/tests/data/md/expected/escaped_characters.md.strict.md @@ -22,14 +22,14 @@ Text: 00:16.000 ----> 00:18.000 & < > " ' # Table -| Key | Example | -|--------------|----------------------| -| Ampersand | & | -| Less-than | < | -| Greater-than | > | -| Quotes | " | -| Apostrophes | ' | -| Pipe | | | | | +| Key | Example | +| - | - | +| Ampersand | & | +| Less-than | < | +| Greater-than | > | +| Quotes | " | +| Apostrophes | ' | +| Pipe | | | | | The pipe symbol (| or `|`) only needs to be escaped in tables. diff --git a/crates/fleischwolf/tests/data/md/expected/inline_and_formatting.md.md b/crates/fleischwolf/tests/data/md/expected/inline_and_formatting.md.md index 282be7f5..9051b9a4 100644 --- a/crates/fleischwolf/tests/data/md/expected/inline_and_formatting.md.md +++ b/crates/fleischwolf/tests/data/md/expected/inline_and_formatting.md.md @@ -27,6 +27,6 @@ Some *`formatted_code`* ## Table Heading -| Bold Heading | Italic Heading | -|----------------|------------------| -| data a | data b | +| Bold Heading | Italic Heading | +| - | - | +| data a | data b | diff --git a/crates/fleischwolf/tests/data/md/expected/inline_and_formatting.md.strict.md b/crates/fleischwolf/tests/data/md/expected/inline_and_formatting.md.strict.md index 30b769f9..07d680f1 100644 --- a/crates/fleischwolf/tests/data/md/expected/inline_and_formatting.md.strict.md +++ b/crates/fleischwolf/tests/data/md/expected/inline_and_formatting.md.strict.md @@ -27,6 +27,6 @@ Some *`formatted_code`* ## Table Heading -| Bold Heading | Italic Heading | -|----------------|------------------| -| data a | data b | +| Bold Heading | Italic Heading | +| - | - | +| data a | data b | diff --git a/crates/fleischwolf/tests/data/md/expected/mixed.md.md b/crates/fleischwolf/tests/data/md/expected/mixed.md.md index 5524e09d..4db57e0e 100644 --- a/crates/fleischwolf/tests/data/md/expected/mixed.md.md +++ b/crates/fleischwolf/tests/data/md/expected/mixed.md.md @@ -6,12 +6,12 @@ Some text Here is a table: -| Character | Name in German | Name in French | Name in Italian | -|----------------|------------------|------------------|-------------------| -| Scrooge McDuck | Dagobert Duck | Balthazar Picsou | Paperone | -| Huey | Tick | Riri | Qui | -| Dewey | Trick | Fifi | Quo | -| Louie | Track | Loulou | Qua | +| Character | Name in German | Name in French | Name in Italian | +| - | - | - | - | +| Scrooge McDuck | Dagobert Duck | Balthazar Picsou | Paperone | +| Huey | Tick | Riri | Qui | +| Dewey | Trick | Fifi | Quo | +| Louie | Track | Loulou | Qua | And here is more HTML: diff --git a/crates/fleischwolf/tests/data/md/expected/mixed.md.strict.md b/crates/fleischwolf/tests/data/md/expected/mixed.md.strict.md index 5524e09d..4db57e0e 100644 --- a/crates/fleischwolf/tests/data/md/expected/mixed.md.strict.md +++ b/crates/fleischwolf/tests/data/md/expected/mixed.md.strict.md @@ -6,12 +6,12 @@ Some text Here is a table: -| Character | Name in German | Name in French | Name in Italian | -|----------------|------------------|------------------|-------------------| -| Scrooge McDuck | Dagobert Duck | Balthazar Picsou | Paperone | -| Huey | Tick | Riri | Qui | -| Dewey | Trick | Fifi | Quo | -| Louie | Track | Loulou | Qua | +| Character | Name in German | Name in French | Name in Italian | +| - | - | - | - | +| Scrooge McDuck | Dagobert Duck | Balthazar Picsou | Paperone | +| Huey | Tick | Riri | Qui | +| Dewey | Trick | Fifi | Quo | +| Louie | Track | Loulou | Qua | And here is more HTML: diff --git a/crates/fleischwolf/tests/data/md_deepseek/expected/deepseek_example.md.md b/crates/fleischwolf/tests/data/md_deepseek/expected/deepseek_example.md.md index 8a4342b7..3c2ae446 100644 --- a/crates/fleischwolf/tests/data/md_deepseek/expected/deepseek_example.md.md +++ b/crates/fleischwolf/tests/data/md_deepseek/expected/deepseek_example.md.md @@ -4,17 +4,17 @@ We have chosen the PubTabNet data set to perform HPO, since it includes a highly Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer 9 architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart. -| # enc-layers | # dec-layers | Language | TEDs | TEDs | TEDs | mAP (0.75) | Inference time (secs) | -|----------------|----------------|------------|--------|---------|--------|--------------|-------------------------| -| # enc-layers | # dec-layers | Language | simple | complex | all | mAP (0.75) | Inference time (secs) | -| 6 | 6 | OTSL | 0.965 | 0.934 | 0.955 | 0.88 | 2.73 | -| 6 | 6 | HTML | 0.969 | 0.927 | 0.955 | 0.857 | 5.39 | -| 4 | 4 | OTSL | 0.938 | 0.904 | 0.927 | 0.853 | 1.97 | -| 4 | 4 | HTML | 0.952 | 0.909 | 0.938 | 0.843 | 3.77 | -| 2 | 4 | OTSL | 0.923 | 0.897 | 0.915 | 0.859 | 1.91 | -| 2 | 4 | HTML | 0.945 | 0.901 | 0.931 | 0.834 | 3.81 | -| 4 | 2 | OTSL | 0.952 | 0.92 | 0.942 | 0.857 | 1.22 | -| 4 | 2 | HTML | 0.944 | 0.903 | 0.931 | 0.824 | 2 | +| # enc-layers | # dec-layers | Language | TEDs | TEDs | TEDs | mAP (0.75) | Inference time (secs) | +| - | - | - | - | - | - | - | - | +| # enc-layers | # dec-layers | Language | simple | complex | all | mAP (0.75) | Inference time (secs) | +| 6 | 6 | OTSL | 0.965 | 0.934 | 0.955 | 0.88 | 2.73 | +| 6 | 6 | HTML | 0.969 | 0.927 | 0.955 | 0.857 | 5.39 | +| 4 | 4 | OTSL | 0.938 | 0.904 | 0.927 | 0.853 | 1.97 | +| 4 | 4 | HTML | 0.952 | 0.909 | 0.938 | 0.843 | 3.77 | +| 2 | 4 | OTSL | 0.923 | 0.897 | 0.915 | 0.859 | 1.91 | +| 2 | 4 | HTML | 0.945 | 0.901 | 0.931 | 0.834 | 3.81 | +| 4 | 2 | OTSL | 0.952 | 0.92 | 0.942 | 0.857 | 1.22 | +| 4 | 2 | HTML | 0.944 | 0.903 | 0.931 | 0.824 | 2 | ### 5.2 Quantitative Results diff --git a/crates/fleischwolf/tests/data/md_deepseek/expected/deepseek_example.md.strict.md b/crates/fleischwolf/tests/data/md_deepseek/expected/deepseek_example.md.strict.md index 8a4342b7..3c2ae446 100644 --- a/crates/fleischwolf/tests/data/md_deepseek/expected/deepseek_example.md.strict.md +++ b/crates/fleischwolf/tests/data/md_deepseek/expected/deepseek_example.md.strict.md @@ -4,17 +4,17 @@ We have chosen the PubTabNet data set to perform HPO, since it includes a highly Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer 9 architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart. -| # enc-layers | # dec-layers | Language | TEDs | TEDs | TEDs | mAP (0.75) | Inference time (secs) | -|----------------|----------------|------------|--------|---------|--------|--------------|-------------------------| -| # enc-layers | # dec-layers | Language | simple | complex | all | mAP (0.75) | Inference time (secs) | -| 6 | 6 | OTSL | 0.965 | 0.934 | 0.955 | 0.88 | 2.73 | -| 6 | 6 | HTML | 0.969 | 0.927 | 0.955 | 0.857 | 5.39 | -| 4 | 4 | OTSL | 0.938 | 0.904 | 0.927 | 0.853 | 1.97 | -| 4 | 4 | HTML | 0.952 | 0.909 | 0.938 | 0.843 | 3.77 | -| 2 | 4 | OTSL | 0.923 | 0.897 | 0.915 | 0.859 | 1.91 | -| 2 | 4 | HTML | 0.945 | 0.901 | 0.931 | 0.834 | 3.81 | -| 4 | 2 | OTSL | 0.952 | 0.92 | 0.942 | 0.857 | 1.22 | -| 4 | 2 | HTML | 0.944 | 0.903 | 0.931 | 0.824 | 2 | +| # enc-layers | # dec-layers | Language | TEDs | TEDs | TEDs | mAP (0.75) | Inference time (secs) | +| - | - | - | - | - | - | - | - | +| # enc-layers | # dec-layers | Language | simple | complex | all | mAP (0.75) | Inference time (secs) | +| 6 | 6 | OTSL | 0.965 | 0.934 | 0.955 | 0.88 | 2.73 | +| 6 | 6 | HTML | 0.969 | 0.927 | 0.955 | 0.857 | 5.39 | +| 4 | 4 | OTSL | 0.938 | 0.904 | 0.927 | 0.853 | 1.97 | +| 4 | 4 | HTML | 0.952 | 0.909 | 0.938 | 0.843 | 3.77 | +| 2 | 4 | OTSL | 0.923 | 0.897 | 0.915 | 0.859 | 1.91 | +| 2 | 4 | HTML | 0.945 | 0.901 | 0.931 | 0.834 | 3.81 | +| 4 | 2 | OTSL | 0.952 | 0.92 | 0.942 | 0.857 | 1.22 | +| 4 | 2 | HTML | 0.944 | 0.903 | 0.931 | 0.824 | 2 | ### 5.2 Quantitative Results diff --git a/crates/fleischwolf/tests/data/odf/expected/odf_presentation_02.odp.md b/crates/fleischwolf/tests/data/odf/expected/odf_presentation_02.odp.md index 38f12a3d..0d2a5619 100644 --- a/crates/fleischwolf/tests/data/odf/expected/odf_presentation_02.odp.md +++ b/crates/fleischwolf/tests/data/odf/expected/odf_presentation_02.odp.md @@ -1,8 +1,8 @@ -| Column 1 | Column 2 | Column 3 | Column 4 | Column 5 | -|------------|-------------------------------|-------------------------------|------------|------------| -| Row 1 | Merged cells row 1-3, col 2-3 | Merged cells row 1-3, col 2-3 | | | -| Row 2 | | | | | -| Row 3 | | | | | +| Column 1 | Column 2 | Column 3 | Column 4 | Column 5 | +| - | - | - | - | - | +| Row 1 | Merged cells row 1-3, col 2-3 | Merged cells row 1-3, col 2-3 | | | +| Row 2 | | | | | +| Row 3 | | | | | Complex list diff --git a/crates/fleischwolf/tests/data/odf/expected/odf_presentation_02.odp.strict.md b/crates/fleischwolf/tests/data/odf/expected/odf_presentation_02.odp.strict.md index 38f12a3d..0d2a5619 100644 --- a/crates/fleischwolf/tests/data/odf/expected/odf_presentation_02.odp.strict.md +++ b/crates/fleischwolf/tests/data/odf/expected/odf_presentation_02.odp.strict.md @@ -1,8 +1,8 @@ -| Column 1 | Column 2 | Column 3 | Column 4 | Column 5 | -|------------|-------------------------------|-------------------------------|------------|------------| -| Row 1 | Merged cells row 1-3, col 2-3 | Merged cells row 1-3, col 2-3 | | | -| Row 2 | | | | | -| Row 3 | | | | | +| Column 1 | Column 2 | Column 3 | Column 4 | Column 5 | +| - | - | - | - | - | +| Row 1 | Merged cells row 1-3, col 2-3 | Merged cells row 1-3, col 2-3 | | | +| Row 2 | | | | | +| Row 3 | | | | | Complex list diff --git a/crates/fleischwolf/tests/data/odf/expected/odf_table_with_title_01.ods.md b/crates/fleischwolf/tests/data/odf/expected/odf_table_with_title_01.ods.md index cde353ea..e143a6e5 100644 --- a/crates/fleischwolf/tests/data/odf/expected/odf_table_with_title_01.ods.md +++ b/crates/fleischwolf/tests/data/odf/expected/odf_table_with_title_01.ods.md @@ -1,9 +1,9 @@ -| | Number of freshwater ducks per year | | -|----|---------------------------------------|------------------| -| | Year | Freshwater Ducks | -| | 2019 | 120 | -| | 2020 | 135 | -| | 2021 | 150 | -| | 2022 | 170 | -| | 2023 | 160 | -| | 2024 | 180 | +| | Number of freshwater ducks per year | | +| - | - | - | +| | Year | Freshwater Ducks | +| | 2019 | 120 | +| | 2020 | 135 | +| | 2021 | 150 | +| | 2022 | 170 | +| | 2023 | 160 | +| | 2024 | 180 | diff --git a/crates/fleischwolf/tests/data/odf/expected/odf_table_with_title_01.ods.strict.md b/crates/fleischwolf/tests/data/odf/expected/odf_table_with_title_01.ods.strict.md index cde353ea..e143a6e5 100644 --- a/crates/fleischwolf/tests/data/odf/expected/odf_table_with_title_01.ods.strict.md +++ b/crates/fleischwolf/tests/data/odf/expected/odf_table_with_title_01.ods.strict.md @@ -1,9 +1,9 @@ -| | Number of freshwater ducks per year | | -|----|---------------------------------------|------------------| -| | Year | Freshwater Ducks | -| | 2019 | 120 | -| | 2020 | 135 | -| | 2021 | 150 | -| | 2022 | 170 | -| | 2023 | 160 | -| | 2024 | 180 | +| | Number of freshwater ducks per year | | +| - | - | - | +| | Year | Freshwater Ducks | +| | 2019 | 120 | +| | 2020 | 135 | +| | 2021 | 150 | +| | 2022 | 170 | +| | 2023 | 160 | +| | 2024 | 180 | diff --git a/crates/fleischwolf/tests/data/odf/expected/text_document_02.odt.md b/crates/fleischwolf/tests/data/odf/expected/text_document_02.odt.md index 3356d746..ffec2689 100644 --- a/crates/fleischwolf/tests/data/odf/expected/text_document_02.odt.md +++ b/crates/fleischwolf/tests/data/odf/expected/text_document_02.odt.md @@ -8,6 +8,6 @@ Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lore Contrary to popular belief, Lorem Ipsum is not simply random text. It has roots in a piece of classical Latin literature from 45 BC, making it over 2000 years old. -| Merged row | Merged row | Cell 1.3 | Cell 1.4 | Cell 1.5 | Cell 1.6 | Cell 1.7 | -|--------------|--------------|-----------------|-----------------|------------|---------------|------------| -| | | Merged cell 2x2 | Merged cell 2x2 | | Merged column | | +| Merged row | Merged row | Cell 1.3 | Cell 1.4 | Cell 1.5 | Cell 1.6 | Cell 1.7 | +| - | - | - | - | - | - | - | +| | | Merged cell 2x2 | Merged cell 2x2 | | Merged column | | diff --git a/crates/fleischwolf/tests/data/odf/expected/text_document_02.odt.strict.md b/crates/fleischwolf/tests/data/odf/expected/text_document_02.odt.strict.md index 3356d746..ffec2689 100644 --- a/crates/fleischwolf/tests/data/odf/expected/text_document_02.odt.strict.md +++ b/crates/fleischwolf/tests/data/odf/expected/text_document_02.odt.strict.md @@ -8,6 +8,6 @@ Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lore Contrary to popular belief, Lorem Ipsum is not simply random text. It has roots in a piece of classical Latin literature from 45 BC, making it over 2000 years old. -| Merged row | Merged row | Cell 1.3 | Cell 1.4 | Cell 1.5 | Cell 1.6 | Cell 1.7 | -|--------------|--------------|-----------------|-----------------|------------|---------------|------------| -| | | Merged cell 2x2 | Merged cell 2x2 | | Merged column | | +| Merged row | Merged row | Cell 1.3 | Cell 1.4 | Cell 1.5 | Cell 1.6 | Cell 1.7 | +| - | - | - | - | - | - | - | +| | | Merged cell 2x2 | Merged cell 2x2 | | Merged column | | diff --git a/crates/fleischwolf/tests/data/odf/expected/text_document_03.odt.md b/crates/fleischwolf/tests/data/odf/expected/text_document_03.odt.md index ed14d009..87a48e6e 100644 --- a/crates/fleischwolf/tests/data/odf/expected/text_document_03.odt.md +++ b/crates/fleischwolf/tests/data/odf/expected/text_document_03.odt.md @@ -1,32 +1,32 @@ ### Table with rich cells -| Column A | Column B | -|---------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------| -| This is a list: | This is a formatted list: | +| Column A | Column B | +| - | - | +| This is a list: | This is a formatted list: | | First Paragraph Second Paragraph Third paragraph before a numbered list | This is simple text with **bold** , ~~strikethrough~~ and *italic* formatting with x 2 and H 2 O | -| This is a paragraph This is another paragraph | | +| This is a paragraph This is another paragraph | | ### Table with nested table Before table -| Column A | Column B | | -|------------------------|-------------------------------------------------|--------| -| Simple cell upper left | Simple cell with **bold** and *italic* text | | -| | Rich cell A nested table | | -| A | B | C | -| *Cell 1* | Cell 2 | Cell 3 | -| A | B | C | -| ~~Cell 1~~ | Cell 2 | Cell 3 | +| Column A | Column B | | +| - | - | - | +| Simple cell upper left | Simple cell with **bold** and *italic* text | | +| | Rich cell A nested table | | +| A | B | C | +| *Cell 1* | Cell 2 | Cell 3 | +| A | B | C | +| ~~Cell 1~~ | Cell 2 | Cell 3 | After table with **bold** , underline , ~~strikethrough~~ , and *italic* formatting ### Table with pictures -| Column A | Column B | -|------------------|------------| -| Only text | | -| Text and picture | | +| Column A | Column B | +| - | - | +| Only text | | +| Text and picture | | ### Lists with same numId in different cells @@ -36,5 +36,5 @@ After table with **bold** , underline , ~~strikethrough~~ , and *italic* fo ### Mixed content - list and regular text in different cells -| Regular text in second cell | -|-------------------------------| +| Regular text in second cell | +| - | diff --git a/crates/fleischwolf/tests/data/odf/expected/text_document_03.odt.strict.md b/crates/fleischwolf/tests/data/odf/expected/text_document_03.odt.strict.md index 63bcd231..25ebd8ea 100644 --- a/crates/fleischwolf/tests/data/odf/expected/text_document_03.odt.strict.md +++ b/crates/fleischwolf/tests/data/odf/expected/text_document_03.odt.strict.md @@ -1,32 +1,32 @@ ### Table with rich cells -| Column A | Column B | -|---------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------| -| This is a list: | This is a formatted list: | +| Column A | Column B | +| - | - | +| This is a list: | This is a formatted list: | | First Paragraph Second Paragraph Third paragraph before a numbered list | This is simple text with **bold** , ~~strikethrough~~ and *italic* formatting with x 2 and H 2 O | -| This is a paragraph This is another paragraph | | +| This is a paragraph This is another paragraph | | ### Table with nested table Before table -| Column A | Column B | | -|------------------------|-------------------------------------------------|--------| -| Simple cell upper left | Simple cell with **bold** and *italic* text | | -| | Rich cell A nested table | | -| A | B | C | -| *Cell 1* | Cell 2 | Cell 3 | -| A | B | C | -| ~~Cell 1~~ | Cell 2 | Cell 3 | +| Column A | Column B | | +| - | - | - | +| Simple cell upper left | Simple cell with **bold** and *italic* text | | +| | Rich cell A nested table | | +| A | B | C | +| *Cell 1* | Cell 2 | Cell 3 | +| A | B | C | +| ~~Cell 1~~ | Cell 2 | Cell 3 | After table with **bold**, underline, ~~strikethrough~~, and *italic* formatting ### Table with pictures -| Column A | Column B | -|------------------|------------| -| Only text | | -| Text and picture | | +| Column A | Column B | +| - | - | +| Only text | | +| Text and picture | | ### Lists with same numId in different cells @@ -36,5 +36,5 @@ After table with **bold**, underline, ~~strikethrough~~, and *italic* forma ### Mixed content - list and regular text in different cells -| Regular text in second cell | -|-------------------------------| +| Regular text in second cell | +| - | diff --git a/crates/fleischwolf/tests/data/pptx/expected/powerpoint_sample.pptx.md b/crates/fleischwolf/tests/data/pptx/expected/powerpoint_sample.pptx.md index f4b98303..d633b7fb 100644 --- a/crates/fleischwolf/tests/data/pptx/expected/powerpoint_sample.pptx.md +++ b/crates/fleischwolf/tests/data/pptx/expected/powerpoint_sample.pptx.md @@ -2,16 +2,16 @@ With footnote -| | Class1 | Class1 | Class1 | Class2 | Class2 | Class2 | -|----|-----------------|-----------------|----------|----------|----------|----------| -| | A merged with B | A merged with B | C | A | B | C | -| R1 | True | False | | False | True | True | -| R2 | | | True | False | | | -| R3 | False | | | | False | | -| R3 | | True | | True | | | -| R4 | | | False | | False | | -| R4 | | True | | True | False | False | -| R4 | True | False | True | False | True | False | +| | Class1 | Class1 | Class1 | Class2 | Class2 | Class2 | +| - | - | - | - | - | - | - | +| | A merged with B | A merged with B | C | A | B | C | +| R1 | True | False | | False | True | True | +| R2 | | | True | False | | | +| R3 | False | | | | False | | +| R3 | | True | | True | | | +| R4 | | | False | | False | | +| R4 | | True | | True | False | False | +| R4 | True | False | True | False | True | False | # Second slide title diff --git a/crates/fleischwolf/tests/data/pptx/expected/powerpoint_sample.pptx.strict.md b/crates/fleischwolf/tests/data/pptx/expected/powerpoint_sample.pptx.strict.md index f4b98303..d633b7fb 100644 --- a/crates/fleischwolf/tests/data/pptx/expected/powerpoint_sample.pptx.strict.md +++ b/crates/fleischwolf/tests/data/pptx/expected/powerpoint_sample.pptx.strict.md @@ -2,16 +2,16 @@ With footnote -| | Class1 | Class1 | Class1 | Class2 | Class2 | Class2 | -|----|-----------------|-----------------|----------|----------|----------|----------| -| | A merged with B | A merged with B | C | A | B | C | -| R1 | True | False | | False | True | True | -| R2 | | | True | False | | | -| R3 | False | | | | False | | -| R3 | | True | | True | | | -| R4 | | | False | | False | | -| R4 | | True | | True | False | False | -| R4 | True | False | True | False | True | False | +| | Class1 | Class1 | Class1 | Class2 | Class2 | Class2 | +| - | - | - | - | - | - | - | +| | A merged with B | A merged with B | C | A | B | C | +| R1 | True | False | | False | True | True | +| R2 | | | True | False | | | +| R3 | False | | | | False | | +| R3 | | True | | True | | | +| R4 | | | False | | False | | +| R4 | | True | | True | False | False | +| R4 | True | False | True | False | True | False | # Second slide title diff --git a/crates/fleischwolf/tests/data/xbrl/expected/grve_10q_htm.xml.md b/crates/fleischwolf/tests/data/xbrl/expected/grve_10q_htm.xml.md index 76a5ceb3..e503603c 100644 --- a/crates/fleischwolf/tests/data/xbrl/expected/grve_10q_htm.xml.md +++ b/crates/fleischwolf/tests/data/xbrl/expected/grve_10q_htm.xml.md @@ -90,17 +90,17 @@ to notice of certain corporate actions. All accrued dividends on the Series B we A summary of accrued dividends payable with respect to the Series A and B Preferred shares on the Company's balance sheets are set out below. Dividends accrued for the benefit of the Company's CEO are included in Dividends payable, related party: -| Schedule of dividends payable, related party | | | | | | | | | -|------------------------------------------------|----|------------------------------|------------------------------|----|----|---------------------------|---------------------------|----| -| | | **December 31, 2025** **$** | **December 31, 2025** **$** | | | **March 31, 2025** **$** | **March 31, 2025** **$** | | -| Dividends payable | | | 399,506 | | | | 290,550 | | -| Dividends payable, related party | | | 201,286 | | | | 146,390 | | - -| Schedule of dividends payable, related party | | | | | | | | | -|------------------------------------------------|----|------------------------------|------------------------------|----|----|---------------------------|---------------------------|----| -| | | **December 31, 2025** **$** | **December 31, 2025** **$** | | | **March 31, 2025** **$** | **March 31, 2025** **$** | | -| Dividends payable | | | 399,506 | | | | 290,550 | | -| Dividends payable, related party | | | 201,286 | | | | 146,390 | | +| Schedule of dividends payable, related party | | | | | | | | | +| - | - | - | - | - | - | - | - | - | +| | | **December 31, 2025** **$** | **December 31, 2025** **$** | | | **March 31, 2025** **$** | **March 31, 2025** **$** | | +| Dividends payable | | | 399,506 | | | | 290,550 | | +| Dividends payable, related party | | | 201,286 | | | | 146,390 | | + +| Schedule of dividends payable, related party | | | | | | | | | +| - | - | - | - | - | - | - | - | - | +| | | **December 31, 2025** **$** | **December 31, 2025** **$** | | | **March 31, 2025** **$** | **March 31, 2025** **$** | | +| Dividends payable | | | 399,506 | | | | 290,550 | | +| Dividends payable, related party | | | 201,286 | | | | 146,390 | | **NOTE 7 - COMMON STOCK** diff --git a/crates/fleischwolf/tests/data/xbrl/expected/grve_10q_htm.xml.strict.md b/crates/fleischwolf/tests/data/xbrl/expected/grve_10q_htm.xml.strict.md index 76a5ceb3..e503603c 100644 --- a/crates/fleischwolf/tests/data/xbrl/expected/grve_10q_htm.xml.strict.md +++ b/crates/fleischwolf/tests/data/xbrl/expected/grve_10q_htm.xml.strict.md @@ -90,17 +90,17 @@ to notice of certain corporate actions. All accrued dividends on the Series B we A summary of accrued dividends payable with respect to the Series A and B Preferred shares on the Company's balance sheets are set out below. Dividends accrued for the benefit of the Company's CEO are included in Dividends payable, related party: -| Schedule of dividends payable, related party | | | | | | | | | -|------------------------------------------------|----|------------------------------|------------------------------|----|----|---------------------------|---------------------------|----| -| | | **December 31, 2025** **$** | **December 31, 2025** **$** | | | **March 31, 2025** **$** | **March 31, 2025** **$** | | -| Dividends payable | | | 399,506 | | | | 290,550 | | -| Dividends payable, related party | | | 201,286 | | | | 146,390 | | - -| Schedule of dividends payable, related party | | | | | | | | | -|------------------------------------------------|----|------------------------------|------------------------------|----|----|---------------------------|---------------------------|----| -| | | **December 31, 2025** **$** | **December 31, 2025** **$** | | | **March 31, 2025** **$** | **March 31, 2025** **$** | | -| Dividends payable | | | 399,506 | | | | 290,550 | | -| Dividends payable, related party | | | 201,286 | | | | 146,390 | | +| Schedule of dividends payable, related party | | | | | | | | | +| - | - | - | - | - | - | - | - | - | +| | | **December 31, 2025** **$** | **December 31, 2025** **$** | | | **March 31, 2025** **$** | **March 31, 2025** **$** | | +| Dividends payable | | | 399,506 | | | | 290,550 | | +| Dividends payable, related party | | | 201,286 | | | | 146,390 | | + +| Schedule of dividends payable, related party | | | | | | | | | +| - | - | - | - | - | - | - | - | - | +| | | **December 31, 2025** **$** | **December 31, 2025** **$** | | | **March 31, 2025** **$** | **March 31, 2025** **$** | | +| Dividends payable | | | 399,506 | | | | 290,550 | | +| Dividends payable, related party | | | 201,286 | | | | 146,390 | | **NOTE 7 - COMMON STOCK** diff --git a/crates/fleischwolf/tests/data/xbrl/expected/mlac-20251231.xml.md b/crates/fleischwolf/tests/data/xbrl/expected/mlac-20251231.xml.md index 37557e73..45050e2f 100644 --- a/crates/fleischwolf/tests/data/xbrl/expected/mlac-20251231.xml.md +++ b/crates/fleischwolf/tests/data/xbrl/expected/mlac-20251231.xml.md @@ -86,17 +86,17 @@ The Public Shares contain a redemption feature which allows for the redemption o As of December 31, 2025 and 2024, the Class A ordinary shares subject to possible redemption reflected in the balance sheets are reconciled in the following table: -| Gross proceeds | | $ | 230,000,000 | | -|---------------------------------------------------------------------------|----|-----|---------------|----| -| Less: | | | | | -| Proceeds allocated to Public Share Rights | | | (2,070,000 | ) | -| Class A ordinary shares issuance costs | | | (13,210,471 | ) | -| Plus: | | | | | -| Accretion for Class A ordinary shares to redemption amount | | | 16,924,324 | | -| Class A ordinary shares subject to possible redemption, December 31, 2024 | | $ | 231,643,853 | | -| Plus: | | | | | -| Accretion for Class A ordinary shares to redemption amount | | | 9,586,719 | | -| Class A ordinary shares subject to possible redemption, December 31, 2025 | | $ | 241,230,572 | | +| Gross proceeds | | $ | 230,000,000 | | +| - | - | - | - | - | +| Less: | | | | | +| Proceeds allocated to Public Share Rights | | | (2,070,000 | ) | +| Class A ordinary shares issuance costs | | | (13,210,471 | ) | +| Plus: | | | | | +| Accretion for Class A ordinary shares to redemption amount | | | 16,924,324 | | +| Class A ordinary shares subject to possible redemption, December 31, 2024 | | $ | 231,643,853 | | +| Plus: | | | | | +| Accretion for Class A ordinary shares to redemption amount | | | 9,586,719 | | +| Class A ordinary shares subject to possible redemption, December 31, 2025 | | $ | 241,230,572 | | ***Fair Value of Financial Instruments*** @@ -106,10 +106,10 @@ The fair value of the Company's assets and liabilities, which qualify as financi Fair value is defined as the price that would be received for sale of an asset or paid for transfer of a liability in an orderly transaction between market participants at the measurement date. GAAP establishes a three-tier fair value hierarchy, which prioritizes the inputs used in measuring fair value. The hierarchy gives the highest priority to unadjusted quoted prices in active markets for identical assets or liabilities (Level 1 measurements) and the lowest priority to unobservable inputs (Level 3 measurements). These tiers include: -| | ● | Level 1, defined as observable inputs such as quoted prices (unadjusted) for identical instruments in active markets; | -|----|-----|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| | ● | Level 2, defined as inputs other than quoted prices in active markets that are either directly or indirectly observable such as quoted prices for similar instruments in active markets or quoted prices for identical or similar instruments in markets that are not active; and | -| | ● | Level 3, defined as unobservable inputs in which little or no market data exists, therefore requiring an entity to develop its own assumptions, such as valuations derived from valuation techniques in which one or more significant inputs or significant value drivers are unobservable. | +| | ● | Level 1, defined as observable inputs such as quoted prices (unadjusted) for identical instruments in active markets; | +| - | - | - | +| | ● | Level 2, defined as inputs other than quoted prices in active markets that are either directly or indirectly observable such as quoted prices for similar instruments in active markets or quoted prices for identical or similar instruments in markets that are not active; and | +| | ● | Level 3, defined as unobservable inputs in which little or no market data exists, therefore requiring an entity to develop its own assumptions, such as valuations derived from valuation techniques in which one or more significant inputs or significant value drivers are unobservable. | In some circumstances, the inputs used to measure fair value might be categorized within different levels of the fair value hierarchy. In those instances, the fair value measurement is categorized in its entirety in the fair value hierarchy based on the lowest level input that is significant to the fair value measurement. @@ -131,27 +131,27 @@ The calculation of diluted net income per ordinary share does not consider the e The following table presents a reconciliation of the numerator and denominator used to compute basic and diluted net income ordinary share for each class of ordinary shares: -| | | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | -|-------------------------------------|----|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|----|----|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----| -| | | 2025 | 2025 | 2025 | 2025 | 2025 | 2025 | | | 2024 | 2024 | 2024 | 2024 | 2024 | 2024 | | -| | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | -| Basic net income per share: | | | | | | | | | | | | | | | | | -| Numerator: | | | | | | | | | | | | | | | | | -| Allocation of net income | | $ | 6,361,272 | | | $ | 1,920,674 | | | $ | 99,286 | | | $ | 343,831 | | -| Denominator: | | | | | | | | | | | | | | | | | -| Weighted-average shares outstanding | | | 23,805,000 | | | | 7,187,500 | | | | 1,785,375 | | | | 6,182,813 | | -| Basic net income per common stock | | $ | 0.27 | | | $ | 0.27 | | | $ | 0.06 | | | $ | 0.06 | | - -| | | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | -|-------------------------------------|----|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|----|----|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----| -| | | 2025 | 2025 | 2025 | 2025 | 2025 | 2025 | | | 2024 | 2024 | 2024 | 2024 | 2024 | 2024 | | -| | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | -| Diluted net income per share: | | | | | | | | | | | | | | | | | -| Numerator: | | | | | | | | | | | | | | | | | -| Allocation of net income | | $ | 6,361,272 | | | $ | 1,920,674 | | | $ | 93,012 | | | $ | 350,105 | | -| Denominator: | | | | | | | | | | | | | | | | | -| Weighted-average shares outstanding | | | 23,805,000 | | | | 7,187,500 | | | | 1,785,375 | | | | 6,720,313 | | -| Diluted net income per common stock | | $ | 0.27 | | | $ | 0.27 | | | $ | 0.05 | | | $ | 0.05 | | +| | | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | +| - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| | | 2025 | 2025 | 2025 | 2025 | 2025 | 2025 | | | 2024 | 2024 | 2024 | 2024 | 2024 | 2024 | | +| | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | +| Basic net income per share: | | | | | | | | | | | | | | | | | +| Numerator: | | | | | | | | | | | | | | | | | +| Allocation of net income | | $ | 6,361,272 | | | $ | 1,920,674 | | | $ | 99,286 | | | $ | 343,831 | | +| Denominator: | | | | | | | | | | | | | | | | | +| Weighted-average shares outstanding | | | 23,805,000 | | | | 7,187,500 | | | | 1,785,375 | | | | 6,182,813 | | +| Basic net income per common stock | | $ | 0.27 | | | $ | 0.27 | | | $ | 0.06 | | | $ | 0.06 | | + +| | | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | +| - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| | | 2025 | 2025 | 2025 | 2025 | 2025 | 2025 | | | 2024 | 2024 | 2024 | 2024 | 2024 | 2024 | | +| | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | +| Diluted net income per share: | | | | | | | | | | | | | | | | | +| Numerator: | | | | | | | | | | | | | | | | | +| Allocation of net income | | $ | 6,361,272 | | | $ | 1,920,674 | | | $ | 93,012 | | | $ | 350,105 | | +| Denominator: | | | | | | | | | | | | | | | | | +| Weighted-average shares outstanding | | | 23,805,000 | | | | 7,187,500 | | | | 1,785,375 | | | | 6,720,313 | | +| Diluted net income per common stock | | $ | 0.27 | | | $ | 0.27 | | | $ | 0.05 | | | $ | 0.05 | | Concentration of Credit Risk @@ -185,40 +185,40 @@ The Public Shares contain a redemption feature which allows for the redemption o As of December 31, 2025 and 2024, the Class A ordinary shares subject to possible redemption reflected in the balance sheets are reconciled in the following table: -| Gross proceeds | | $ | 230,000,000 | | -|---------------------------------------------------------------------------|----|-----|---------------|----| -| Less: | | | | | -| Proceeds allocated to Public Share Rights | | | (2,070,000 | ) | -| Class A ordinary shares issuance costs | | | (13,210,471 | ) | -| Plus: | | | | | -| Accretion for Class A ordinary shares to redemption amount | | | 16,924,324 | | -| Class A ordinary shares subject to possible redemption, December 31, 2024 | | $ | 231,643,853 | | -| Plus: | | | | | -| Accretion for Class A ordinary shares to redemption amount | | | 9,586,719 | | -| Class A ordinary shares subject to possible redemption, December 31, 2025 | | $ | 241,230,572 | | +| Gross proceeds | | $ | 230,000,000 | | +| - | - | - | - | - | +| Less: | | | | | +| Proceeds allocated to Public Share Rights | | | (2,070,000 | ) | +| Class A ordinary shares issuance costs | | | (13,210,471 | ) | +| Plus: | | | | | +| Accretion for Class A ordinary shares to redemption amount | | | 16,924,324 | | +| Class A ordinary shares subject to possible redemption, December 31, 2024 | | $ | 231,643,853 | | +| Plus: | | | | | +| Accretion for Class A ordinary shares to redemption amount | | | 9,586,719 | | +| Class A ordinary shares subject to possible redemption, December 31, 2025 | | $ | 241,230,572 | | As of December 31, 2025 and 2024, the Class A ordinary shares subject to possible redemption reflected in the balance sheets are reconciled in the following table: -| Gross proceeds | | $ | 230,000,000 | | -|---------------------------------------------------------------------------|----|-----|---------------|----| -| Less: | | | | | -| Proceeds allocated to Public Share Rights | | | (2,070,000 | ) | -| Class A ordinary shares issuance costs | | | (13,210,471 | ) | -| Plus: | | | | | -| Accretion for Class A ordinary shares to redemption amount | | | 16,924,324 | | -| Class A ordinary shares subject to possible redemption, December 31, 2024 | | $ | 231,643,853 | | -| Plus: | | | | | -| Accretion for Class A ordinary shares to redemption amount | | | 9,586,719 | | -| Class A ordinary shares subject to possible redemption, December 31, 2025 | | $ | 241,230,572 | | +| Gross proceeds | | $ | 230,000,000 | | +| - | - | - | - | - | +| Less: | | | | | +| Proceeds allocated to Public Share Rights | | | (2,070,000 | ) | +| Class A ordinary shares issuance costs | | | (13,210,471 | ) | +| Plus: | | | | | +| Accretion for Class A ordinary shares to redemption amount | | | 16,924,324 | | +| Class A ordinary shares subject to possible redemption, December 31, 2024 | | $ | 231,643,853 | | +| Plus: | | | | | +| Accretion for Class A ordinary shares to redemption amount | | | 9,586,719 | | +| Class A ordinary shares subject to possible redemption, December 31, 2025 | | $ | 241,230,572 | | ***Fair Value Measurements*** Fair value is defined as the price that would be received for sale of an asset or paid for transfer of a liability in an orderly transaction between market participants at the measurement date. GAAP establishes a three-tier fair value hierarchy, which prioritizes the inputs used in measuring fair value. The hierarchy gives the highest priority to unadjusted quoted prices in active markets for identical assets or liabilities (Level 1 measurements) and the lowest priority to unobservable inputs (Level 3 measurements). These tiers include: -| | ● | Level 1, defined as observable inputs such as quoted prices (unadjusted) for identical instruments in active markets; | -|----|-----|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| | ● | Level 2, defined as inputs other than quoted prices in active markets that are either directly or indirectly observable such as quoted prices for similar instruments in active markets or quoted prices for identical or similar instruments in markets that are not active; and | -| | ● | Level 3, defined as unobservable inputs in which little or no market data exists, therefore requiring an entity to develop its own assumptions, such as valuations derived from valuation techniques in which one or more significant inputs or significant value drivers are unobservable. | +| | ● | Level 1, defined as observable inputs such as quoted prices (unadjusted) for identical instruments in active markets; | +| - | - | - | +| | ● | Level 2, defined as inputs other than quoted prices in active markets that are either directly or indirectly observable such as quoted prices for similar instruments in active markets or quoted prices for identical or similar instruments in markets that are not active; and | +| | ● | Level 3, defined as unobservable inputs in which little or no market data exists, therefore requiring an entity to develop its own assumptions, such as valuations derived from valuation techniques in which one or more significant inputs or significant value drivers are unobservable. | In some circumstances, the inputs used to measure fair value might be categorized within different levels of the fair value hierarchy. In those instances, the fair value measurement is categorized in its entirety in the fair value hierarchy based on the lowest level input that is significant to the fair value measurement. @@ -240,51 +240,51 @@ The calculation of diluted net income per ordinary share does not consider the e The following table presents a reconciliation of the numerator and denominator used to compute basic and diluted net income ordinary share for each class of ordinary shares: -| | | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | -|-------------------------------------|----|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|----|----|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----| -| | | 2025 | 2025 | 2025 | 2025 | 2025 | 2025 | | | 2024 | 2024 | 2024 | 2024 | 2024 | 2024 | | -| | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | -| Basic net income per share: | | | | | | | | | | | | | | | | | -| Numerator: | | | | | | | | | | | | | | | | | -| Allocation of net income | | $ | 6,361,272 | | | $ | 1,920,674 | | | $ | 99,286 | | | $ | 343,831 | | -| Denominator: | | | | | | | | | | | | | | | | | -| Weighted-average shares outstanding | | | 23,805,000 | | | | 7,187,500 | | | | 1,785,375 | | | | 6,182,813 | | -| Basic net income per common stock | | $ | 0.27 | | | $ | 0.27 | | | $ | 0.06 | | | $ | 0.06 | | - -| | | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | -|-------------------------------------|----|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|----|----|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----| -| | | 2025 | 2025 | 2025 | 2025 | 2025 | 2025 | | | 2024 | 2024 | 2024 | 2024 | 2024 | 2024 | | -| | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | -| Diluted net income per share: | | | | | | | | | | | | | | | | | -| Numerator: | | | | | | | | | | | | | | | | | -| Allocation of net income | | $ | 6,361,272 | | | $ | 1,920,674 | | | $ | 93,012 | | | $ | 350,105 | | -| Denominator: | | | | | | | | | | | | | | | | | -| Weighted-average shares outstanding | | | 23,805,000 | | | | 7,187,500 | | | | 1,785,375 | | | | 6,720,313 | | -| Diluted net income per common stock | | $ | 0.27 | | | $ | 0.27 | | | $ | 0.05 | | | $ | 0.05 | | +| | | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | +| - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| | | 2025 | 2025 | 2025 | 2025 | 2025 | 2025 | | | 2024 | 2024 | 2024 | 2024 | 2024 | 2024 | | +| | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | +| Basic net income per share: | | | | | | | | | | | | | | | | | +| Numerator: | | | | | | | | | | | | | | | | | +| Allocation of net income | | $ | 6,361,272 | | | $ | 1,920,674 | | | $ | 99,286 | | | $ | 343,831 | | +| Denominator: | | | | | | | | | | | | | | | | | +| Weighted-average shares outstanding | | | 23,805,000 | | | | 7,187,500 | | | | 1,785,375 | | | | 6,182,813 | | +| Basic net income per common stock | | $ | 0.27 | | | $ | 0.27 | | | $ | 0.06 | | | $ | 0.06 | | + +| | | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | +| - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| | | 2025 | 2025 | 2025 | 2025 | 2025 | 2025 | | | 2024 | 2024 | 2024 | 2024 | 2024 | 2024 | | +| | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | +| Diluted net income per share: | | | | | | | | | | | | | | | | | +| Numerator: | | | | | | | | | | | | | | | | | +| Allocation of net income | | $ | 6,361,272 | | | $ | 1,920,674 | | | $ | 93,012 | | | $ | 350,105 | | +| Denominator: | | | | | | | | | | | | | | | | | +| Weighted-average shares outstanding | | | 23,805,000 | | | | 7,187,500 | | | | 1,785,375 | | | | 6,720,313 | | +| Diluted net income per common stock | | $ | 0.27 | | | $ | 0.27 | | | $ | 0.05 | | | $ | 0.05 | | The following table presents a reconciliation of the numerator and denominator used to compute basic and diluted net income ordinary share for each class of ordinary shares: -| | | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | -|-------------------------------------|----|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|----|----|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----| -| | | 2025 | 2025 | 2025 | 2025 | 2025 | 2025 | | | 2024 | 2024 | 2024 | 2024 | 2024 | 2024 | | -| | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | -| Basic net income per share: | | | | | | | | | | | | | | | | | -| Numerator: | | | | | | | | | | | | | | | | | -| Allocation of net income | | $ | 6,361,272 | | | $ | 1,920,674 | | | $ | 99,286 | | | $ | 343,831 | | -| Denominator: | | | | | | | | | | | | | | | | | -| Weighted-average shares outstanding | | | 23,805,000 | | | | 7,187,500 | | | | 1,785,375 | | | | 6,182,813 | | -| Basic net income per common stock | | $ | 0.27 | | | $ | 0.27 | | | $ | 0.06 | | | $ | 0.06 | | - -| | | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | -|-------------------------------------|----|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|----|----|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----| -| | | 2025 | 2025 | 2025 | 2025 | 2025 | 2025 | | | 2024 | 2024 | 2024 | 2024 | 2024 | 2024 | | -| | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | -| Diluted net income per share: | | | | | | | | | | | | | | | | | -| Numerator: | | | | | | | | | | | | | | | | | -| Allocation of net income | | $ | 6,361,272 | | | $ | 1,920,674 | | | $ | 93,012 | | | $ | 350,105 | | -| Denominator: | | | | | | | | | | | | | | | | | -| Weighted-average shares outstanding | | | 23,805,000 | | | | 7,187,500 | | | | 1,785,375 | | | | 6,720,313 | | -| Diluted net income per common stock | | $ | 0.27 | | | $ | 0.27 | | | $ | 0.05 | | | $ | 0.05 | | +| | | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | +| - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| | | 2025 | 2025 | 2025 | 2025 | 2025 | 2025 | | | 2024 | 2024 | 2024 | 2024 | 2024 | 2024 | | +| | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | +| Basic net income per share: | | | | | | | | | | | | | | | | | +| Numerator: | | | | | | | | | | | | | | | | | +| Allocation of net income | | $ | 6,361,272 | | | $ | 1,920,674 | | | $ | 99,286 | | | $ | 343,831 | | +| Denominator: | | | | | | | | | | | | | | | | | +| Weighted-average shares outstanding | | | 23,805,000 | | | | 7,187,500 | | | | 1,785,375 | | | | 6,182,813 | | +| Basic net income per common stock | | $ | 0.27 | | | $ | 0.27 | | | $ | 0.06 | | | $ | 0.06 | | + +| | | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | +| - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| | | 2025 | 2025 | 2025 | 2025 | 2025 | 2025 | | | 2024 | 2024 | 2024 | 2024 | 2024 | 2024 | | +| | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | +| Diluted net income per share: | | | | | | | | | | | | | | | | | +| Numerator: | | | | | | | | | | | | | | | | | +| Allocation of net income | | $ | 6,361,272 | | | $ | 1,920,674 | | | $ | 93,012 | | | $ | 350,105 | | +| Denominator: | | | | | | | | | | | | | | | | | +| Weighted-average shares outstanding | | | 23,805,000 | | | | 7,187,500 | | | | 1,785,375 | | | | 6,720,313 | | +| Diluted net income per common stock | | $ | 0.27 | | | $ | 0.27 | | | $ | 0.05 | | | $ | 0.05 | | Share Rights @@ -378,59 +378,59 @@ NOTE 8 - FAIR VALUE MEASUREMENTS At December 31, 2025, assets held in the Trust Account were comprised of $1,121 in cash and $241,229,451 in U.S. Treasury securities. During the year ended December 31, 2025, the Company did not withdraw any interest income from the Trust Account. -| | | | | | | | | | Gross | Gross | | | | | | -|-------------------|----|----|-------------------------------------------------------|----|-----------|-------------|----|----|---------|---------|----|----|------------|-------------|----| -| | | | | | Amortized | Amortized | | | Holding | Holding | | | | | | -| | | | Held To Maturity | | Cost | Cost | | | Gain | Gain | | | Fair Value | Fair Value | | -| December 31, 2025 | | | U.S. Treasury Securities (Matured on January 8, 2026) | | $ | 241,229,451 | | | $ | 58,588 | | | $ | 241,288,039 | | +| | | | | | | | | | Gross | Gross | | | | | | +| - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| | | | | | Amortized | Amortized | | | Holding | Holding | | | | | | +| | | | Held To Maturity | | Cost | Cost | | | Gain | Gain | | | Fair Value | Fair Value | | +| December 31, 2025 | | | U.S. Treasury Securities (Matured on January 8, 2026) | | $ | 241,229,451 | | | $ | 58,588 | | | $ | 241,288,039 | | At December 31, 2024, assets held in the Trust Account were comprised of $593 in cash and $231,643,260 in U.S. Treasury securities. During the period from June 14, 2014 (inception) through December 31, 2024, the Company did not withdraw any interest income from the Trust Account. -| | | | | | | | | | Gross | Gross | | | | | | -|-------------------|----|----|-----------------------------------------------------|----|-----------|-------------|----|----|---------|----------|----|----|------------|-------------|----| -| | | | | | Amortized | Amortized | | | Holding | Holding | | | | | | -| | | | Held To Maturity | | Cost | Cost | | | Loss | Loss | | | Fair Value | Fair Value | | -| December 31, 2024 | | | U.S. Treasury Securities (Matured on June 12, 2025) | | $ | 231,643,260 | | | $ | (128,995 | ) | | $ | 231,514,265 | | +| | | | | | | | | | Gross | Gross | | | | | | +| - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| | | | | | Amortized | Amortized | | | Holding | Holding | | | | | | +| | | | Held To Maturity | | Cost | Cost | | | Loss | Loss | | | Fair Value | Fair Value | | +| December 31, 2024 | | | U.S. Treasury Securities (Matured on June 12, 2025) | | $ | 231,643,260 | | | $ | (128,995 | ) | | $ | 231,514,265 | | The Public Share Rights have been classified within shareholders' deficit and will not require remeasurement after issuance. The following table presents the quantitative information regarding market assumptions used in the valuation of the Public Share Rights: -| | | **December 16,** **2024** **(Initial Public Offering)** | **December 16,** **2024** **(Initial Public Offering)** | | -|----------------------------|----|------------------------------------------------------------|------------------------------------------------------------|----| -| Trade price of Unit | | $ | 10.94 | | -| Risk-free rate | | | 4.17 | % | -| Market adjustment (1) | | | 9.2 | % | -| Fair value per share right | | $ | 0.09 | | +| | | **December 16,** **2024** **(Initial Public Offering)** | **December 16,** **2024** **(Initial Public Offering)** | | +| - | - | - | - | - | +| Trade price of Unit | | $ | 10.94 | | +| Risk-free rate | | | 4.17 | % | +| Market adjustment (1) | | | 9.2 | % | +| Fair value per share right | | $ | 0.09 | | -| (1) | Market adjustment reflects additional factors not fully captured by low volatility selection, which may include likelihood of Business Combination occurring, market perception of lack of available or suitable targets, or possible post-acquisition decline of stock price prior to beginning of the exercise period. The adjustment is determined by comparing traded warrant prices to simulated model outputs. | -|-------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| (1) | Market adjustment reflects additional factors not fully captured by low volatility selection, which may include likelihood of Business Combination occurring, market perception of lack of available or suitable targets, or possible post-acquisition decline of stock price prior to beginning of the exercise period. The adjustment is determined by comparing traded warrant prices to simulated model outputs. | +| - | - | During the year ended December 31, 2025, the Company did not withdraw any interest income from the Trust Account. -| | | | | | | | | | Gross | Gross | | | | | | -|-------------------|----|----|-------------------------------------------------------|----|-----------|-------------|----|----|---------|---------|----|----|------------|-------------|----| -| | | | | | Amortized | Amortized | | | Holding | Holding | | | | | | -| | | | Held To Maturity | | Cost | Cost | | | Gain | Gain | | | Fair Value | Fair Value | | -| December 31, 2025 | | | U.S. Treasury Securities (Matured on January 8, 2026) | | $ | 241,229,451 | | | $ | 58,588 | | | $ | 241,288,039 | | +| | | | | | | | | | Gross | Gross | | | | | | +| - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| | | | | | Amortized | Amortized | | | Holding | Holding | | | | | | +| | | | Held To Maturity | | Cost | Cost | | | Gain | Gain | | | Fair Value | Fair Value | | +| December 31, 2025 | | | U.S. Treasury Securities (Matured on January 8, 2026) | | $ | 241,229,451 | | | $ | 58,588 | | | $ | 241,288,039 | | During the period from June 14, 2014 (inception) through December 31, 2024, the Company did not withdraw any interest income from the Trust Account. -| | | | | | | | | | Gross | Gross | | | | | | -|-------------------|----|----|-----------------------------------------------------|----|-----------|-------------|----|----|---------|----------|----|----|------------|-------------|----| -| | | | | | Amortized | Amortized | | | Holding | Holding | | | | | | -| | | | Held To Maturity | | Cost | Cost | | | Loss | Loss | | | Fair Value | Fair Value | | -| December 31, 2024 | | | U.S. Treasury Securities (Matured on June 12, 2025) | | $ | 231,643,260 | | | $ | (128,995 | ) | | $ | 231,514,265 | | +| | | | | | | | | | Gross | Gross | | | | | | +| - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| | | | | | Amortized | Amortized | | | Holding | Holding | | | | | | +| | | | Held To Maturity | | Cost | Cost | | | Loss | Loss | | | Fair Value | Fair Value | | +| December 31, 2024 | | | U.S. Treasury Securities (Matured on June 12, 2025) | | $ | 231,643,260 | | | $ | (128,995 | ) | | $ | 231,514,265 | | The following table presents the quantitative information regarding market assumptions used in the valuation of the Public Share Rights: -| | | **December 16,** **2024** **(Initial Public Offering)** | **December 16,** **2024** **(Initial Public Offering)** | | -|----------------------------|----|------------------------------------------------------------|------------------------------------------------------------|----| -| Trade price of Unit | | $ | 10.94 | | -| Risk-free rate | | | 4.17 | % | -| Market adjustment (1) | | | 9.2 | % | -| Fair value per share right | | $ | 0.09 | | +| | | **December 16,** **2024** **(Initial Public Offering)** | **December 16,** **2024** **(Initial Public Offering)** | | +| - | - | - | - | - | +| Trade price of Unit | | $ | 10.94 | | +| Risk-free rate | | | 4.17 | % | +| Market adjustment (1) | | | 9.2 | % | +| Fair value per share right | | $ | 0.09 | | -| (1) | Market adjustment reflects additional factors not fully captured by low volatility selection, which may include likelihood of Business Combination occurring, market perception of lack of available or suitable targets, or possible post-acquisition decline of stock price prior to beginning of the exercise period. The adjustment is determined by comparing traded warrant prices to simulated model outputs. | -|-------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| (1) | Market adjustment reflects additional factors not fully captured by low volatility selection, which may include likelihood of Business Combination occurring, market perception of lack of available or suitable targets, or possible post-acquisition decline of stock price prior to beginning of the exercise period. The adjustment is determined by comparing traded warrant prices to simulated model outputs. | +| - | - | NOTE 9 - SEGMENT INFORMATION @@ -440,17 +440,17 @@ The Company's CODM has been identified as the Chief Executive Officer , who revi The CODM assesses performance for the single segment and decides how to allocate resources based on net income that also is reported on the statements of operations as net income. The measure of segment assets is reported on the balance sheets as total assets. When evaluating the Company's performance and making key decisions regarding resource allocation, the CODM reviews several key metrics, which include the following: -| | | December 31, | December 31, | | | December 31, | December 31, | | -|------------------------------------------------------|----|----------------|----------------|----|----|----------------|----------------|----| -| | | 2025 | 2025 | | | 2024 | 2024 | | -| Cash | | $ | 452,680 | | | $ | 1,383,392 | | -| Cash and marketable securities held in Trust Account | | $ | 241,230,572 | | | $ | 231,643,853 | | +| | | December 31, | December 31, | | | December 31, | December 31, | | +| - | - | - | - | - | - | - | - | - | +| | | 2025 | 2025 | | | 2024 | 2024 | | +| Cash | | $ | 452,680 | | | $ | 1,383,392 | | +| Cash and marketable securities held in Trust Account | | $ | 241,230,572 | | | $ | 231,643,853 | | -| | | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | -|-------------------------------------------------------------------------|----|-----------------------------------|-----------------------------------|----|----|----------------------------------------------------------------------|----------------------------------------------------------------------|----| -| | | 2025 | 2025 | | | 2024 | 2024 | | -| General and administrative expenses | | $ | 1,304,773 | | | $ | 50,736 | | -| Interest earned on cash and marketable securities held in Trust Account | | $ | 9,586,719 | | | $ | 493,853 | | +| | | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | +| - | - | - | - | - | - | - | - | - | +| | | 2025 | 2025 | | | 2024 | 2024 | | +| General and administrative expenses | | $ | 1,304,773 | | | $ | 50,736 | | +| Interest earned on cash and marketable securities held in Trust Account | | $ | 9,586,719 | | | $ | 493,853 | | The CODM reviews interest earned on marketable securities held in Trust Account to measure and monitor shareholder value and determine the most effective strategy of investment with the Trust Account funds while maintaining compliance with the trust agreement. General and administrative expenses are reviewed and monitored by the CODM to manage and forecast cash to ensure enough capital is available to complete a business combination within the business combination period. The CODM also reviews general and administrative expenses to manage, maintain and enforce all contractual agreements to ensure costs are aligned with all agreements and budget. @@ -458,17 +458,17 @@ General and administrative expenses, as reported on the statements of operations When evaluating the Company's performance and making key decisions regarding resource allocation, the CODM reviews several key metrics, which include the following: -| | | December 31, | December 31, | | | December 31, | December 31, | | -|------------------------------------------------------|----|----------------|----------------|----|----|----------------|----------------|----| -| | | 2025 | 2025 | | | 2024 | 2024 | | -| Cash | | $ | 452,680 | | | $ | 1,383,392 | | -| Cash and marketable securities held in Trust Account | | $ | 241,230,572 | | | $ | 231,643,853 | | - -| | | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | -|-------------------------------------------------------------------------|----|-----------------------------------|-----------------------------------|----|----|----------------------------------------------------------------------|----------------------------------------------------------------------|----| -| | | 2025 | 2025 | | | 2024 | 2024 | | -| General and administrative expenses | | $ | 1,304,773 | | | $ | 50,736 | | -| Interest earned on cash and marketable securities held in Trust Account | | $ | 9,586,719 | | | $ | 493,853 | | +| | | December 31, | December 31, | | | December 31, | December 31, | | +| - | - | - | - | - | - | - | - | - | +| | | 2025 | 2025 | | | 2024 | 2024 | | +| Cash | | $ | 452,680 | | | $ | 1,383,392 | | +| Cash and marketable securities held in Trust Account | | $ | 241,230,572 | | | $ | 231,643,853 | | + +| | | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | +| - | - | - | - | - | - | - | - | - | +| | | 2025 | 2025 | | | 2024 | 2024 | | +| General and administrative expenses | | $ | 1,304,773 | | | $ | 50,736 | | +| Interest earned on cash and marketable securities held in Trust Account | | $ | 9,586,719 | | | $ | 493,853 | | NOTE 10 - SUBSEQUENT EVENTS diff --git a/crates/fleischwolf/tests/data/xbrl/expected/mlac-20251231.xml.strict.md b/crates/fleischwolf/tests/data/xbrl/expected/mlac-20251231.xml.strict.md index 127afdab..8b7f65b5 100644 --- a/crates/fleischwolf/tests/data/xbrl/expected/mlac-20251231.xml.strict.md +++ b/crates/fleischwolf/tests/data/xbrl/expected/mlac-20251231.xml.strict.md @@ -86,17 +86,17 @@ The Public Shares contain a redemption feature which allows for the redemption o As of December 31, 2025 and 2024, the Class A ordinary shares subject to possible redemption reflected in the balance sheets are reconciled in the following table: -| Gross proceeds | | $ | 230,000,000 | | -|---------------------------------------------------------------------------|----|-----|---------------|----| -| Less: | | | | | -| Proceeds allocated to Public Share Rights | | | (2,070,000 | ) | -| Class A ordinary shares issuance costs | | | (13,210,471 | ) | -| Plus: | | | | | -| Accretion for Class A ordinary shares to redemption amount | | | 16,924,324 | | -| Class A ordinary shares subject to possible redemption, December 31, 2024 | | $ | 231,643,853 | | -| Plus: | | | | | -| Accretion for Class A ordinary shares to redemption amount | | | 9,586,719 | | -| Class A ordinary shares subject to possible redemption, December 31, 2025 | | $ | 241,230,572 | | +| Gross proceeds | | $ | 230,000,000 | | +| - | - | - | - | - | +| Less: | | | | | +| Proceeds allocated to Public Share Rights | | | (2,070,000 | ) | +| Class A ordinary shares issuance costs | | | (13,210,471 | ) | +| Plus: | | | | | +| Accretion for Class A ordinary shares to redemption amount | | | 16,924,324 | | +| Class A ordinary shares subject to possible redemption, December 31, 2024 | | $ | 231,643,853 | | +| Plus: | | | | | +| Accretion for Class A ordinary shares to redemption amount | | | 9,586,719 | | +| Class A ordinary shares subject to possible redemption, December 31, 2025 | | $ | 241,230,572 | | ***Fair Value of Financial Instruments*** @@ -106,10 +106,10 @@ The fair value of the Company's assets and liabilities, which qualify as financi Fair value is defined as the price that would be received for sale of an asset or paid for transfer of a liability in an orderly transaction between market participants at the measurement date. GAAP establishes a three-tier fair value hierarchy, which prioritizes the inputs used in measuring fair value. The hierarchy gives the highest priority to unadjusted quoted prices in active markets for identical assets or liabilities (Level 1 measurements) and the lowest priority to unobservable inputs (Level 3 measurements). These tiers include: -| | ● | Level 1, defined as observable inputs such as quoted prices (unadjusted) for identical instruments in active markets; | -|----|-----|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| | ● | Level 2, defined as inputs other than quoted prices in active markets that are either directly or indirectly observable such as quoted prices for similar instruments in active markets or quoted prices for identical or similar instruments in markets that are not active; and | -| | ● | Level 3, defined as unobservable inputs in which little or no market data exists, therefore requiring an entity to develop its own assumptions, such as valuations derived from valuation techniques in which one or more significant inputs or significant value drivers are unobservable. | +| | ● | Level 1, defined as observable inputs such as quoted prices (unadjusted) for identical instruments in active markets; | +| - | - | - | +| | ● | Level 2, defined as inputs other than quoted prices in active markets that are either directly or indirectly observable such as quoted prices for similar instruments in active markets or quoted prices for identical or similar instruments in markets that are not active; and | +| | ● | Level 3, defined as unobservable inputs in which little or no market data exists, therefore requiring an entity to develop its own assumptions, such as valuations derived from valuation techniques in which one or more significant inputs or significant value drivers are unobservable. | In some circumstances, the inputs used to measure fair value might be categorized within different levels of the fair value hierarchy. In those instances, the fair value measurement is categorized in its entirety in the fair value hierarchy based on the lowest level input that is significant to the fair value measurement. @@ -131,27 +131,27 @@ The calculation of diluted net income per ordinary share does not consider the e The following table presents a reconciliation of the numerator and denominator used to compute basic and diluted net income ordinary share for each class of ordinary shares: -| | | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | -|-------------------------------------|----|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|----|----|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----| -| | | 2025 | 2025 | 2025 | 2025 | 2025 | 2025 | | | 2024 | 2024 | 2024 | 2024 | 2024 | 2024 | | -| | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | -| Basic net income per share: | | | | | | | | | | | | | | | | | -| Numerator: | | | | | | | | | | | | | | | | | -| Allocation of net income | | $ | 6,361,272 | | | $ | 1,920,674 | | | $ | 99,286 | | | $ | 343,831 | | -| Denominator: | | | | | | | | | | | | | | | | | -| Weighted-average shares outstanding | | | 23,805,000 | | | | 7,187,500 | | | | 1,785,375 | | | | 6,182,813 | | -| Basic net income per common stock | | $ | 0.27 | | | $ | 0.27 | | | $ | 0.06 | | | $ | 0.06 | | - -| | | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | -|-------------------------------------|----|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|----|----|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----| -| | | 2025 | 2025 | 2025 | 2025 | 2025 | 2025 | | | 2024 | 2024 | 2024 | 2024 | 2024 | 2024 | | -| | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | -| Diluted net income per share: | | | | | | | | | | | | | | | | | -| Numerator: | | | | | | | | | | | | | | | | | -| Allocation of net income | | $ | 6,361,272 | | | $ | 1,920,674 | | | $ | 93,012 | | | $ | 350,105 | | -| Denominator: | | | | | | | | | | | | | | | | | -| Weighted-average shares outstanding | | | 23,805,000 | | | | 7,187,500 | | | | 1,785,375 | | | | 6,720,313 | | -| Diluted net income per common stock | | $ | 0.27 | | | $ | 0.27 | | | $ | 0.05 | | | $ | 0.05 | | +| | | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | +| - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| | | 2025 | 2025 | 2025 | 2025 | 2025 | 2025 | | | 2024 | 2024 | 2024 | 2024 | 2024 | 2024 | | +| | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | +| Basic net income per share: | | | | | | | | | | | | | | | | | +| Numerator: | | | | | | | | | | | | | | | | | +| Allocation of net income | | $ | 6,361,272 | | | $ | 1,920,674 | | | $ | 99,286 | | | $ | 343,831 | | +| Denominator: | | | | | | | | | | | | | | | | | +| Weighted-average shares outstanding | | | 23,805,000 | | | | 7,187,500 | | | | 1,785,375 | | | | 6,182,813 | | +| Basic net income per common stock | | $ | 0.27 | | | $ | 0.27 | | | $ | 0.06 | | | $ | 0.06 | | + +| | | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | +| - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| | | 2025 | 2025 | 2025 | 2025 | 2025 | 2025 | | | 2024 | 2024 | 2024 | 2024 | 2024 | 2024 | | +| | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | +| Diluted net income per share: | | | | | | | | | | | | | | | | | +| Numerator: | | | | | | | | | | | | | | | | | +| Allocation of net income | | $ | 6,361,272 | | | $ | 1,920,674 | | | $ | 93,012 | | | $ | 350,105 | | +| Denominator: | | | | | | | | | | | | | | | | | +| Weighted-average shares outstanding | | | 23,805,000 | | | | 7,187,500 | | | | 1,785,375 | | | | 6,720,313 | | +| Diluted net income per common stock | | $ | 0.27 | | | $ | 0.27 | | | $ | 0.05 | | | $ | 0.05 | | Concentration of Credit Risk @@ -185,40 +185,40 @@ The Public Shares contain a redemption feature which allows for the redemption o As of December 31, 2025 and 2024, the Class A ordinary shares subject to possible redemption reflected in the balance sheets are reconciled in the following table: -| Gross proceeds | | $ | 230,000,000 | | -|---------------------------------------------------------------------------|----|-----|---------------|----| -| Less: | | | | | -| Proceeds allocated to Public Share Rights | | | (2,070,000 | ) | -| Class A ordinary shares issuance costs | | | (13,210,471 | ) | -| Plus: | | | | | -| Accretion for Class A ordinary shares to redemption amount | | | 16,924,324 | | -| Class A ordinary shares subject to possible redemption, December 31, 2024 | | $ | 231,643,853 | | -| Plus: | | | | | -| Accretion for Class A ordinary shares to redemption amount | | | 9,586,719 | | -| Class A ordinary shares subject to possible redemption, December 31, 2025 | | $ | 241,230,572 | | +| Gross proceeds | | $ | 230,000,000 | | +| - | - | - | - | - | +| Less: | | | | | +| Proceeds allocated to Public Share Rights | | | (2,070,000 | ) | +| Class A ordinary shares issuance costs | | | (13,210,471 | ) | +| Plus: | | | | | +| Accretion for Class A ordinary shares to redemption amount | | | 16,924,324 | | +| Class A ordinary shares subject to possible redemption, December 31, 2024 | | $ | 231,643,853 | | +| Plus: | | | | | +| Accretion for Class A ordinary shares to redemption amount | | | 9,586,719 | | +| Class A ordinary shares subject to possible redemption, December 31, 2025 | | $ | 241,230,572 | | As of December 31, 2025 and 2024, the Class A ordinary shares subject to possible redemption reflected in the balance sheets are reconciled in the following table: -| Gross proceeds | | $ | 230,000,000 | | -|---------------------------------------------------------------------------|----|-----|---------------|----| -| Less: | | | | | -| Proceeds allocated to Public Share Rights | | | (2,070,000 | ) | -| Class A ordinary shares issuance costs | | | (13,210,471 | ) | -| Plus: | | | | | -| Accretion for Class A ordinary shares to redemption amount | | | 16,924,324 | | -| Class A ordinary shares subject to possible redemption, December 31, 2024 | | $ | 231,643,853 | | -| Plus: | | | | | -| Accretion for Class A ordinary shares to redemption amount | | | 9,586,719 | | -| Class A ordinary shares subject to possible redemption, December 31, 2025 | | $ | 241,230,572 | | +| Gross proceeds | | $ | 230,000,000 | | +| - | - | - | - | - | +| Less: | | | | | +| Proceeds allocated to Public Share Rights | | | (2,070,000 | ) | +| Class A ordinary shares issuance costs | | | (13,210,471 | ) | +| Plus: | | | | | +| Accretion for Class A ordinary shares to redemption amount | | | 16,924,324 | | +| Class A ordinary shares subject to possible redemption, December 31, 2024 | | $ | 231,643,853 | | +| Plus: | | | | | +| Accretion for Class A ordinary shares to redemption amount | | | 9,586,719 | | +| Class A ordinary shares subject to possible redemption, December 31, 2025 | | $ | 241,230,572 | | ***Fair Value Measurements*** Fair value is defined as the price that would be received for sale of an asset or paid for transfer of a liability in an orderly transaction between market participants at the measurement date. GAAP establishes a three-tier fair value hierarchy, which prioritizes the inputs used in measuring fair value. The hierarchy gives the highest priority to unadjusted quoted prices in active markets for identical assets or liabilities (Level 1 measurements) and the lowest priority to unobservable inputs (Level 3 measurements). These tiers include: -| | ● | Level 1, defined as observable inputs such as quoted prices (unadjusted) for identical instruments in active markets; | -|----|-----|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -| | ● | Level 2, defined as inputs other than quoted prices in active markets that are either directly or indirectly observable such as quoted prices for similar instruments in active markets or quoted prices for identical or similar instruments in markets that are not active; and | -| | ● | Level 3, defined as unobservable inputs in which little or no market data exists, therefore requiring an entity to develop its own assumptions, such as valuations derived from valuation techniques in which one or more significant inputs or significant value drivers are unobservable. | +| | ● | Level 1, defined as observable inputs such as quoted prices (unadjusted) for identical instruments in active markets; | +| - | - | - | +| | ● | Level 2, defined as inputs other than quoted prices in active markets that are either directly or indirectly observable such as quoted prices for similar instruments in active markets or quoted prices for identical or similar instruments in markets that are not active; and | +| | ● | Level 3, defined as unobservable inputs in which little or no market data exists, therefore requiring an entity to develop its own assumptions, such as valuations derived from valuation techniques in which one or more significant inputs or significant value drivers are unobservable. | In some circumstances, the inputs used to measure fair value might be categorized within different levels of the fair value hierarchy. In those instances, the fair value measurement is categorized in its entirety in the fair value hierarchy based on the lowest level input that is significant to the fair value measurement. @@ -240,51 +240,51 @@ The calculation of diluted net income per ordinary share does not consider the e The following table presents a reconciliation of the numerator and denominator used to compute basic and diluted net income ordinary share for each class of ordinary shares: -| | | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | -|-------------------------------------|----|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|----|----|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----| -| | | 2025 | 2025 | 2025 | 2025 | 2025 | 2025 | | | 2024 | 2024 | 2024 | 2024 | 2024 | 2024 | | -| | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | -| Basic net income per share: | | | | | | | | | | | | | | | | | -| Numerator: | | | | | | | | | | | | | | | | | -| Allocation of net income | | $ | 6,361,272 | | | $ | 1,920,674 | | | $ | 99,286 | | | $ | 343,831 | | -| Denominator: | | | | | | | | | | | | | | | | | -| Weighted-average shares outstanding | | | 23,805,000 | | | | 7,187,500 | | | | 1,785,375 | | | | 6,182,813 | | -| Basic net income per common stock | | $ | 0.27 | | | $ | 0.27 | | | $ | 0.06 | | | $ | 0.06 | | - -| | | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | -|-------------------------------------|----|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|----|----|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----| -| | | 2025 | 2025 | 2025 | 2025 | 2025 | 2025 | | | 2024 | 2024 | 2024 | 2024 | 2024 | 2024 | | -| | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | -| Diluted net income per share: | | | | | | | | | | | | | | | | | -| Numerator: | | | | | | | | | | | | | | | | | -| Allocation of net income | | $ | 6,361,272 | | | $ | 1,920,674 | | | $ | 93,012 | | | $ | 350,105 | | -| Denominator: | | | | | | | | | | | | | | | | | -| Weighted-average shares outstanding | | | 23,805,000 | | | | 7,187,500 | | | | 1,785,375 | | | | 6,720,313 | | -| Diluted net income per common stock | | $ | 0.27 | | | $ | 0.27 | | | $ | 0.05 | | | $ | 0.05 | | +| | | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | +| - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| | | 2025 | 2025 | 2025 | 2025 | 2025 | 2025 | | | 2024 | 2024 | 2024 | 2024 | 2024 | 2024 | | +| | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | +| Basic net income per share: | | | | | | | | | | | | | | | | | +| Numerator: | | | | | | | | | | | | | | | | | +| Allocation of net income | | $ | 6,361,272 | | | $ | 1,920,674 | | | $ | 99,286 | | | $ | 343,831 | | +| Denominator: | | | | | | | | | | | | | | | | | +| Weighted-average shares outstanding | | | 23,805,000 | | | | 7,187,500 | | | | 1,785,375 | | | | 6,182,813 | | +| Basic net income per common stock | | $ | 0.27 | | | $ | 0.27 | | | $ | 0.06 | | | $ | 0.06 | | + +| | | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | +| - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| | | 2025 | 2025 | 2025 | 2025 | 2025 | 2025 | | | 2024 | 2024 | 2024 | 2024 | 2024 | 2024 | | +| | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | +| Diluted net income per share: | | | | | | | | | | | | | | | | | +| Numerator: | | | | | | | | | | | | | | | | | +| Allocation of net income | | $ | 6,361,272 | | | $ | 1,920,674 | | | $ | 93,012 | | | $ | 350,105 | | +| Denominator: | | | | | | | | | | | | | | | | | +| Weighted-average shares outstanding | | | 23,805,000 | | | | 7,187,500 | | | | 1,785,375 | | | | 6,720,313 | | +| Diluted net income per common stock | | $ | 0.27 | | | $ | 0.27 | | | $ | 0.05 | | | $ | 0.05 | | The following table presents a reconciliation of the numerator and denominator used to compute basic and diluted net income ordinary share for each class of ordinary shares: -| | | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | -|-------------------------------------|----|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|----|----|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----| -| | | 2025 | 2025 | 2025 | 2025 | 2025 | 2025 | | | 2024 | 2024 | 2024 | 2024 | 2024 | 2024 | | -| | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | -| Basic net income per share: | | | | | | | | | | | | | | | | | -| Numerator: | | | | | | | | | | | | | | | | | -| Allocation of net income | | $ | 6,361,272 | | | $ | 1,920,674 | | | $ | 99,286 | | | $ | 343,831 | | -| Denominator: | | | | | | | | | | | | | | | | | -| Weighted-average shares outstanding | | | 23,805,000 | | | | 7,187,500 | | | | 1,785,375 | | | | 6,182,813 | | -| Basic net income per common stock | | $ | 0.27 | | | $ | 0.27 | | | $ | 0.06 | | | $ | 0.06 | | - -| | | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | -|-------------------------------------|----|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|----|----|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----| -| | | 2025 | 2025 | 2025 | 2025 | 2025 | 2025 | | | 2024 | 2024 | 2024 | 2024 | 2024 | 2024 | | -| | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | -| Diluted net income per share: | | | | | | | | | | | | | | | | | -| Numerator: | | | | | | | | | | | | | | | | | -| Allocation of net income | | $ | 6,361,272 | | | $ | 1,920,674 | | | $ | 93,012 | | | $ | 350,105 | | -| Denominator: | | | | | | | | | | | | | | | | | -| Weighted-average shares outstanding | | | 23,805,000 | | | | 7,187,500 | | | | 1,785,375 | | | | 6,720,313 | | -| Diluted net income per common stock | | $ | 0.27 | | | $ | 0.27 | | | $ | 0.05 | | | $ | 0.05 | | +| | | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | +| - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| | | 2025 | 2025 | 2025 | 2025 | 2025 | 2025 | | | 2024 | 2024 | 2024 | 2024 | 2024 | 2024 | | +| | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | +| Basic net income per share: | | | | | | | | | | | | | | | | | +| Numerator: | | | | | | | | | | | | | | | | | +| Allocation of net income | | $ | 6,361,272 | | | $ | 1,920,674 | | | $ | 99,286 | | | $ | 343,831 | | +| Denominator: | | | | | | | | | | | | | | | | | +| Weighted-average shares outstanding | | | 23,805,000 | | | | 7,187,500 | | | | 1,785,375 | | | | 6,182,813 | | +| Basic net income per common stock | | $ | 0.27 | | | $ | 0.27 | | | $ | 0.06 | | | $ | 0.06 | | + +| | | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | +| - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| | | 2025 | 2025 | 2025 | 2025 | 2025 | 2025 | | | 2024 | 2024 | 2024 | 2024 | 2024 | 2024 | | +| | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | +| Diluted net income per share: | | | | | | | | | | | | | | | | | +| Numerator: | | | | | | | | | | | | | | | | | +| Allocation of net income | | $ | 6,361,272 | | | $ | 1,920,674 | | | $ | 93,012 | | | $ | 350,105 | | +| Denominator: | | | | | | | | | | | | | | | | | +| Weighted-average shares outstanding | | | 23,805,000 | | | | 7,187,500 | | | | 1,785,375 | | | | 6,720,313 | | +| Diluted net income per common stock | | $ | 0.27 | | | $ | 0.27 | | | $ | 0.05 | | | $ | 0.05 | | Share Rights @@ -378,59 +378,59 @@ NOTE 8 - FAIR VALUE MEASUREMENTS At December 31, 2025, assets held in the Trust Account were comprised of $1,121 in cash and $241,229,451 in U.S. Treasury securities. During the year ended December 31, 2025, the Company did not withdraw any interest income from the Trust Account. -| | | | | | | | | | Gross | Gross | | | | | | -|-------------------|----|----|-------------------------------------------------------|----|-----------|-------------|----|----|---------|---------|----|----|------------|-------------|----| -| | | | | | Amortized | Amortized | | | Holding | Holding | | | | | | -| | | | Held To Maturity | | Cost | Cost | | | Gain | Gain | | | Fair Value | Fair Value | | -| December 31, 2025 | | | U.S. Treasury Securities (Matured on January 8, 2026) | | $ | 241,229,451 | | | $ | 58,588 | | | $ | 241,288,039 | | +| | | | | | | | | | Gross | Gross | | | | | | +| - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| | | | | | Amortized | Amortized | | | Holding | Holding | | | | | | +| | | | Held To Maturity | | Cost | Cost | | | Gain | Gain | | | Fair Value | Fair Value | | +| December 31, 2025 | | | U.S. Treasury Securities (Matured on January 8, 2026) | | $ | 241,229,451 | | | $ | 58,588 | | | $ | 241,288,039 | | At December 31, 2024, assets held in the Trust Account were comprised of $593 in cash and $231,643,260 in U.S. Treasury securities. During the period from June 14, 2014 (inception) through December 31, 2024, the Company did not withdraw any interest income from the Trust Account. -| | | | | | | | | | Gross | Gross | | | | | | -|-------------------|----|----|-----------------------------------------------------|----|-----------|-------------|----|----|---------|----------|----|----|------------|-------------|----| -| | | | | | Amortized | Amortized | | | Holding | Holding | | | | | | -| | | | Held To Maturity | | Cost | Cost | | | Loss | Loss | | | Fair Value | Fair Value | | -| December 31, 2024 | | | U.S. Treasury Securities (Matured on June 12, 2025) | | $ | 231,643,260 | | | $ | (128,995 | ) | | $ | 231,514,265 | | +| | | | | | | | | | Gross | Gross | | | | | | +| - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| | | | | | Amortized | Amortized | | | Holding | Holding | | | | | | +| | | | Held To Maturity | | Cost | Cost | | | Loss | Loss | | | Fair Value | Fair Value | | +| December 31, 2024 | | | U.S. Treasury Securities (Matured on June 12, 2025) | | $ | 231,643,260 | | | $ | (128,995 | ) | | $ | 231,514,265 | | The Public Share Rights have been classified within shareholders' deficit and will not require remeasurement after issuance. The following table presents the quantitative information regarding market assumptions used in the valuation of the Public Share Rights: -| | | **December 16,** **2024** **(Initial Public Offering)** | **December 16,** **2024** **(Initial Public Offering)** | | -|----------------------------|----|------------------------------------------------------------|------------------------------------------------------------|----| -| Trade price of Unit | | $ | 10.94 | | -| Risk-free rate | | | 4.17 | % | -| Market adjustment (1) | | | 9.2 | % | -| Fair value per share right | | $ | 0.09 | | +| | | **December 16,** **2024** **(Initial Public Offering)** | **December 16,** **2024** **(Initial Public Offering)** | | +| - | - | - | - | - | +| Trade price of Unit | | $ | 10.94 | | +| Risk-free rate | | | 4.17 | % | +| Market adjustment (1) | | | 9.2 | % | +| Fair value per share right | | $ | 0.09 | | -| (1) | Market adjustment reflects additional factors not fully captured by low volatility selection, which may include likelihood of Business Combination occurring, market perception of lack of available or suitable targets, or possible post-acquisition decline of stock price prior to beginning of the exercise period. The adjustment is determined by comparing traded warrant prices to simulated model outputs. | -|-------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| (1) | Market adjustment reflects additional factors not fully captured by low volatility selection, which may include likelihood of Business Combination occurring, market perception of lack of available or suitable targets, or possible post-acquisition decline of stock price prior to beginning of the exercise period. The adjustment is determined by comparing traded warrant prices to simulated model outputs. | +| - | - | During the year ended December 31, 2025, the Company did not withdraw any interest income from the Trust Account. -| | | | | | | | | | Gross | Gross | | | | | | -|-------------------|----|----|-------------------------------------------------------|----|-----------|-------------|----|----|---------|---------|----|----|------------|-------------|----| -| | | | | | Amortized | Amortized | | | Holding | Holding | | | | | | -| | | | Held To Maturity | | Cost | Cost | | | Gain | Gain | | | Fair Value | Fair Value | | -| December 31, 2025 | | | U.S. Treasury Securities (Matured on January 8, 2026) | | $ | 241,229,451 | | | $ | 58,588 | | | $ | 241,288,039 | | +| | | | | | | | | | Gross | Gross | | | | | | +| - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| | | | | | Amortized | Amortized | | | Holding | Holding | | | | | | +| | | | Held To Maturity | | Cost | Cost | | | Gain | Gain | | | Fair Value | Fair Value | | +| December 31, 2025 | | | U.S. Treasury Securities (Matured on January 8, 2026) | | $ | 241,229,451 | | | $ | 58,588 | | | $ | 241,288,039 | | During the period from June 14, 2014 (inception) through December 31, 2024, the Company did not withdraw any interest income from the Trust Account. -| | | | | | | | | | Gross | Gross | | | | | | -|-------------------|----|----|-----------------------------------------------------|----|-----------|-------------|----|----|---------|----------|----|----|------------|-------------|----| -| | | | | | Amortized | Amortized | | | Holding | Holding | | | | | | -| | | | Held To Maturity | | Cost | Cost | | | Loss | Loss | | | Fair Value | Fair Value | | -| December 31, 2024 | | | U.S. Treasury Securities (Matured on June 12, 2025) | | $ | 231,643,260 | | | $ | (128,995 | ) | | $ | 231,514,265 | | +| | | | | | | | | | Gross | Gross | | | | | | +| - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | +| | | | | | Amortized | Amortized | | | Holding | Holding | | | | | | +| | | | Held To Maturity | | Cost | Cost | | | Loss | Loss | | | Fair Value | Fair Value | | +| December 31, 2024 | | | U.S. Treasury Securities (Matured on June 12, 2025) | | $ | 231,643,260 | | | $ | (128,995 | ) | | $ | 231,514,265 | | The following table presents the quantitative information regarding market assumptions used in the valuation of the Public Share Rights: -| | | **December 16,** **2024** **(Initial Public Offering)** | **December 16,** **2024** **(Initial Public Offering)** | | -|----------------------------|----|------------------------------------------------------------|------------------------------------------------------------|----| -| Trade price of Unit | | $ | 10.94 | | -| Risk-free rate | | | 4.17 | % | -| Market adjustment (1) | | | 9.2 | % | -| Fair value per share right | | $ | 0.09 | | +| | | **December 16,** **2024** **(Initial Public Offering)** | **December 16,** **2024** **(Initial Public Offering)** | | +| - | - | - | - | - | +| Trade price of Unit | | $ | 10.94 | | +| Risk-free rate | | | 4.17 | % | +| Market adjustment (1) | | | 9.2 | % | +| Fair value per share right | | $ | 0.09 | | -| (1) | Market adjustment reflects additional factors not fully captured by low volatility selection, which may include likelihood of Business Combination occurring, market perception of lack of available or suitable targets, or possible post-acquisition decline of stock price prior to beginning of the exercise period. The adjustment is determined by comparing traded warrant prices to simulated model outputs. | -|-------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| (1) | Market adjustment reflects additional factors not fully captured by low volatility selection, which may include likelihood of Business Combination occurring, market perception of lack of available or suitable targets, or possible post-acquisition decline of stock price prior to beginning of the exercise period. The adjustment is determined by comparing traded warrant prices to simulated model outputs. | +| - | - | NOTE 9 - SEGMENT INFORMATION @@ -440,17 +440,17 @@ The Company's CODM has been identified as the Chief Executive Officer, who revie The CODM assesses performance for the single segment and decides how to allocate resources based on net income that also is reported on the statements of operations as net income. The measure of segment assets is reported on the balance sheets as total assets. When evaluating the Company's performance and making key decisions regarding resource allocation, the CODM reviews several key metrics, which include the following: -| | | December 31, | December 31, | | | December 31, | December 31, | | -|------------------------------------------------------|----|----------------|----------------|----|----|----------------|----------------|----| -| | | 2025 | 2025 | | | 2024 | 2024 | | -| Cash | | $ | 452,680 | | | $ | 1,383,392 | | -| Cash and marketable securities held in Trust Account | | $ | 241,230,572 | | | $ | 231,643,853 | | +| | | December 31, | December 31, | | | December 31, | December 31, | | +| - | - | - | - | - | - | - | - | - | +| | | 2025 | 2025 | | | 2024 | 2024 | | +| Cash | | $ | 452,680 | | | $ | 1,383,392 | | +| Cash and marketable securities held in Trust Account | | $ | 241,230,572 | | | $ | 231,643,853 | | -| | | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | -|-------------------------------------------------------------------------|----|-----------------------------------|-----------------------------------|----|----|----------------------------------------------------------------------|----------------------------------------------------------------------|----| -| | | 2025 | 2025 | | | 2024 | 2024 | | -| General and administrative expenses | | $ | 1,304,773 | | | $ | 50,736 | | -| Interest earned on cash and marketable securities held in Trust Account | | $ | 9,586,719 | | | $ | 493,853 | | +| | | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | +| - | - | - | - | - | - | - | - | - | +| | | 2025 | 2025 | | | 2024 | 2024 | | +| General and administrative expenses | | $ | 1,304,773 | | | $ | 50,736 | | +| Interest earned on cash and marketable securities held in Trust Account | | $ | 9,586,719 | | | $ | 493,853 | | The CODM reviews interest earned on marketable securities held in Trust Account to measure and monitor shareholder value and determine the most effective strategy of investment with the Trust Account funds while maintaining compliance with the trust agreement. General and administrative expenses are reviewed and monitored by the CODM to manage and forecast cash to ensure enough capital is available to complete a business combination within the business combination period. The CODM also reviews general and administrative expenses to manage, maintain and enforce all contractual agreements to ensure costs are aligned with all agreements and budget. @@ -458,17 +458,17 @@ General and administrative expenses, as reported on the statements of operations When evaluating the Company's performance and making key decisions regarding resource allocation, the CODM reviews several key metrics, which include the following: -| | | December 31, | December 31, | | | December 31, | December 31, | | -|------------------------------------------------------|----|----------------|----------------|----|----|----------------|----------------|----| -| | | 2025 | 2025 | | | 2024 | 2024 | | -| Cash | | $ | 452,680 | | | $ | 1,383,392 | | -| Cash and marketable securities held in Trust Account | | $ | 241,230,572 | | | $ | 231,643,853 | | - -| | | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | -|-------------------------------------------------------------------------|----|-----------------------------------|-----------------------------------|----|----|----------------------------------------------------------------------|----------------------------------------------------------------------|----| -| | | 2025 | 2025 | | | 2024 | 2024 | | -| General and administrative expenses | | $ | 1,304,773 | | | $ | 50,736 | | -| Interest earned on cash and marketable securities held in Trust Account | | $ | 9,586,719 | | | $ | 493,853 | | +| | | December 31, | December 31, | | | December 31, | December 31, | | +| - | - | - | - | - | - | - | - | - | +| | | 2025 | 2025 | | | 2024 | 2024 | | +| Cash | | $ | 452,680 | | | $ | 1,383,392 | | +| Cash and marketable securities held in Trust Account | | $ | 241,230,572 | | | $ | 231,643,853 | | + +| | | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | +| - | - | - | - | - | - | - | - | - | +| | | 2025 | 2025 | | | 2024 | 2024 | | +| General and administrative expenses | | $ | 1,304,773 | | | $ | 50,736 | | +| Interest earned on cash and marketable securities held in Trust Account | | $ | 9,586,719 | | | $ | 493,853 | | NOTE 10 - SUBSEQUENT EVENTS diff --git a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_01.xlsx.md b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_01.xlsx.md index 43430c7a..1eb9b914 100644 --- a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_01.xlsx.md +++ b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_01.xlsx.md @@ -1,53 +1,53 @@ -| first | second | third | -|----------|-----------|---------| -| 1 | 5 | 9 | -| 2 | 4 | 6 | -| 3 | 3 | 3 | -| 4 | 2 | 0 | -| 5 | 1 | -3 | -| 6 | 0 | -6 | +| first | second | third | +| - | - | - | +| 1 | 5 | 9 | +| 2 | 4 | 6 | +| 3 | 3 | 3 | +| 4 | 2 | 0 | +| 5 | 1 | -3 | +| 6 | 0 | -6 | -| col-1 | col-2 | col-3 | col-4 | -|---------|---------|---------|---------| -| 1 | 2 | 3 | 4 | -| 2 | 4 | 6 | 8 | -| 3 | 6 | 9 | 12 | -| 4 | 8 | 12 | 16 | -| 5 | 10 | 15 | 20 | -| 6 | 12 | 18 | 24 | -| 7 | 14 | 21 | 28 | -| 8 | 16 | 24 | 32 | +| col-1 | col-2 | col-3 | col-4 | +| - | - | - | - | +| 1 | 2 | 3 | 4 | +| 2 | 4 | 6 | 8 | +| 3 | 6 | 9 | 12 | +| 4 | 8 | 12 | 16 | +| 5 | 10 | 15 | 20 | +| 6 | 12 | 18 | 24 | +| 7 | 14 | 21 | 28 | +| 8 | 16 | 24 | 32 | -| col-1 | col-2 | col-3 | -|---------|---------|---------| -| 1 | 2 | 3 | -| 2 | 4 | 6 | -| 3 | 6 | 9 | -| 4 | 8 | 12 | +| col-1 | col-2 | col-3 | +| - | - | - | +| 1 | 2 | 3 | +| 2 | 4 | 6 | +| 3 | 6 | 9 | +| 4 | 8 | 12 | -| col-1 | col-2 | col-3 | -|---------|---------|---------| -| 1 | 2 | 3 | -| 2 | 4 | 6 | -| 3 | 6 | 9 | -| 4 | 8 | 12 | +| col-1 | col-2 | col-3 | +| - | - | - | +| 1 | 2 | 3 | +| 2 | 4 | 6 | +| 3 | 6 | 9 | +| 4 | 8 | 12 | -| first | header | header | -|----------|----------|----------| -| first | second | third | -| 1 | 2 | 3 | -| 3 | 4 | 5 | -| 3 | 6 | 7 | -| 8 | 9 | 9 | -| 10 | 9 | 9 | +| first | header | header | +| - | - | - | +| first | second | third | +| 1 | 2 | 3 | +| 3 | 4 | 5 | +| 3 | 6 | 7 | +| 8 | 9 | 9 | +| 10 | 9 | 9 | -| first (f) | header (f) | header (f) | -|-------------|--------------|--------------| -| first (f) | second | third | -| 1 | 2 | 3 | -| 3 | 4 | 5 | -| 3 | 6 | 7 | -| 8 | 9 | 9 | -| 10 | 9 | 9 | +| first (f) | header (f) | header (f) | +| - | - | - | +| first (f) | second | third | +| 1 | 2 | 3 | +| 3 | 4 | 5 | +| 3 | 6 | 7 | +| 8 | 9 | 9 | +| 10 | 9 | 9 | diff --git a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_01.xlsx.strict.md b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_01.xlsx.strict.md index 43430c7a..1eb9b914 100644 --- a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_01.xlsx.strict.md +++ b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_01.xlsx.strict.md @@ -1,53 +1,53 @@ -| first | second | third | -|----------|-----------|---------| -| 1 | 5 | 9 | -| 2 | 4 | 6 | -| 3 | 3 | 3 | -| 4 | 2 | 0 | -| 5 | 1 | -3 | -| 6 | 0 | -6 | +| first | second | third | +| - | - | - | +| 1 | 5 | 9 | +| 2 | 4 | 6 | +| 3 | 3 | 3 | +| 4 | 2 | 0 | +| 5 | 1 | -3 | +| 6 | 0 | -6 | -| col-1 | col-2 | col-3 | col-4 | -|---------|---------|---------|---------| -| 1 | 2 | 3 | 4 | -| 2 | 4 | 6 | 8 | -| 3 | 6 | 9 | 12 | -| 4 | 8 | 12 | 16 | -| 5 | 10 | 15 | 20 | -| 6 | 12 | 18 | 24 | -| 7 | 14 | 21 | 28 | -| 8 | 16 | 24 | 32 | +| col-1 | col-2 | col-3 | col-4 | +| - | - | - | - | +| 1 | 2 | 3 | 4 | +| 2 | 4 | 6 | 8 | +| 3 | 6 | 9 | 12 | +| 4 | 8 | 12 | 16 | +| 5 | 10 | 15 | 20 | +| 6 | 12 | 18 | 24 | +| 7 | 14 | 21 | 28 | +| 8 | 16 | 24 | 32 | -| col-1 | col-2 | col-3 | -|---------|---------|---------| -| 1 | 2 | 3 | -| 2 | 4 | 6 | -| 3 | 6 | 9 | -| 4 | 8 | 12 | +| col-1 | col-2 | col-3 | +| - | - | - | +| 1 | 2 | 3 | +| 2 | 4 | 6 | +| 3 | 6 | 9 | +| 4 | 8 | 12 | -| col-1 | col-2 | col-3 | -|---------|---------|---------| -| 1 | 2 | 3 | -| 2 | 4 | 6 | -| 3 | 6 | 9 | -| 4 | 8 | 12 | +| col-1 | col-2 | col-3 | +| - | - | - | +| 1 | 2 | 3 | +| 2 | 4 | 6 | +| 3 | 6 | 9 | +| 4 | 8 | 12 | -| first | header | header | -|----------|----------|----------| -| first | second | third | -| 1 | 2 | 3 | -| 3 | 4 | 5 | -| 3 | 6 | 7 | -| 8 | 9 | 9 | -| 10 | 9 | 9 | +| first | header | header | +| - | - | - | +| first | second | third | +| 1 | 2 | 3 | +| 3 | 4 | 5 | +| 3 | 6 | 7 | +| 8 | 9 | 9 | +| 10 | 9 | 9 | -| first (f) | header (f) | header (f) | -|-------------|--------------|--------------| -| first (f) | second | third | -| 1 | 2 | 3 | -| 3 | 4 | 5 | -| 3 | 6 | 7 | -| 8 | 9 | 9 | -| 10 | 9 | 9 | +| first (f) | header (f) | header (f) | +| - | - | - | +| first (f) | second | third | +| 1 | 2 | 3 | +| 3 | 4 | 5 | +| 3 | 6 | 7 | +| 8 | 9 | 9 | +| 10 | 9 | 9 | diff --git a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_02_sample_sales_data.xlsm.md b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_02_sample_sales_data.xlsm.md index dc1f3c20..8ba4b58c 100644 --- a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_02_sample_sales_data.xlsm.md +++ b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_02_sample_sales_data.xlsm.md @@ -1,22 +1,22 @@ -| Product | Date | Quantity | Revenue | -|-----------|---------------------|------------|-----------| -| Widget A | 2024-01-01 00:00:00 | 5 | 5000 | -| Widget B | 2024-01-02 00:00:00 | 10 | 12000 | -| Widget C | 2024-01-03 00:00:00 | 3 | 3000 | -| Widget D | 2024-01-04 00:00:00 | 8 | 8000 | -| Widget A | 2024-01-05 00:00:00 | 7 | 7000 | -| Widget B | 2024-01-06 00:00:00 | 6 | 6000 | -| Widget C | 2024-01-07 00:00:00 | 12 | 15000 | -| Widget D | 2024-01-08 00:00:00 | 9 | 9000 | -| Widget A | 2024-01-09 00:00:00 | 4 | 4000 | -| Widget B | 2024-01-10 00:00:00 | 11 | 11000 | -| Widget C | 2024-01-11 00:00:00 | 5 | 5000 | -| Widget D | 2024-01-12 00:00:00 | 8 | 8500 | -| Widget A | 2024-01-13 00:00:00 | 6 | 6200 | -| Widget B | 2024-01-14 00:00:00 | 7 | 7100 | -| Widget C | 2024-01-15 00:00:00 | 10 | 10500 | -| Widget D | 2024-01-16 00:00:00 | 3 | 3200 | -| Widget A | 2024-01-17 00:00:00 | 9 | 9400 | -| Widget B | 2024-01-18 00:00:00 | 12 | 12500 | -| Widget C | 2024-01-19 00:00:00 | 6 | 6100 | -| Widget D | 2024-01-20 00:00:00 | 8 | 8900 | +| Product | Date | Quantity | Revenue | +| - | - | - | - | +| Widget A | 2024-01-01 00:00:00 | 5 | 5000 | +| Widget B | 2024-01-02 00:00:00 | 10 | 12000 | +| Widget C | 2024-01-03 00:00:00 | 3 | 3000 | +| Widget D | 2024-01-04 00:00:00 | 8 | 8000 | +| Widget A | 2024-01-05 00:00:00 | 7 | 7000 | +| Widget B | 2024-01-06 00:00:00 | 6 | 6000 | +| Widget C | 2024-01-07 00:00:00 | 12 | 15000 | +| Widget D | 2024-01-08 00:00:00 | 9 | 9000 | +| Widget A | 2024-01-09 00:00:00 | 4 | 4000 | +| Widget B | 2024-01-10 00:00:00 | 11 | 11000 | +| Widget C | 2024-01-11 00:00:00 | 5 | 5000 | +| Widget D | 2024-01-12 00:00:00 | 8 | 8500 | +| Widget A | 2024-01-13 00:00:00 | 6 | 6200 | +| Widget B | 2024-01-14 00:00:00 | 7 | 7100 | +| Widget C | 2024-01-15 00:00:00 | 10 | 10500 | +| Widget D | 2024-01-16 00:00:00 | 3 | 3200 | +| Widget A | 2024-01-17 00:00:00 | 9 | 9400 | +| Widget B | 2024-01-18 00:00:00 | 12 | 12500 | +| Widget C | 2024-01-19 00:00:00 | 6 | 6100 | +| Widget D | 2024-01-20 00:00:00 | 8 | 8900 | diff --git a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_02_sample_sales_data.xlsm.strict.md b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_02_sample_sales_data.xlsm.strict.md index dc1f3c20..8ba4b58c 100644 --- a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_02_sample_sales_data.xlsm.strict.md +++ b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_02_sample_sales_data.xlsm.strict.md @@ -1,22 +1,22 @@ -| Product | Date | Quantity | Revenue | -|-----------|---------------------|------------|-----------| -| Widget A | 2024-01-01 00:00:00 | 5 | 5000 | -| Widget B | 2024-01-02 00:00:00 | 10 | 12000 | -| Widget C | 2024-01-03 00:00:00 | 3 | 3000 | -| Widget D | 2024-01-04 00:00:00 | 8 | 8000 | -| Widget A | 2024-01-05 00:00:00 | 7 | 7000 | -| Widget B | 2024-01-06 00:00:00 | 6 | 6000 | -| Widget C | 2024-01-07 00:00:00 | 12 | 15000 | -| Widget D | 2024-01-08 00:00:00 | 9 | 9000 | -| Widget A | 2024-01-09 00:00:00 | 4 | 4000 | -| Widget B | 2024-01-10 00:00:00 | 11 | 11000 | -| Widget C | 2024-01-11 00:00:00 | 5 | 5000 | -| Widget D | 2024-01-12 00:00:00 | 8 | 8500 | -| Widget A | 2024-01-13 00:00:00 | 6 | 6200 | -| Widget B | 2024-01-14 00:00:00 | 7 | 7100 | -| Widget C | 2024-01-15 00:00:00 | 10 | 10500 | -| Widget D | 2024-01-16 00:00:00 | 3 | 3200 | -| Widget A | 2024-01-17 00:00:00 | 9 | 9400 | -| Widget B | 2024-01-18 00:00:00 | 12 | 12500 | -| Widget C | 2024-01-19 00:00:00 | 6 | 6100 | -| Widget D | 2024-01-20 00:00:00 | 8 | 8900 | +| Product | Date | Quantity | Revenue | +| - | - | - | - | +| Widget A | 2024-01-01 00:00:00 | 5 | 5000 | +| Widget B | 2024-01-02 00:00:00 | 10 | 12000 | +| Widget C | 2024-01-03 00:00:00 | 3 | 3000 | +| Widget D | 2024-01-04 00:00:00 | 8 | 8000 | +| Widget A | 2024-01-05 00:00:00 | 7 | 7000 | +| Widget B | 2024-01-06 00:00:00 | 6 | 6000 | +| Widget C | 2024-01-07 00:00:00 | 12 | 15000 | +| Widget D | 2024-01-08 00:00:00 | 9 | 9000 | +| Widget A | 2024-01-09 00:00:00 | 4 | 4000 | +| Widget B | 2024-01-10 00:00:00 | 11 | 11000 | +| Widget C | 2024-01-11 00:00:00 | 5 | 5000 | +| Widget D | 2024-01-12 00:00:00 | 8 | 8500 | +| Widget A | 2024-01-13 00:00:00 | 6 | 6200 | +| Widget B | 2024-01-14 00:00:00 | 7 | 7100 | +| Widget C | 2024-01-15 00:00:00 | 10 | 10500 | +| Widget D | 2024-01-16 00:00:00 | 3 | 3200 | +| Widget A | 2024-01-17 00:00:00 | 9 | 9400 | +| Widget B | 2024-01-18 00:00:00 | 12 | 12500 | +| Widget C | 2024-01-19 00:00:00 | 6 | 6100 | +| Widget D | 2024-01-20 00:00:00 | 8 | 8900 | diff --git a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_03_chartsheet.xlsx.md b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_03_chartsheet.xlsx.md index 00acd12f..32ac188d 100644 --- a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_03_chartsheet.xlsx.md +++ b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_03_chartsheet.xlsx.md @@ -1,8 +1,8 @@ -| Year | Freshwater Ducks | Saltwater Ducks | Ducks | -|--------|--------------------|-------------------|---------| -| 2019 | 120 | 80 | 200 | -| 2020 | 135 | 95 | 230 | -| 2021 | 150 | 100 | 250 | -| 2022 | 170 | 110 | 280 | -| 2023 | 160 | 120 | 280 | -| 2024 | 180 | 130 | 310 | +| Year | Freshwater Ducks | Saltwater Ducks | Ducks | +| - | - | - | - | +| 2019 | 120 | 80 | 200 | +| 2020 | 135 | 95 | 230 | +| 2021 | 150 | 100 | 250 | +| 2022 | 170 | 110 | 280 | +| 2023 | 160 | 120 | 280 | +| 2024 | 180 | 130 | 310 | diff --git a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_03_chartsheet.xlsx.strict.md b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_03_chartsheet.xlsx.strict.md index 00acd12f..32ac188d 100644 --- a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_03_chartsheet.xlsx.strict.md +++ b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_03_chartsheet.xlsx.strict.md @@ -1,8 +1,8 @@ -| Year | Freshwater Ducks | Saltwater Ducks | Ducks | -|--------|--------------------|-------------------|---------| -| 2019 | 120 | 80 | 200 | -| 2020 | 135 | 95 | 230 | -| 2021 | 150 | 100 | 250 | -| 2022 | 170 | 110 | 280 | -| 2023 | 160 | 120 | 280 | -| 2024 | 180 | 130 | 310 | +| Year | Freshwater Ducks | Saltwater Ducks | Ducks | +| - | - | - | - | +| 2019 | 120 | 80 | 200 | +| 2020 | 135 | 95 | 230 | +| 2021 | 150 | 100 | 250 | +| 2022 | 170 | 110 | 280 | +| 2023 | 160 | 120 | 280 | +| 2024 | 180 | 130 | 310 | diff --git a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_04_inflated.xlsx.md b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_04_inflated.xlsx.md index 43430c7a..1eb9b914 100644 --- a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_04_inflated.xlsx.md +++ b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_04_inflated.xlsx.md @@ -1,53 +1,53 @@ -| first | second | third | -|----------|-----------|---------| -| 1 | 5 | 9 | -| 2 | 4 | 6 | -| 3 | 3 | 3 | -| 4 | 2 | 0 | -| 5 | 1 | -3 | -| 6 | 0 | -6 | +| first | second | third | +| - | - | - | +| 1 | 5 | 9 | +| 2 | 4 | 6 | +| 3 | 3 | 3 | +| 4 | 2 | 0 | +| 5 | 1 | -3 | +| 6 | 0 | -6 | -| col-1 | col-2 | col-3 | col-4 | -|---------|---------|---------|---------| -| 1 | 2 | 3 | 4 | -| 2 | 4 | 6 | 8 | -| 3 | 6 | 9 | 12 | -| 4 | 8 | 12 | 16 | -| 5 | 10 | 15 | 20 | -| 6 | 12 | 18 | 24 | -| 7 | 14 | 21 | 28 | -| 8 | 16 | 24 | 32 | +| col-1 | col-2 | col-3 | col-4 | +| - | - | - | - | +| 1 | 2 | 3 | 4 | +| 2 | 4 | 6 | 8 | +| 3 | 6 | 9 | 12 | +| 4 | 8 | 12 | 16 | +| 5 | 10 | 15 | 20 | +| 6 | 12 | 18 | 24 | +| 7 | 14 | 21 | 28 | +| 8 | 16 | 24 | 32 | -| col-1 | col-2 | col-3 | -|---------|---------|---------| -| 1 | 2 | 3 | -| 2 | 4 | 6 | -| 3 | 6 | 9 | -| 4 | 8 | 12 | +| col-1 | col-2 | col-3 | +| - | - | - | +| 1 | 2 | 3 | +| 2 | 4 | 6 | +| 3 | 6 | 9 | +| 4 | 8 | 12 | -| col-1 | col-2 | col-3 | -|---------|---------|---------| -| 1 | 2 | 3 | -| 2 | 4 | 6 | -| 3 | 6 | 9 | -| 4 | 8 | 12 | +| col-1 | col-2 | col-3 | +| - | - | - | +| 1 | 2 | 3 | +| 2 | 4 | 6 | +| 3 | 6 | 9 | +| 4 | 8 | 12 | -| first | header | header | -|----------|----------|----------| -| first | second | third | -| 1 | 2 | 3 | -| 3 | 4 | 5 | -| 3 | 6 | 7 | -| 8 | 9 | 9 | -| 10 | 9 | 9 | +| first | header | header | +| - | - | - | +| first | second | third | +| 1 | 2 | 3 | +| 3 | 4 | 5 | +| 3 | 6 | 7 | +| 8 | 9 | 9 | +| 10 | 9 | 9 | -| first (f) | header (f) | header (f) | -|-------------|--------------|--------------| -| first (f) | second | third | -| 1 | 2 | 3 | -| 3 | 4 | 5 | -| 3 | 6 | 7 | -| 8 | 9 | 9 | -| 10 | 9 | 9 | +| first (f) | header (f) | header (f) | +| - | - | - | +| first (f) | second | third | +| 1 | 2 | 3 | +| 3 | 4 | 5 | +| 3 | 6 | 7 | +| 8 | 9 | 9 | +| 10 | 9 | 9 | diff --git a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_04_inflated.xlsx.strict.md b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_04_inflated.xlsx.strict.md index 43430c7a..1eb9b914 100644 --- a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_04_inflated.xlsx.strict.md +++ b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_04_inflated.xlsx.strict.md @@ -1,53 +1,53 @@ -| first | second | third | -|----------|-----------|---------| -| 1 | 5 | 9 | -| 2 | 4 | 6 | -| 3 | 3 | 3 | -| 4 | 2 | 0 | -| 5 | 1 | -3 | -| 6 | 0 | -6 | +| first | second | third | +| - | - | - | +| 1 | 5 | 9 | +| 2 | 4 | 6 | +| 3 | 3 | 3 | +| 4 | 2 | 0 | +| 5 | 1 | -3 | +| 6 | 0 | -6 | -| col-1 | col-2 | col-3 | col-4 | -|---------|---------|---------|---------| -| 1 | 2 | 3 | 4 | -| 2 | 4 | 6 | 8 | -| 3 | 6 | 9 | 12 | -| 4 | 8 | 12 | 16 | -| 5 | 10 | 15 | 20 | -| 6 | 12 | 18 | 24 | -| 7 | 14 | 21 | 28 | -| 8 | 16 | 24 | 32 | +| col-1 | col-2 | col-3 | col-4 | +| - | - | - | - | +| 1 | 2 | 3 | 4 | +| 2 | 4 | 6 | 8 | +| 3 | 6 | 9 | 12 | +| 4 | 8 | 12 | 16 | +| 5 | 10 | 15 | 20 | +| 6 | 12 | 18 | 24 | +| 7 | 14 | 21 | 28 | +| 8 | 16 | 24 | 32 | -| col-1 | col-2 | col-3 | -|---------|---------|---------| -| 1 | 2 | 3 | -| 2 | 4 | 6 | -| 3 | 6 | 9 | -| 4 | 8 | 12 | +| col-1 | col-2 | col-3 | +| - | - | - | +| 1 | 2 | 3 | +| 2 | 4 | 6 | +| 3 | 6 | 9 | +| 4 | 8 | 12 | -| col-1 | col-2 | col-3 | -|---------|---------|---------| -| 1 | 2 | 3 | -| 2 | 4 | 6 | -| 3 | 6 | 9 | -| 4 | 8 | 12 | +| col-1 | col-2 | col-3 | +| - | - | - | +| 1 | 2 | 3 | +| 2 | 4 | 6 | +| 3 | 6 | 9 | +| 4 | 8 | 12 | -| first | header | header | -|----------|----------|----------| -| first | second | third | -| 1 | 2 | 3 | -| 3 | 4 | 5 | -| 3 | 6 | 7 | -| 8 | 9 | 9 | -| 10 | 9 | 9 | +| first | header | header | +| - | - | - | +| first | second | third | +| 1 | 2 | 3 | +| 3 | 4 | 5 | +| 3 | 6 | 7 | +| 8 | 9 | 9 | +| 10 | 9 | 9 | -| first (f) | header (f) | header (f) | -|-------------|--------------|--------------| -| first (f) | second | third | -| 1 | 2 | 3 | -| 3 | 4 | 5 | -| 3 | 6 | 7 | -| 8 | 9 | 9 | -| 10 | 9 | 9 | +| first (f) | header (f) | header (f) | +| - | - | - | +| first (f) | second | third | +| 1 | 2 | 3 | +| 3 | 4 | 5 | +| 3 | 6 | 7 | +| 8 | 9 | 9 | +| 10 | 9 | 9 | diff --git a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_05_table_with_title.xlsx.md b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_05_table_with_title.xlsx.md index 23391157..f8f7672e 100644 --- a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_05_table_with_title.xlsx.md +++ b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_05_table_with_title.xlsx.md @@ -1,11 +1,11 @@ -| Number of freshwater ducks per year | -|---------------------------------------| +| Number of freshwater ducks per year | +| - | -| Year | Freshwater Ducks | -|--------|--------------------| -| 2019 | 120 | -| 2020 | 135 | -| 2021 | 150 | -| 2022 | 170 | -| 2023 | 160 | -| 2024 | 180 | +| Year | Freshwater Ducks | +| - | - | +| 2019 | 120 | +| 2020 | 135 | +| 2021 | 150 | +| 2022 | 170 | +| 2023 | 160 | +| 2024 | 180 | diff --git a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_05_table_with_title.xlsx.strict.md b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_05_table_with_title.xlsx.strict.md index 23391157..f8f7672e 100644 --- a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_05_table_with_title.xlsx.strict.md +++ b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_05_table_with_title.xlsx.strict.md @@ -1,11 +1,11 @@ -| Number of freshwater ducks per year | -|---------------------------------------| +| Number of freshwater ducks per year | +| - | -| Year | Freshwater Ducks | -|--------|--------------------| -| 2019 | 120 | -| 2020 | 135 | -| 2021 | 150 | -| 2022 | 170 | -| 2023 | 160 | -| 2024 | 180 | +| Year | Freshwater Ducks | +| - | - | +| 2019 | 120 | +| 2020 | 135 | +| 2021 | 150 | +| 2022 | 170 | +| 2023 | 160 | +| 2024 | 180 | diff --git a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_06_edge_cases_.xlsx.md b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_06_edge_cases_.xlsx.md index b455d4e2..196ed35b 100644 --- a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_06_edge_cases_.xlsx.md +++ b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_06_edge_cases_.xlsx.md @@ -1,22 +1,22 @@ -| | Sub-category | ID | Question | Answer | Extra | -|----------|-----------------------|------|-----------------------------------------|----------------------------|----------------------------------------------------| -| Overview | Purpose and Use Cases | AI 1 | What is the main objective? | The main objective is... | Additional interviewers insights, or information | -| Overview | Purpose and Use Cases | AI 2 | What are the specific use cases? | The AI is applied for... | More information | +| | Sub-category | ID | Question | Answer | Extra | +| - | - | - | - | - | - | +| Overview | Purpose and Use Cases | AI 1 | What is the main objective? | The main objective is... | Additional interviewers insights, or information | +| Overview | Purpose and Use Cases | AI 2 | What are the specific use cases? | The AI is applied for... | More information | | Overview | Purpose and Use Cases | AI 3 | What types of data will the AI process? | The AI processes code data | With Privacy Mode disabled, no code data is stored | -| | Product/Integration | Sub-category | ID | Question | Answer | Extra | -|---------------|-----------------------|-----------------------|------|-----------------------------------------|----------------------------|----------------------------------------------------| -| | Overview | Purpose and Use Cases | AI 1 | What is the main objective? | The main objective is... | Additional interviewers insights, or information | -| Attached left | Overview | Purpose and Use Cases | AI 2 | What are the specific use cases? | The AI is applied for... | More information | -| | Overview | Purpose and Use Cases | AI 3 | What types of data will the AI process? | The AI processes code data | With Privacy Mode disabled, no code data is stored | +| | Product/Integration | Sub-category | ID | Question | Answer | Extra | +| - | - | - | - | - | - | - | +| | Overview | Purpose and Use Cases | AI 1 | What is the main objective? | The main objective is... | Additional interviewers insights, or information | +| Attached left | Overview | Purpose and Use Cases | AI 2 | What are the specific use cases? | The AI is applied for... | More information | +| | Overview | Purpose and Use Cases | AI 3 | What types of data will the AI process? | The AI processes code data | With Privacy Mode disabled, no code data is stored | -| Product/Integration | Sub-category | ID | Question | Answer | Extra | -|-----------------------|-----------------------|------|-----------------------------------------|----------------------------|----------------------------------------------------| -| Overview | Purpose and Use Cases | AI 1 | What is the main objective? | The main objective is... | Additional interviewers insights, or information | -| Overview | Purpose and Use Cases | AI 2 | What are the specific use cases? | The AI is applied for... | More information | -| Overview | Purpose and Use Cases | AI 3 | What types of data will the AI process? | The AI processes code data | With Privacy Mode disabled, no code data is stored | +| Product/Integration | Sub-category | ID | Question | Answer | Extra | +| - | - | - | - | - | - | +| Overview | Purpose and Use Cases | AI 1 | What is the main objective? | The main objective is... | Additional interviewers insights, or information | +| Overview | Purpose and Use Cases | AI 2 | What are the specific use cases? | The AI is applied for... | More information | +| Overview | Purpose and Use Cases | AI 3 | What types of data will the AI process? | The AI processes code data | With Privacy Mode disabled, no code data is stored | -| G1 | H1 | I3 | -|------------|-----------|---------| +| G1 | H1 | I3 | +| - | - | - | | Overview 1 | Purpose 1 | Extra 1 | | Overview 2 | Purpose 2 | Extra 2 | diff --git a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_06_edge_cases_.xlsx.strict.md b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_06_edge_cases_.xlsx.strict.md index b455d4e2..196ed35b 100644 --- a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_06_edge_cases_.xlsx.strict.md +++ b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_06_edge_cases_.xlsx.strict.md @@ -1,22 +1,22 @@ -| | Sub-category | ID | Question | Answer | Extra | -|----------|-----------------------|------|-----------------------------------------|----------------------------|----------------------------------------------------| -| Overview | Purpose and Use Cases | AI 1 | What is the main objective? | The main objective is... | Additional interviewers insights, or information | -| Overview | Purpose and Use Cases | AI 2 | What are the specific use cases? | The AI is applied for... | More information | +| | Sub-category | ID | Question | Answer | Extra | +| - | - | - | - | - | - | +| Overview | Purpose and Use Cases | AI 1 | What is the main objective? | The main objective is... | Additional interviewers insights, or information | +| Overview | Purpose and Use Cases | AI 2 | What are the specific use cases? | The AI is applied for... | More information | | Overview | Purpose and Use Cases | AI 3 | What types of data will the AI process? | The AI processes code data | With Privacy Mode disabled, no code data is stored | -| | Product/Integration | Sub-category | ID | Question | Answer | Extra | -|---------------|-----------------------|-----------------------|------|-----------------------------------------|----------------------------|----------------------------------------------------| -| | Overview | Purpose and Use Cases | AI 1 | What is the main objective? | The main objective is... | Additional interviewers insights, or information | -| Attached left | Overview | Purpose and Use Cases | AI 2 | What are the specific use cases? | The AI is applied for... | More information | -| | Overview | Purpose and Use Cases | AI 3 | What types of data will the AI process? | The AI processes code data | With Privacy Mode disabled, no code data is stored | +| | Product/Integration | Sub-category | ID | Question | Answer | Extra | +| - | - | - | - | - | - | - | +| | Overview | Purpose and Use Cases | AI 1 | What is the main objective? | The main objective is... | Additional interviewers insights, or information | +| Attached left | Overview | Purpose and Use Cases | AI 2 | What are the specific use cases? | The AI is applied for... | More information | +| | Overview | Purpose and Use Cases | AI 3 | What types of data will the AI process? | The AI processes code data | With Privacy Mode disabled, no code data is stored | -| Product/Integration | Sub-category | ID | Question | Answer | Extra | -|-----------------------|-----------------------|------|-----------------------------------------|----------------------------|----------------------------------------------------| -| Overview | Purpose and Use Cases | AI 1 | What is the main objective? | The main objective is... | Additional interviewers insights, or information | -| Overview | Purpose and Use Cases | AI 2 | What are the specific use cases? | The AI is applied for... | More information | -| Overview | Purpose and Use Cases | AI 3 | What types of data will the AI process? | The AI processes code data | With Privacy Mode disabled, no code data is stored | +| Product/Integration | Sub-category | ID | Question | Answer | Extra | +| - | - | - | - | - | - | +| Overview | Purpose and Use Cases | AI 1 | What is the main objective? | The main objective is... | Additional interviewers insights, or information | +| Overview | Purpose and Use Cases | AI 2 | What are the specific use cases? | The AI is applied for... | More information | +| Overview | Purpose and Use Cases | AI 3 | What types of data will the AI process? | The AI processes code data | With Privacy Mode disabled, no code data is stored | -| G1 | H1 | I3 | -|------------|-----------|---------| +| G1 | H1 | I3 | +| - | - | - | | Overview 1 | Purpose 1 | Extra 1 | | Overview 2 | Purpose 2 | Extra 2 | diff --git a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_07_gap_tolerance_.xlsx.md b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_07_gap_tolerance_.xlsx.md index 83152fd6..5bc4baff 100644 --- a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_07_gap_tolerance_.xlsx.md +++ b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_07_gap_tolerance_.xlsx.md @@ -1,90 +1,90 @@ -| HIGH VOLTAGE SWITCHBOARD | -|----------------------------| -| DATA SHEET | +| HIGH VOLTAGE SWITCHBOARD | +| - | +| DATA SHEET | -| MODEL-000 | -|--------------| -| 2016 | +| MODEL-000 | +| - | +| 2016 | | Page 1 of 10 | -| E-123 | -|---------| +| E-123 | +| - | -| Package no.: | | | Doc. no.: | | | | Rev. | -|----------------|----------|----------|-------------|--------|--------|--------|--------| -| 13156456 | 13156456 | 13156456 | 144564 | 144564 | 144564 | 144564 | A | +| Package no.: | | | Doc. no.: | | | | Rev. | +| - | - | - | - | - | - | - | - | +| 13156456 | 13156456 | 13156456 | 144564 | 144564 | 144564 | 144564 | A | -| Functional requirements | -|---------------------------| +| Functional requirements | +| - | -| Power system | -|--------------------------| -| 1 | -| 2 | -| 3 | -| 4 | -| 5 | -| 6 | -| Construction | -| 7 | -| 8 | -| 9 | +| Power system | +| - | +| 1 | +| 2 | +| 3 | +| 4 | +| 5 | +| 6 | +| Construction | +| 7 | +| 8 | +| 9 | | Environmental conditions | -| 10 | -| 11 | -| 12 | -| 13 | -| Arc test | -| 14 | -| Notes | -| 1 | -| 2 | +| 10 | +| 11 | +| 12 | +| 13 | +| Arc test | +| 14 | +| Notes | +| 1 | +| 2 | -| Rated system voltage | -|------------------------| +| Rated system voltage | +| - | | Rated system frequency | -| No. of phases | -| System earthing | -| Earth fault current | +| No. of phases | +| System earthing | +| Earth fault current | | Control voltage supply | -| kV | : | 130 (131 Um, 132 AC, 133 BIL) | | -|------|-----|------------------------------------|--------| -| Hz | : | 134 | | -| | : | 3 | | -| | : | Solidly Earthed | | -| A | : | 135 kA | | -| | : | 2 x 136V AC UPS 1 x 137V AC normal | Note 1 | +| kV | : | 130 (131 Um, 132 AC, 133 BIL) | | +| - | - | - | - | +| Hz | : | 134 | | +| | : | 3 | | +| | : | Solidly Earthed | | +| A | : | 135 kA | | +| | : | 2 x 136V AC UPS 1 x 137V AC normal | Note 1 | -| Metal-enclosed partition | -|---------------------------------| -| VT for cable discharging | +| Metal-enclosed partition | +| - | +| VT for cable discharging | | Voltage and Current measurement | -| : | Gas Insulated Switchgear (GIS) | Non Sticky | -|-----|-----------------------------------|--------------| -| | No | | -| | Low Power Instrument Transformers | | +| : | Gas Insulated Switchgear (GIS) | Non Sticky | +| - | - | - | +| | No | | +| | Low Power Instrument Transformers | | -| Hazardous area classification | -|---------------------------------| -| Ambient temp. | -| Location | -| Humidity | +| Hazardous area classification | +| - | +| Ambient temp. | +| Location | +| Humidity | -| | : | Non hazardous | -|----|-----|-------------------| -| ºC | : | Min. -5, max. +40 | -| | : | Indoor | -| % | : | 100 | +| | : | Non hazardous | +| - | - | - | +| ºC | : | Min. -5, max. +40 | +| | : | Indoor | +| % | : | 100 | -| Arc testing | -|---------------| +| Arc testing | +| - | -| Yes/no | : | Yes | Note 2 | -|----------|-----|-------|----------| +| Yes/no | : | Yes | Note 2 | +| - | - | - | - | -| Converted to 110VDC | Converted to 110VDC | Converted to 110VDC | Converted to 110VDC | Converted to 110VDC | -|------------------------|------------------------|------------------------|------------------------|------------------------| -| Arc test (type test) | Arc test (type test) | Arc test (type test) | Arc test (type test) | Arc test (type test) | -| | | | | | +| Converted to 110VDC | Converted to 110VDC | Converted to 110VDC | Converted to 110VDC | Converted to 110VDC | +| - | - | - | - | - | +| Arc test (type test) | Arc test (type test) | Arc test (type test) | Arc test (type test) | Arc test (type test) | +| | | | | | diff --git a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_07_gap_tolerance_.xlsx.strict.md b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_07_gap_tolerance_.xlsx.strict.md index 83152fd6..5bc4baff 100644 --- a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_07_gap_tolerance_.xlsx.strict.md +++ b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_07_gap_tolerance_.xlsx.strict.md @@ -1,90 +1,90 @@ -| HIGH VOLTAGE SWITCHBOARD | -|----------------------------| -| DATA SHEET | +| HIGH VOLTAGE SWITCHBOARD | +| - | +| DATA SHEET | -| MODEL-000 | -|--------------| -| 2016 | +| MODEL-000 | +| - | +| 2016 | | Page 1 of 10 | -| E-123 | -|---------| +| E-123 | +| - | -| Package no.: | | | Doc. no.: | | | | Rev. | -|----------------|----------|----------|-------------|--------|--------|--------|--------| -| 13156456 | 13156456 | 13156456 | 144564 | 144564 | 144564 | 144564 | A | +| Package no.: | | | Doc. no.: | | | | Rev. | +| - | - | - | - | - | - | - | - | +| 13156456 | 13156456 | 13156456 | 144564 | 144564 | 144564 | 144564 | A | -| Functional requirements | -|---------------------------| +| Functional requirements | +| - | -| Power system | -|--------------------------| -| 1 | -| 2 | -| 3 | -| 4 | -| 5 | -| 6 | -| Construction | -| 7 | -| 8 | -| 9 | +| Power system | +| - | +| 1 | +| 2 | +| 3 | +| 4 | +| 5 | +| 6 | +| Construction | +| 7 | +| 8 | +| 9 | | Environmental conditions | -| 10 | -| 11 | -| 12 | -| 13 | -| Arc test | -| 14 | -| Notes | -| 1 | -| 2 | +| 10 | +| 11 | +| 12 | +| 13 | +| Arc test | +| 14 | +| Notes | +| 1 | +| 2 | -| Rated system voltage | -|------------------------| +| Rated system voltage | +| - | | Rated system frequency | -| No. of phases | -| System earthing | -| Earth fault current | +| No. of phases | +| System earthing | +| Earth fault current | | Control voltage supply | -| kV | : | 130 (131 Um, 132 AC, 133 BIL) | | -|------|-----|------------------------------------|--------| -| Hz | : | 134 | | -| | : | 3 | | -| | : | Solidly Earthed | | -| A | : | 135 kA | | -| | : | 2 x 136V AC UPS 1 x 137V AC normal | Note 1 | +| kV | : | 130 (131 Um, 132 AC, 133 BIL) | | +| - | - | - | - | +| Hz | : | 134 | | +| | : | 3 | | +| | : | Solidly Earthed | | +| A | : | 135 kA | | +| | : | 2 x 136V AC UPS 1 x 137V AC normal | Note 1 | -| Metal-enclosed partition | -|---------------------------------| -| VT for cable discharging | +| Metal-enclosed partition | +| - | +| VT for cable discharging | | Voltage and Current measurement | -| : | Gas Insulated Switchgear (GIS) | Non Sticky | -|-----|-----------------------------------|--------------| -| | No | | -| | Low Power Instrument Transformers | | +| : | Gas Insulated Switchgear (GIS) | Non Sticky | +| - | - | - | +| | No | | +| | Low Power Instrument Transformers | | -| Hazardous area classification | -|---------------------------------| -| Ambient temp. | -| Location | -| Humidity | +| Hazardous area classification | +| - | +| Ambient temp. | +| Location | +| Humidity | -| | : | Non hazardous | -|----|-----|-------------------| -| ºC | : | Min. -5, max. +40 | -| | : | Indoor | -| % | : | 100 | +| | : | Non hazardous | +| - | - | - | +| ºC | : | Min. -5, max. +40 | +| | : | Indoor | +| % | : | 100 | -| Arc testing | -|---------------| +| Arc testing | +| - | -| Yes/no | : | Yes | Note 2 | -|----------|-----|-------|----------| +| Yes/no | : | Yes | Note 2 | +| - | - | - | - | -| Converted to 110VDC | Converted to 110VDC | Converted to 110VDC | Converted to 110VDC | Converted to 110VDC | -|------------------------|------------------------|------------------------|------------------------|------------------------| -| Arc test (type test) | Arc test (type test) | Arc test (type test) | Arc test (type test) | Arc test (type test) | -| | | | | | +| Converted to 110VDC | Converted to 110VDC | Converted to 110VDC | Converted to 110VDC | Converted to 110VDC | +| - | - | - | - | - | +| Arc test (type test) | Arc test (type test) | Arc test (type test) | Arc test (type test) | Arc test (type test) | +| | | | | | diff --git a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_08_one_cell_anchor.xlsx.md b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_08_one_cell_anchor.xlsx.md index ab7814b8..3a2e37f5 100644 --- a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_08_one_cell_anchor.xlsx.md +++ b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_08_one_cell_anchor.xlsx.md @@ -1,6 +1,6 @@ -| Header1 | Header2 | -|-----------|-----------| -| data1 | data2 | -| data3 | data4 | +| Header1 | Header2 | +| - | - | +| data1 | data2 | +| data3 | data4 | diff --git a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_08_one_cell_anchor.xlsx.strict.md b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_08_one_cell_anchor.xlsx.strict.md index ab7814b8..3a2e37f5 100644 --- a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_08_one_cell_anchor.xlsx.strict.md +++ b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_08_one_cell_anchor.xlsx.strict.md @@ -1,6 +1,6 @@ -| Header1 | Header2 | -|-----------|-----------| -| data1 | data2 | -| data3 | data4 | +| Header1 | Header2 | +| - | - | +| data1 | data2 | +| data3 | data4 | diff --git a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_comments.xlsx.md b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_comments.xlsx.md index 5f5597ee..f15e3075 100644 --- a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_comments.xlsx.md +++ b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_comments.xlsx.md @@ -1,14 +1,14 @@ -| Python is great | -|-------------------| +| Python is great | +| - | -| Machine learning libraries | -|------------------------------| +| Machine learning libraries | +| - | -| Year | Freshwater Ducks | Saltwater Ducks | Ducks | -|--------|--------------------|-------------------|---------| -| 2019 | 120 | 80 | 200 | -| 2020 | 135 | 95 | 230 | -| 2021 | 150 | 100 | 250 | -| 2022 | 170 | 110 | 280 | -| 2023 | 160 | 120 | 280 | -| 2024 | 180 | 130 | 310 | +| Year | Freshwater Ducks | Saltwater Ducks | Ducks | +| - | - | - | - | +| 2019 | 120 | 80 | 200 | +| 2020 | 135 | 95 | 230 | +| 2021 | 150 | 100 | 250 | +| 2022 | 170 | 110 | 280 | +| 2023 | 160 | 120 | 280 | +| 2024 | 180 | 130 | 310 | diff --git a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_comments.xlsx.strict.md b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_comments.xlsx.strict.md index 5f5597ee..f15e3075 100644 --- a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_comments.xlsx.strict.md +++ b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_comments.xlsx.strict.md @@ -1,14 +1,14 @@ -| Python is great | -|-------------------| +| Python is great | +| - | -| Machine learning libraries | -|------------------------------| +| Machine learning libraries | +| - | -| Year | Freshwater Ducks | Saltwater Ducks | Ducks | -|--------|--------------------|-------------------|---------| -| 2019 | 120 | 80 | 200 | -| 2020 | 135 | 95 | 230 | -| 2021 | 150 | 100 | 250 | -| 2022 | 170 | 110 | 280 | -| 2023 | 160 | 120 | 280 | -| 2024 | 180 | 130 | 310 | +| Year | Freshwater Ducks | Saltwater Ducks | Ducks | +| - | - | - | - | +| 2019 | 120 | 80 | 200 | +| 2020 | 135 | 95 | 230 | +| 2021 | 150 | 100 | 250 | +| 2022 | 170 | 110 | 280 | +| 2023 | 160 | 120 | 280 | +| 2024 | 180 | 130 | 310 | From 39d58b29b606a08096c99eb132b0b1db03fd46e9 Mon Sep 17 00:00:00 2001 From: artiz Date: Mon, 29 Jun 2026 08:20:51 +0200 Subject: [PATCH 13/42] feat(tableformer): export the bbox decoder (full model now exported) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Extends the export with the third graph, completing the model: the encoder now also emits the raw enc_out [1,28,28,256], and bbox.onnx batches docling's BBoxDecoder.inference over cells — enc_out + per-cell tag hidden states [N,512] → boxes [N,4] (cxcywh, sigmoid) + classes. All three graphs verified against PyTorch (boxes max diff 2e-7). This gives the per-cell geometry needed to match PDF text cells onto the OTSL grid for cell content. The Rust OTSL decode is unaffected (still byte-exact: 54 tokens on 2305v1-pg9). Co-Authored-By: Claude Opus 4.8 (1M context) --- scripts/export_tableformer.py | 60 +++++++++++++++++++++++++++++------ 1 file changed, 51 insertions(+), 9 deletions(-) diff --git a/scripts/export_tableformer.py b/scripts/export_tableformer.py index 34f018bb..f612c709 100644 --- a/scripts/export_tableformer.py +++ b/scripts/export_tableformer.py @@ -55,11 +55,35 @@ class Encode(nn.Module): def forward(self, img): - eo = m._encoder(img) - eo = tt._input_filter(eo.permute(0, 3, 1, 2)).permute(0, 2, 3, 1) + eo_raw = m._encoder(img) # [1,28,28,512] — also feeds the bbox decoder + eo = tt._input_filter(eo_raw.permute(0, 3, 1, 2)).permute(0, 2, 3, 1) ei = eo.reshape(1, -1, eo.size(-1)).permute(1, 0, 2) pos = ei.shape[0] - return tt._encoder(ei, mask=torch.zeros((nh, pos, pos), dtype=torch.bool)) + mem = tt._encoder(ei, mask=torch.zeros((nh, pos, pos), dtype=torch.bool)) + return mem, eo_raw + + +class BBoxDecode(nn.Module): + # docling's BBoxDecoder.inference, batched over cells: each cell's tag hidden + # state attends over the (bbox-decoder-filtered) encoder output to a box. + def forward(self, enc_out, tag_h): # enc_out [1,28,28,512], tag_h [N,512] + bd = m._bbox_decoder + e = bd._input_filter(enc_out.permute(0, 3, 1, 2)).permute(0, 2, 3, 1) + e = e.reshape(1, -1, e.size(3)) # [1, 784, 512] + n = tag_h.shape[0] + h = bd._init_h(e.mean(dim=1)).expand(n, -1) # [N, dim] (same for all cells) + a = bd._attention + att = a._full_att( + a._relu( + a._encoder_att(e) + + a._tag_decoder_att(tag_h).unsqueeze(1) + + a._language_att(h).unsqueeze(1) + ) + ).squeeze(2) + alpha = a._softmax(att) # [N, 784] + awe = (e * alpha.unsqueeze(2)).sum(dim=1) # [N, 512] + h = (bd._sigmoid(bd._f_beta(h)) * awe) * h + return bd._bbox_embed(h).sigmoid(), bd._class_embed(h) class Decode(nn.Module): @@ -88,12 +112,15 @@ def check(name, a, b): return d +from torch.export import Dim # noqa: E402 + img = torch.randn(1, 3, 448, 448) with torch.no_grad(): - mem = Encode()(img) + mem, enc_out = Encode()(img) torch.onnx.export( Encode(), (img,), f"{OUT}/encoder.onnx", - input_names=["image"], output_names=["memory"], opset_version=17, dynamo=False, + input_names=["image"], output_names=["memory", "enc_out"], + opset_version=17, dynamo=False, ) tags = torch.full((4, 1), start, dtype=torch.long) with torch.no_grad(): @@ -101,26 +128,41 @@ def check(name, a, b): # The dynamo exporter is needed here: the legacy tracer bakes the sequence length # into nn.MultiheadAttention's reshape, so a 1-token first step fails. dynamo keeps # the `seq` axis symbolic. -from torch.export import Dim # noqa: E402 - seq = Dim("seq", min=1, max=1024) torch.onnx.export( Decode(), (tags, mem), f"{OUT}/decoder.onnx", input_names=["tags", "memory"], output_names=["logits", "hidden"], dynamo=True, dynamic_shapes=({0: seq}, {}), ) +# bbox decoder: N cell hiddens → N boxes (+ classes). N is dynamic. +tag_h = torch.randn(5, 512) +with torch.no_grad(): + boxes, classes = BBoxDecode()(enc_out, tag_h) +ncells = Dim("ncells", min=1, max=1024) +torch.onnx.export( + BBoxDecode(), (enc_out, tag_h), f"{OUT}/bbox.onnx", + input_names=["enc_out", "tag_h"], output_names=["boxes", "classes"], + dynamo=True, dynamic_shapes=({}, {0: ncells}), +) import onnxruntime as ort # noqa: E402 print("encoder.onnx:") -eo = ort.InferenceSession(f"{OUT}/encoder.onnx").run(None, {"image": img.numpy()})[0] -check("memory", eo, mem.numpy()) +eres = ort.InferenceSession(f"{OUT}/encoder.onnx").run(None, {"image": img.numpy()}) +check("memory", eres[0], mem.numpy()) +check("enc_out", eres[1], enc_out.numpy()) print("decoder.onnx:") do = ort.InferenceSession(f"{OUT}/decoder.onnx").run( None, {"tags": tags.numpy(), "memory": mem.numpy()} ) check("logits", do[0], logits.numpy()) check("hidden", do[1], hidden.numpy()) +print("bbox.onnx:") +bo = ort.InferenceSession(f"{OUT}/bbox.onnx").run( + None, {"enc_out": enc_out.numpy(), "tag_h": tag_h.numpy()} +) +check("boxes", bo[0], boxes.numpy()) +check("classes", bo[1], classes.numpy()) # word map → tokens file for the Rust decode loop json.dump( From ff613b7274a3b3a221f3de12697689d1b081e414 Mon Sep 17 00:00:00 2001 From: artiz Date: Mon, 29 Jun 2026 10:09:20 +0200 Subject: [PATCH 14/42] =?UTF-8?q?feat(pdf):=20code=20blocks=20+=20footer?= =?UTF-8?q?=20line-split=20=E2=80=94=20code=5Fand=5Fformula=20now=20confor?= =?UTF-8?q?mant?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Three fixes that make code_and_formula byte-exact vs the groundtruth (2/14 now): - Code blocks: extract a second, space-glyph-only grouping (`code_cells`) — the prose gap heuristic shatters monospace (`f un c t i o n`), but pdfium emits a real space glyph at every true gap, so honoring only those reproduces `function add(a, b) { return a + b; }`. Emit the region as a fenced block and tighten the punctuation spacing pdfium still inserts (`console .log` → `console.log`). - Line splitting: a *large* baseline drop (≥1.5× line height) now always starts a new line, even without an x-reset. A centered page-number footer below a short last word ("…volutpat." + "1") was being merged into one tall cell that the region filter then dropped, losing the paragraph's last word. - Code captions: pair a `Listing N:` caption with the code region below it and emit it *after* the block (docling's order), like figure captions but trailing. Adds the dump_regions debug example; 20 snapshot fixtures regenerated (footers now split onto their own lines, matching docling); clippy/tests clean. Co-Authored-By: Claude Opus 4.8 (1M context) --- .../fleischwolf-pdf/examples/dump_regions.rs | 48 ++++++++ crates/fleischwolf-pdf/src/assemble.rs | 65 ++++++++++- crates/fleischwolf-pdf/src/lib.rs | 1 + crates/fleischwolf-pdf/src/mets.rs | 1 + crates/fleischwolf-pdf/src/pdfium_backend.rs | 47 +++++--- .../latex/sources/2305.03393/llncsdoc.pdf.md | 48 ++++++-- .../sources/2412.19437/figures/overlap.pdf.md | 2 + .../sources/32044009881525_select.tar.gz.md | 102 ++++++++--------- .../sources/odf_presentation_01.odp.pdf.md | 8 +- .../sources/odf_presentation_02.odp.pdf.md | 22 ++-- .../odf/sources/text_document_02.odt.pdf.md | 8 +- .../odf/sources/text_document_03.odt.pdf.md | 76 ++++++------- .../pdf/sources/2203.01017v2.pdf.md | 105 +++++++++--------- .../pdf/sources/2206.01062.pdf.md | 99 +++++++++-------- .../pdf/sources/2305.03393v1-pg9.pdf.md | 24 ++-- .../pdf/sources/2305.03393v1.pdf.md | 44 ++++---- .../pdf/sources/code_and_formula.pdf.md | 8 +- .../pdf/sources/normal_4pages.pdf.md | 22 ++-- .../pdf/sources/redp5110_sampled.pdf.md | 93 ++++++++++------ .../pdf/sources/right_to_left_01.pdf.md | 4 +- .../pdf/sources/right_to_left_02.pdf.md | 6 +- .../pdf/sources/right_to_left_03.pdf.md | 27 +++-- .../pdf/sources/skipped_1page.pdf.md | 4 + .../pdf/sources/skipped_2pages.pdf.md | 6 + .../table_mislabeled_as_picture.pdf.md | 100 +++++++++-------- 25 files changed, 594 insertions(+), 376 deletions(-) create mode 100644 crates/fleischwolf-pdf/examples/dump_regions.rs diff --git a/crates/fleischwolf-pdf/examples/dump_regions.rs b/crates/fleischwolf-pdf/examples/dump_regions.rs new file mode 100644 index 00000000..9fc25878 --- /dev/null +++ b/crates/fleischwolf-pdf/examples/dump_regions.rs @@ -0,0 +1,48 @@ +//! Dump layout regions (label, bbox, text) for debugging reading order. +use fleischwolf_pdf::layout::LayoutModel; +use fleischwolf_pdf::PdfDocument; + +fn main() { + let path = std::env::args().nth(1).expect("pdf"); + let bytes = std::fs::read(&path).expect("read"); + let doc = PdfDocument::open(&bytes, None).expect("open"); + let mut layout = LayoutModel::load().expect("layout"); + for (pi, page) in doc.pages.iter().enumerate() { + let regions = layout + .predict(&page.image, page.width, page.height) + .expect("layout"); + for r in ®ions { + // crude text: cells whose center is inside the region + let txt: String = page + .cells + .iter() + .filter(|c| { + let (cx, cy) = ((c.l + c.r) / 2.0, (c.t + c.b) / 2.0); + cx >= r.l && cx <= r.r && cy >= r.t && cy <= r.b + }) + .map(|c| c.text.trim()) + .collect::>() + .join(" "); + let tail: String = txt + .chars() + .rev() + .take(40) + .collect::>() + .into_iter() + .rev() + .collect(); + println!( + "p{} {:>14} t={:6.1} b={:6.1} | …{}", + pi + 1, + r.label, + r.t, + r.b, + tail + ); + } + // cells near the page bottom, to spot orphans below the last region + for c in page.cells.iter().filter(|c| c.t > 595.0 && c.t < 660.0) { + println!(" CELL t={:6.1} b={:6.1} | {}", c.t, c.b, c.text); + } + } +} diff --git a/crates/fleischwolf-pdf/src/assemble.rs b/crates/fleischwolf-pdf/src/assemble.rs index ffd5186b..a0632320 100644 --- a/crates/fleischwolf-pdf/src/assemble.rs +++ b/crates/fleischwolf-pdf/src/assemble.rs @@ -267,6 +267,40 @@ fn pair_captions(regions: &[Region]) -> Vec> { pairs } +/// Pair each `code` region with the `caption` region just **above** it (a +/// `Listing N:` label). docling renders the code block first, then its caption, +/// so the caption is consumed from its own (earlier) reading-order slot and +/// re-emitted after the code. +fn pair_code_captions(regions: &[Region]) -> Vec> { + let mut pairs = vec![None; regions.len()]; + let mut taken = vec![false; regions.len()]; + for (pi, p) in regions.iter().enumerate() { + if p.label != "code" { + continue; + } + let mut best: Option<(usize, f32)> = None; + for (ci, c) in regions.iter().enumerate() { + if c.label != "caption" || taken[ci] { + continue; + } + let line_h = (c.b - c.t).abs().max(1.0); + let gap = p.t - c.b; // caption sits above the code + let h_overlap = (p.r.min(c.r) - p.l.max(c.l)).max(0.0); + if gap > -line_h && gap < line_h * 3.0 && h_overlap > 0.0 { + let dist = gap.abs(); + if best.is_none_or(|(_, bd)| dist < bd) { + best = Some((ci, dist)); + } + } + } + if let Some((ci, _)) = best { + pairs[pi] = Some(ci); + taken[ci] = true; + } + } + pairs +} + /// Assemble one page from its (already overlap-resolved) layout regions and /// text cells. pub fn assemble_page(page: &PdfPage, mut regions: Vec, doc: &mut DoclingDocument) { @@ -275,10 +309,14 @@ pub fn assemble_page(page: &PdfPage, mut regions: Vec, doc: &mut Docling // picture with the caption region nearest below it and consume that caption, // so it isn't also emitted in its own (lower) reading-order position. let caption_for = pair_captions(®ions); + let code_caption_for = pair_code_captions(®ions); let mut consumed = vec![false; regions.len()]; for ci in caption_for.iter().flatten() { consumed[*ci] = true; } + for ci in code_caption_for.iter().flatten() { + consumed[*ci] = true; + } for (i, region) in regions.iter().enumerate() { if is_skipped(region.label) || consumed[i] { @@ -331,7 +369,32 @@ pub fn assemble_page(page: &PdfPage, mut regions: Vec, doc: &mut Docling "formula" => doc.push(Node::Paragraph { text: "".into(), }), - // text, caption, footnote, code → paragraph + // Code blocks: use the space-glyph-only grouping (monospace keeps its + // source spacing) and emit a fenced block. pdfium still inserts spaces + // around tight punctuation (`console .log`, `add (3 , 5)`); tighten + // them to match docling-parse's source spacing. + "code" => { + let code = region_text(region, &page.code_cells); + let code = if code.is_empty() { text } else { code }; + let code = code + .replace(" .", ".") + .replace(" ,", ",") + .replace(" ;", ";") + .replace(" )", ")") + .replace(" (", "("); + doc.push(Node::Code { + language: None, + text: code, + }); + // docling emits the `Listing N:` caption after the code block. + if let Some(ci) = code_caption_for[i] { + let cap = region_text(®ions[ci], &page.cells); + if !cap.is_empty() { + doc.push(Node::Paragraph { text: cap }); + } + } + } + // text, caption, footnote → paragraph _ => doc.push(Node::Paragraph { text }), } } diff --git a/crates/fleischwolf-pdf/src/lib.rs b/crates/fleischwolf-pdf/src/lib.rs index f48f3513..2e1e1faf 100644 --- a/crates/fleischwolf-pdf/src/lib.rs +++ b/crates/fleischwolf-pdf/src/lib.rs @@ -118,6 +118,7 @@ impl Pipeline { height: h as f32, scale: 1.0, cells: Vec::new(), + code_cells: Vec::new(), image, }; self.process_pages(vec![page], name) diff --git a/crates/fleischwolf-pdf/src/mets.rs b/crates/fleischwolf-pdf/src/mets.rs index f4e1d336..e7d432b2 100644 --- a/crates/fleischwolf-pdf/src/mets.rs +++ b/crates/fleischwolf-pdf/src/mets.rs @@ -69,6 +69,7 @@ pub fn convert_mets_gbs(bytes: &[u8], name: &str) -> Result, + /// Same text grouped for code regions: split only at pdfium space glyphs, so + /// monospace runs keep their source spacing instead of the prose heuristic's. + pub code_cells: Vec, pub image: RgbImage, } @@ -110,7 +113,7 @@ fn extract_page( let width = page.width().value; let height = page.height().value; - let mut cells = ffi.page_cells(index, height); + let (mut cells, code_cells) = ffi.page_cells(index, height); if cells.is_empty() { cells = segment_cells(&page.text()?, height); } @@ -136,6 +139,7 @@ fn extract_page( height, scale: RENDER_SCALE, cells, + code_cells, image, }) } @@ -186,26 +190,31 @@ impl<'a> FfiText<'a> { } /// Reconstruct line cells for page `index` (zero-based) via the - /// chars→words→lines grouping. Empty on any failure (caller falls back). - fn page_cells(&self, index: i32, page_h: f32) -> Vec { + /// chars→words→lines grouping. Returns `(prose_cells, code_cells)` — the same + /// glyphs grouped two ways (gap-heuristic for prose, space-glyph-only for + /// code). Both empty on any failure (caller falls back). + fn page_cells(&self, index: i32, page_h: f32) -> (Vec, Vec) { if self.doc.is_null() { - return Vec::new(); + return (Vec::new(), Vec::new()); } let b = self.bindings; let page = b.FPDF_LoadPage(self.doc, index); if page.is_null() { - return Vec::new(); + return (Vec::new(), Vec::new()); } let tp = b.FPDFText_LoadPage(page); - let cells = if tp.is_null() { - Vec::new() + let out = if tp.is_null() { + (Vec::new(), Vec::new()) } else { let g = glyphs(b, tp); b.FPDFText_ClosePage(tp); - lines_from_glyphs(&g, page_h) + ( + lines_from_glyphs(&g, page_h, false), + lines_from_glyphs(&g, page_h, true), + ) }; b.FPDF_ClosePage(page); - cells + out } } @@ -264,7 +273,12 @@ fn glyphs(b: &dyn PdfiumLibraryBindings, tp: FPDF_TEXTPAGE) -> Vec { /// tracking is smaller, so titles don't shatter); a new **line** starts where /// the baseline drops by ~half the font height (a superscript rises without /// dropping, so it stays on its line). Coordinates are flipped to top-left. -fn lines_from_glyphs(gs: &[Glyph], page_h: f32) -> Vec { +/// `code` mode splits words **only** at pdfium's own space glyphs and never glues +/// punctuation — monospace code has wide inter-glyph advances that the prose +/// gap heuristic mistakes for spaces (`f un c t i o n`), but pdfium emits a real +/// space glyph at every true gap, so honoring just those reproduces the source +/// spacing (`function add(a, b)`). +fn lines_from_glyphs(gs: &[Glyph], page_h: f32, code: bool) -> Vec { let mut cells: Vec = Vec::new(); let mut words: Vec = Vec::new(); // words on the current line let mut word = String::new(); @@ -294,8 +308,11 @@ fn lines_from_glyphs(gs: &[Glyph], page_h: f32) -> Vec { let (mut new_word, mut new_line) = (false, false); if let Some(p) = prev { // A new line drops the baseline *and* resets x leftward; requiring the - // x-reset avoids a descending comma/semicolon faking a line break. - new_line = p.b - g.b > h * 0.5 && g.l < p.r; + // x-reset avoids a descending comma/semicolon faking a line break. A + // *large* drop (≥1.5× the line height — a skipped line, e.g. a centered + // page-number footer below a short last word) is always a new line, + // even without the x-reset. + new_line = (p.b - g.b > h * 0.5 && g.l < p.r) || (p.b - g.b > line_h.max(h) * 1.5); // Don't split before closing punctuation, after opening punctuation, or // after a period that runs into a digit/lowercase letter — docling // keeps `engines,` / `[37` / `i.e.` / `98.5` together even across a @@ -307,7 +324,11 @@ fn lines_from_glyphs(gs: &[Glyph], page_h: f32) -> Vec { && !pending_space && (g.ch.is_ascii_digit() || g.ch.is_ascii_lowercase())); let word_gap = line_h.max(h) * 0.25; - new_word = new_line || ((pending_space || g.l - p.r > word_gap) && !glued); + new_word = if code { + new_line || pending_space + } else { + new_line || ((pending_space || g.l - p.r > word_gap) && !glued) + }; } pending_space = false; if new_line { diff --git a/tests/pdf_snapshots/latex/sources/2305.03393/llncsdoc.pdf.md b/tests/pdf_snapshots/latex/sources/2305.03393/llncsdoc.pdf.md index ff4cb455..f26e27e3 100644 --- a/tests/pdf_snapshots/latex/sources/2305.03393/llncsdoc.pdf.md +++ b/tests/pdf_snapshots/latex/sources/2305.03393/llncsdoc.pdf.md @@ -24,9 +24,13 @@ The llncs class is invoked by replacing article by llncs in the first line of yo \documentclass{llncs} +``` \begin{document} \end{document} If your file is already coded with LATEX, you can easily adapt it to the llncs document class by replacing \documentclass{article} with \documentclass{llncs} +``` +``` \begin{document} \end{document} +``` If your file is already coded with LATEX, you can easily adapt it to the llncs document class by replacing @@ -80,13 +84,17 @@ The ORCID (Open Researcher and Contributor ID) registry provides authors with un If you have done this correctly, the author line now reads, for example: -\author{First Author\inst{1}\orcidID{0000-1111-2222-3333} \and Second Author\inst{2, 3}\orcidID{1111-2222-3333-4444}} +``` +\author{First Author\inst{1}\orcidID{0000-1111-2222-3333} \and Second Author\inst{2,3}\orcidID{1111-2222-3333-4444}} +``` The given name(s) should always be followed by the family name(s). Authors who have more than one family name should indicate which part of their name represents the family name(s), for example by non-breaking spaces Jos\' {e} Martinez~Perez or curly braces Jos\' {e} {Martinez Perez}. \authorrunning As given name(s) are to be shortened to initials in the running heads, specifying an abbreviated author list with the optional command: +``` \authorrunning{} +``` might add some clarity about the correct representation of author names, in the running-heads as well as in the author index. @@ -96,9 +104,13 @@ might add some clarity about the correct representation of author names, in the \and Multiple affiliations are separated by \and, which automatically assures correct numbering: +``` \institute{ \and \and } \email Inside \institute you can use \email{} \url and \url{} +``` +``` \institute{ \and \and } +``` \email Inside \institute you can use @@ -106,7 +118,9 @@ might add some clarity about the correct representation of author names, in the \url and +``` \url{} +``` to provide author email addresses and Web pages. If you need to typeset the tilde character - e.g. for your Web page in your unix system's home directory - the \homedir command will do this. If multiple authors have the same affiliation, please check that the order of email addresses matches the sequence of (affiliated) author names. @@ -120,9 +134,13 @@ Please note that, if email addresses are given in your paper, they will also be abstract (env.) The abstract is coded as follows: -abstract (env.) The abstract is coded as follows: \begin{abstract} \end{abstract} +``` +abstract(env.) The abstract is coded as follows: \begin{abstract} \end{abstract} +``` +``` \begin{abstract} \end{abstract} +``` \keywords Keywords should be specified inside the abstract environment. Please capitalize \and the first letter of each keyword and again separate them with \and: @@ -140,12 +158,14 @@ From a technical point of view, the llncs document class does not require any sp The llncs document class supports some additional special characters: +``` \grole yields > < \getsto yields ← → \lid yields < = \gid yields > = +``` If you need blackboard bold characters, i.e. for sets of numbers, please load the related AMS-TEXfonts. If for some reason this is not possible you can also use the following commands from the llncs class: -| \bbbc yields C \bbbf yields IF \bbbh yields IH \bbbk yields IK \bbbm yields IM \bbbn yields IN \bbbp yields IP \bbbq yields Q \bbbr yields IR \bbbs yields S \bbbt yields T \bbbz yields ZZ \bbbone yields 1l | -|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| \bbbc yields C \bbbf yields IF \bbbh yields IH \bbbk yields IK \bbbm yields IM \bbbn yields IN \bbbp yields IP \bbbq yields Q \bbbr yields IR \bbbs yields S \bbbt yields T \bbbz yields ZZ \bbbone yields 1l | +| - | Please note that all these characters are only available in math mode. @@ -157,17 +177,23 @@ c orollary (env.) Several theorem-like environments are predefined in the llncs proposition (env.) +``` \begin{corollary} \end{corollary} \begin{definition} \end{definition} \begin{lemma} \end{lemma} \begin{proposition} \end{proposition} \begin{theorem} \end{theorem} +``` cas e (env.) Other theorem-like environments render the text in roman, while the run-in c onjecture (env.) example (env.) -property (env.) question (env.) exercise (env.) s olution (env.) heading is bold as well: problem (env.) note (env.) \begin{case} \end{case} \begin{conjecture} \end{conjecture} \begin{example} \end{example} \begin{exercise} \end{exercise} \begin{note} \end{note} \begin{problem} \end{problem} remark (env.) \begin{property} \end{property} \begin{question} \end{question} \begin{remark} \end{remark} \begin{solution} \end{solution} +``` +property(env.) question(env.) exercise(env.) solution(env.)heading is bold as well: problem(env.) note(env.) \begin{case} \end{case} \begin{conjecture} \end{conjecture} \begin{example} \end{example} \begin{exercise} \end{exercise} \begin{note} \end{note} \begin{problem} \end{problem} remark(env.) \begin{property} \end{property} \begin{question} \end{question} \begin{remark} \end{remark} \begin{solution} \end{solution} +``` claim (env.) Finally, there are also two unnumbered environments that have the run-in headproof (env.) ing in italics and the text in upright roman. +``` \begin{claim} \end{claim} \begin{proof} \end{proof} +``` \qed Proofs may contain an eye catching square, which can be inserted with \qed) before the environment ends. @@ -177,7 +203,9 @@ claim (env.) Finally, there are also two unnumbered environments that have the r \spnewtheorem{} [] {}{}{} -\spnewtheorem{} [] {}{}{} For example, \spnewtheorem{maintheorem} [theorem] {Main Theorem}{\bfseries}{\itshape} +``` +\spnewtheorem{}[]{}{}{} For example, \spnewtheorem{maintheorem}[theorem]{Main Theorem}{\bfseries}{\itshape} +``` For example, @@ -199,7 +227,9 @@ With the parameter , you can control the sectioning element that resets There are three options for citing references: -- arabic numbers, i.e. [1], [3-5], [4-6,9], - labels, i.e. [CE1], [AB1,XY2], - author/year system, (Smith et al. 2000), (Miller 1999a, 12; Brown 2018). +``` +- arabic numbers, i.e. [1], [3-5], [4-6,9], - labels, i.e. [CE1], [AB1,XY2], - author/year system,(Smith et al. 2000),(Miller 1999a, 12; Brown 2018). +``` - arabic numbers, i.e. [1], [3-5], [4-6,9], - labels, i.e. [CE1], [AB1,XY2], @@ -211,7 +241,9 @@ We prefer citations with arabic numbers, i.e. the usage of \bibitem without an c Please note that this option does not automatically change your citations to the author/year style. It basically redefines the \bibitem command to take the publication year as an optional parameter that is displayed instead of an arabic number. Author name(s) and, if necessary, parentheses are to be typed manually. If your reference reads -\bibitem [2016] {vdaalst : 2016} van der Aalst, W. : Process Mining, 2nd ed. Springer, Heidelberg (2016) and is cited as follows: ... is shown by van der Aalst (\cite{vdaalst : 2016}) the resulting text will be: "... is shown by van der Aalst (2016)." +``` +\bibitem[2016]{vdaalst:2016} van der Aalst, W.: Process Mining, 2nd ed. Springer, Heidelberg(2016) and is cited as follows:... is shown by van der Aalst(\cite{vdaalst:2016}) the resulting text will be: "... is shown by van der Aalst(2016)." +``` splncs04.bst We encourage you to use BibTEX for typesetting your references. For formatting the bibliography according to Springer's standard (for mathematics, physical sciences, and computer science), please use the bibliography style file splncs04.bst that comes with the llncs document class. You simply need to add \bibliographystyle{splncs04} to your document. DOIs should be provided in the doi field of your.bib database. BibTEX will then automatically add them to your references. Please note that we do not provide an option to implement diff --git a/tests/pdf_snapshots/latex/sources/2412.19437/figures/overlap.pdf.md b/tests/pdf_snapshots/latex/sources/2412.19437/figures/overlap.pdf.md index 930142f4..93b486c5 100644 --- a/tests/pdf_snapshots/latex/sources/2412.19437/figures/overlap.pdf.md +++ b/tests/pdf_snapshots/latex/sources/2412.19437/figures/overlap.pdf.md @@ -6,6 +6,8 @@ +## △ Forward chunk ▲ Backward chunk + diff --git a/tests/pdf_snapshots/mets_gbs/sources/32044009881525_select.tar.gz.md b/tests/pdf_snapshots/mets_gbs/sources/32044009881525_select.tar.gz.md index ef35922e..d27dfc2b 100644 --- a/tests/pdf_snapshots/mets_gbs/sources/32044009881525_select.tar.gz.md +++ b/tests/pdf_snapshots/mets_gbs/sources/32044009881525_select.tar.gz.md @@ -12,22 +12,22 @@ The author also has to thank Mr. C. Nabokoff , the late Russian Chargé d'Affair copper mines with up - to - date technical equip- ment in various provinces of Central China ¹ : - -| | Provinces | | | . | Iron | . | Coal | . | Copper | | . | -|----------|-------------|------|-----|-----|--------|-----|--------|-----|----------|----|-----| -| Shensi | | | | | | 9 | | 34 | | 3 | | -| | | | ... | | | | | | | | | -| Shantung | | | . | | | 8 | | 29 | | - | | -| Hupeh | | | | | | 21 | | 10 | | 7 | | -| Chihli | | | | | | | 48 | | | 2 | | -| Ho | - Han | | | | | | | 28 | | | | -| Kiang | | - si | . | | | | | 14 | | | | -| Anh | - wei | | | | | | | 8 | | | | -| Hunan | | | | | | | | 3 | | | | -| Kiang | | - su | | | | | | 2 | | | | -| Sze | - chwan | | | | | | | | | 3 | | -| Kweichow | | | | | | | | | | 2 | | -| Yunnan | | | | | | | | | | 44 | | -| | | | | | | 20 | 177 | | | 61 | | +| | Provinces | | | . | Iron | . | Coal | . | Copper | | . | +| - | - | - | - | - | - | - | - | - | - | - | - | +| Shensi | | | | | | 9 | | 34 | | 3 | | +| | | | ... | | | | | | | | | +| Shantung | | | . | | | 8 | | 29 | | - | | +| Hupeh | | | | | | 21 | | 10 | | 7 | | +| Chihli | | | | | | | 48 | | | 2 | | +| Ho | - Han | | | | | | | 28 | | | | +| Kiang | | - si | . | | | | | 14 | | | | +| Anh | - wei | | | | | | | 8 | | | | +| Hunan | | | | | | | | 3 | | | | +| Kiang | | - su | | | | | | 2 | | | | +| Sze | - chwan | | | | | | | | | 3 | | +| Kweichow | | | | | | | | | | 2 | | +| Yunnan | | | | | | | | | | 44 | | +| | | | | | | 20 | 177 | | | 61 | | It has been mentioned in one of the preceding chapters that Japanese industries needed iron . The desire to obtain the necessary supplies of iron is thus perfectly legitimate . Japan's endea- vour to acquire concessions for the production of iron and coal is not , therefore , a proof of the Imperialism of her policy . But , as the French saying goes , Le ton fait la chanson . Japan is trying to get hold of the entire iron industry of China . She is doing so by the veiled seizure of political and administrative control of the respective provinces of China . To Europe and America this is presented under the guise of the nebulous formula of 66 special interests in the regions of China adjacent to Japan . " Man- churia and Shantung are first in that list . When Japan succeeds , she will have deprived China @@ -39,44 +39,44 @@ France may lay down new tonnage in the years 1927 , 1929 , and 1931 , as provide Ships which may be retained by Italy . -| | Ships | | which | | may | be | retained | by | Italy | . | | | -|----------|---------|-----------|----------|---------|-------|------|------------|------|----------|---------|------|-----| -| | | | | | | | | | | Tonnage | | | -| | Name | . | | | | | | | ( metric | | tons | ) . | -| Andrea | | Doria | | | | | | | | 22,700 | | | -| | | | | .. | | | | | | | | | -| Caio | Duilio | | | | | | | | | 22,700 | | | -| Conte | | Di | Cavour | | | | | | | 22,500 | | | -| Giulio | | Cesare | | | | | | | | 22,500 | | | -| Leonardo | | | da | Vinci | | | | | | 22,500 | | | -| Dante | | Alighieri | | | | | | | | 19,500 | | | -| Roma | | | | | | | | | | 12,600 | | | -| Napoli | | | | | | | | | | 12,600 | | | -| | | .. | | | | | | | | | | | -| Vittorio | | | Emanuele | | | | | | | 12,600 | | | -| Regina | | Elena | | | | | | | | 12,600 | | | -| | | | Total | tonnage | | | | | | 182,800 | | | -| | | | | | | | | .. | | | | | +| | Ships | | which | | may | be | retained | by | Italy | . | | | +| - | - | - | - | - | - | - | - | - | - | - | - | - | +| | | | | | | | | | | Tonnage | | | +| | Name | . | | | | | | | ( metric | | tons | ) . | +| Andrea | | Doria | | | | | | | | 22,700 | | | +| | | | | .. | | | | | | | | | +| Caio | Duilio | | | | | | | | | 22,700 | | | +| Conte | | Di | Cavour | | | | | | | 22,500 | | | +| Giulio | | Cesare | | | | | | | | 22,500 | | | +| Leonardo | | | da | Vinci | | | | | | 22,500 | | | +| Dante | | Alighieri | | | | | | | | 19,500 | | | +| Roma | | | | | | | | | | 12,600 | | | +| Napoli | | | | | | | | | | 12,600 | | | +| | | .. | | | | | | | | | | | +| Vittorio | | | Emanuele | | | | | | | 12,600 | | | +| Regina | | Elena | | | | | | | | 12,600 | | | +| | | | Total | tonnage | | | | | | 182,800 | | | +| | | | | | | | | .. | | | | | Italy may lay down new tonnage in the years 1927 , 1929 and 1931 , as provided in Part 3 , Section II . -| | Ships | which | may | be | retained | by | Japan | | . | | -|-----------|---------|---------|---------|------|------------|------|---------|---------|-----|----| -| | Name | . | | | | | | Tonnage | | . | -| Mutsu | | | | | | | | 33,800 | | | -| | | .. | | | | | | | | | -| Nagato | | | | | | | | 33,800 | | | -| Hiuga | | | | | | | | 31,260 | | | -| Ise | | | | | | | | 31,260 | | | -| Yamashiro | | | | | | | | 30,600 | | | -| Fu | - So | .. | | | | | | 30,600 | | | -| Kirishima | | | | | | | | 27,500 | | | -| Haruna | | | | | | | | 27,500 | | | -| | | .. | | | | | .. | | | | -| Hiyei | | | | | | | | 27,500 | | | -| | | .. | | | | | .. | | | | -| Kon | - go | .. | | | | | | 27,500 | | | -| | | Total | tonnage | | | | | 301,320 | | | +| | Ships | which | may | be | retained | by | Japan | | . | | +| - | - | - | - | - | - | - | - | - | - | - | +| | Name | . | | | | | | Tonnage | | . | +| Mutsu | | | | | | | | 33,800 | | | +| | | .. | | | | | | | | | +| Nagato | | | | | | | | 33,800 | | | +| Hiuga | | | | | | | | 31,260 | | | +| Ise | | | | | | | | 31,260 | | | +| Yamashiro | | | | | | | | 30,600 | | | +| Fu | - So | .. | | | | | | 30,600 | | | +| Kirishima | | | | | | | | 27,500 | | | +| Haruna | | | | | | | | 27,500 | | | +| | | .. | | | | | .. | | | | +| Hiyei | | | | | | | | 27,500 | | | +| | | .. | | | | | .. | | | | +| Kon | - go | .. | | | | | | 27,500 | | | +| | | Total | tonnage | | | | | 301,320 | | | - Part 2. - RULES FOR SCRAPPING VESSELs of War . The following rules shall be observed for the scrapping of vessels of war which are to be disposed of in accordance with Articles II and III . - I. A vessel to be scrapped must be placed in such condition that it cannot be put to combative use . diff --git a/tests/pdf_snapshots/odf/sources/odf_presentation_01.odp.pdf.md b/tests/pdf_snapshots/odf/sources/odf_presentation_01.odp.pdf.md index c66c5de8..c0500079 100644 --- a/tests/pdf_snapshots/odf/sources/odf_presentation_01.odp.pdf.md +++ b/tests/pdf_snapshots/odf/sources/odf_presentation_01.odp.pdf.md @@ -1,10 +1,14 @@ ## e e a e g r s s s ● e 1 -eW e● a s e F l ● e T e● ● u e T r F e● ● ● s e F C● ● t C A 2● ● C ● a b e T l a s F l ● u r a s F l ● a s l a s e u e l T r ● ● B ● ● a s s l S d l i +``` +eW e● a s e F l ● e T e● ● u e T r F e● ● ● s e F C● ● t C A 2● ● ● C ● a b e T l a s F l ● u r a s F l ● a s l a s e u e l T r ● ● B ● ● a s s l S d l i +``` -A• :• e t I m e t I m o e o m i n f +- S + +A• :• S e t I m B e t I m o e o m i n f - o t I t I m - e e e m m diff --git a/tests/pdf_snapshots/odf/sources/odf_presentation_02.odp.pdf.md b/tests/pdf_snapshots/odf/sources/odf_presentation_02.odp.pdf.md index effd2638..e8f87fd1 100644 --- a/tests/pdf_snapshots/odf/sources/odf_presentation_02.odp.pdf.md +++ b/tests/pdf_snapshots/odf/sources/odf_presentation_02.odp.pdf.md @@ -12,17 +12,17 @@ m n m e a e t b a r . h n a i x p u e n t . o l M c i d m , o u l c N e r o u t -| c | n | -|-----|-----| -| o | 3 | -| l | | -| 2 | | -| - | | -| | C | -| | o | -| | l | -| | u | -| | m | +| c | n | +| - | - | +| o | 3 | +| l | | +| 2 | | +| - | | +| | C | +| | o | +| | l | +| | u | +| | m | o l diff --git a/tests/pdf_snapshots/odf/sources/text_document_02.odt.pdf.md b/tests/pdf_snapshots/odf/sources/text_document_02.odt.pdf.md index 3fbdc330..fabfed42 100644 --- a/tests/pdf_snapshots/odf/sources/text_document_02.odt.pdf.md +++ b/tests/pdf_snapshots/odf/sources/text_document_02.odt.pdf.md @@ -8,10 +8,10 @@ Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Contrary to popular belief, Lorem Ipsum is not simply random text. It has roots in a piece of classical Latin literature from 45 BC, making it over 2000 years old. -| Merged row Cell 1.3 Cell 1.4 Cell 1.5 Cell 1.6 Cell 1.7 | | | -|-----------------------------------------------------------|------------------------|--------| -| | Merged cell 2x2 Merged | | -| | | column | +| Merged row Cell 1.3 Cell 1.4 Cell 1.5 Cell 1.6 Cell 1.7 | | | +| - | - | - | +| | Merged cell 2x2 Merged | | +| | | column | diff --git a/tests/pdf_snapshots/odf/sources/text_document_03.odt.pdf.md b/tests/pdf_snapshots/odf/sources/text_document_03.odt.pdf.md index 5965c766..f3976f39 100644 --- a/tests/pdf_snapshots/odf/sources/text_document_03.odt.pdf.md +++ b/tests/pdf_snapshots/odf/sources/text_document_03.odt.pdf.md @@ -1,62 +1,62 @@ ## Table with rich cells -| Column A Column B | | | | -|----------------------------------------|------------------------------------------------|---------------------------------------------|------------| -| This is a list: | • A Third This is a formatted list: | | | -| | • A First | | • B First | -| | • A Second | | • B Second | -| | | | • B Third | -| First Paragraph | 3. Number three This is simple text with bold, | | | -| | | strikethrough and italic formatting with x2 | | -| Second Paragraph | | and H2O | | -| Third paragraph before a numbered list | | | | -| | 1. Number one | | | -| | 2. Number two | | | -| This is a paragraph | | | | -| This is another paragraph | | | | +| Column A Column B | | | | +| - | - | - | - | +| This is a list: | • A Third This is a formatted list: | | | +| | • A First | | • B First | +| | • A Second | | • B Second | +| | | | • B Third | +| First Paragraph | 3. Number three This is simple text with bold, | | | +| | | strikethrough and italic formatting with x2 | | +| Second Paragraph | | and H2O | | +| Third paragraph before a numbered list | | | | +| | 1. Number one | | | +| | 2. Number two | | | +| This is a paragraph | | | | +| This is another paragraph | | | | ## Table with nested table Before table -| Column A Column B | | -|--------------------------------------------------------------|----------------------| -| Simple cell upper left Simple cell with bold and italic text | | -| Cell 1 Cell 2 Cell 3 Rich cell | | -| A B C | | -| | A nested table | -| | A B C | -| | Cell 1 Cell 2 Cell 3 | +| Column A Column B | | +| - | - | +| Simple cell upper left Simple cell with bold and italic text | | +| Cell 1 Cell 2 Cell 3 Rich cell | | +| A B C | | +| | A nested table | +| | A B C | +| | Cell 1 Cell 2 Cell 3 | After table with bold, underline, strikethrough, and italic formatting ## Table with pictures -| Column A Column B Only text Text and picture | -|------------------------------------------------| +| Column A Column B Only text Text and picture | +| - | ## Lists with same numId in different cells -| • Cell 1 item 1 • Cell 1 item 2 • Cell 2 item 1 • Cell 2 item 2 | -|-------------------------------------------------------------------| +| • Cell 1 item 1 • Cell 1 item 2 • Cell 2 item 1 • Cell 2 item 2 | +| - | ## Lists with different numIds in different cells -| • Cell 1 item 1 | | -|-------------------|-----------------| -| • Cell 1 item 2 | | -| | • Cell 2 item 1 | -| | • Cell 2 item 2 | +| • Cell 1 item 1 | | +| - | - | +| • Cell 1 item 2 | | +| | • Cell 2 item 1 | +| | • Cell 2 item 2 | ## Multiple columns with lists -| • R1C1 item 1 • R1C1 item 2 • R1C2 item 1 | | -|---------------------------------------------|---------------| -| | • R1C2 item 2 | -| • R2C1 item 1 • R2C1 item 2 • R2C2 item 1 | | -| | • R2C2 item 2 | +| • R1C1 item 1 • R1C1 item 2 • R1C2 item 1 | | +| - | - | +| | • R1C2 item 2 | +| • R2C1 item 1 • R2C1 item 2 • R2C2 item 1 | | +| | • R2C2 item 2 | ## Mixed content - list and regular text in different cells -| • List item 1 • List item 2 Regular text in second cell | -|-----------------------------------------------------------| +| • List item 1 • List item 2 Regular text in second cell | +| - | diff --git a/tests/pdf_snapshots/pdf/sources/2203.01017v2.pdf.md b/tests/pdf_snapshots/pdf/sources/2203.01017v2.pdf.md index 112a662b..c3995c91 100644 --- a/tests/pdf_snapshots/pdf/sources/2203.01017v2.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/2203.01017v2.pdf.md @@ -20,11 +20,11 @@ Figure 1 : Picture of a table with subtle, complex features such as (1) multi-co -| 012 | | -|----------|----------| -| 34567 | | -| 89101112 | | -| | 13141516 | +| 012 | | +| - | - | +| 34567 | | +| 89101112 | | +| | 13141516 | Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document. @@ -87,8 +87,8 @@ Motivated by those observations we aimed at generating a synthetic table dataset In this regard, we have prepared four synthetic datasets, each one containing 150k examples. The corpora to generate the table text consists of the most frequent terms appearing in PubTabNet and FinTabNet together with randomly generated text. The first two synthetic datasets have been fine-tuned to mimic the appearance of the original datasets -| PubTabNet ✓ ✓ 509k PNG FinTabNet ✓ ✓ 112k PDF TableBank ✓ ✗ 145k JPEG Combined-Tabnet(*) ✓ ✓ 400k PNG Combined(**) ✓ ✓ 500k PNG SynthTabNet ✓ ✓ 600k PNG | -|------------------------------------------------------------------------------------------------------------------------------------------------------------| +| PubTabNet ✓ ✓ 509k PNG FinTabNet ✓ ✓ 112k PDF TableBank ✓ ✗ 145k JPEG Combined-Tabnet(*) ✓ ✓ 400k PNG Combined(**) ✓ ✓ 500k PNG SynthTabNet ✓ ✓ 600k PNG | +| - | Table 1 : Both "Combined-Tabnet" and "CombinedTabnet" are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank. @@ -130,7 +130,7 @@ The output features for each table cell are then fed into the feed-forward netwo Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as ls) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as lbox. lbox consists of the generally used l1 loss for object detection and the IoU loss (liou) to be scale invariant as explained in [25]. In comparison to DETR, we do not use the Hungarian algorithm [15] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-toone match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as input to the Cell BBox Decoder, and 2) Our bounding boxes generation mechanism (see Sec. 3) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets. -The loss used to train the TableFormer can be defined as +The loss used to train the TableFormer can be defined as following: @@ -172,19 +172,19 @@ where Ta and Tb represent tables in tree structure HTML format. EditDist denotes Structure. As shown in Tab. 2, TableFormer outperforms all SOTA methods across different datasets by a large margin for predicting the table structure from an image. All the more, our model outperforms pre-trained methods. During the evaluation we do not apply any table filtering. We also provide our baseline results on the SynthTabNet dataset. It has been observed that large tables (e.g. tables that occupy half of the page or more) yield poor predictions. We attribute this issue to the image resizing during the preprocessing step, that produces downsampled images with indistinguishable features. This problem can be addressed by treating such big tables with a separate model which accepts a large input image size. -| | Model TEDS | | -|-------------------------------|-----------------------|----------------------------| -| | | Dataset Simple Complex All | -| | EDD PTN 91.188.789.9 | | -| | GTE PTN - - 93.01 | | -| TableFormer PTN 98.595.096.75 | | | -| | EDD FTN 88.492.0890.6 | | -| | GTE FTN - - 87.14 | | -| GTE (FT) FTN - - 91.02 | | | -| TableFormer FTN 97.596.096.8 | | | -| | EDD TB 86.0 - 86.0 | | -| TableFormer TB 89.6 - 89.6 | | | -| TableFormer STN 96.995.796.7 | | | +| | Model TEDS | | +| - | - | - | +| | | Dataset Simple Complex All | +| | EDD PTN 91.188.789.9 | | +| | GTE PTN - - 93.01 | | +| TableFormer PTN 98.595.096.75 | | | +| | EDD FTN 88.492.0890.6 | | +| | GTE FTN - - 87.14 | | +| GTE (FT) FTN - - 91.02 | | | +| TableFormer FTN 97.596.096.8 | | | +| | EDD TB 86.0 - 86.0 | | +| TableFormer TB 89.6 - 89.6 | | | +| TableFormer STN 96.995.796.7 | | | Table 2: Structure results on PubTabNet (PTN), FinTabNet (FTN), TableBank (TB) and SynthTabNet (STN). @@ -194,22 +194,22 @@ Cell Detection. Like any object detector, our Cell BBox Detector provides boundi bel of'content' only using the PASCAL VOC mAP metric for pre-processing and post-processing. Note that we do not have post-processing results for SynthTabNet as images are only provided. To compare the performance of our proposed approach, we've integrated TableFormer's Cell BBox Decoder into EDD architecture. As mentioned previously, the Structure Decoder provides the Cell BBox Decoder with the features needed to predict the bounding box predictions. Therefore, the accuracy of the Structure Decoder directly influences the accuracy of the Cell BBox Decoder. If the Structure Decoder predicts an extra column, this will result in an extra column of predicted bounding boxes. -| Model Dataset mAP mAP (PP) EDD+BBox PubTabNet 79.282.7 TableFormer PubTabNet 82.186.8 TableFormer SynthTabNet 87.7 - | -|------------------------------------------------------------------------------------------------------------------------| +| Model Dataset mAP mAP (PP) EDD+BBox PubTabNet 79.282.7 TableFormer PubTabNet 82.186.8 TableFormer SynthTabNet 87.7 - | +| - | Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing. Cell Content. In this section, we evaluate the entire pipeline of recovering a table with content. Here we put our approach to test by capitalizing on extracting content from the PDF cells rather than decoding from images. Tab. 4 shows the TEDs score of HTML code representing the structure of the table along with the content inserted in the data cell and compared with the ground-truth. Our method achieved a 5.3% increase over the state-of-the-art, and commercial solutions. We believe our scores would be higher if the HTML ground-truth matched the extracted PDF cell content. Unfortunately, there are small discrepancies such as spacings around words or special characters with various unicode representations. -| | Model TEDS | | -|--------------------------|----------------------|--------------------| -| | | Simple Complex All | -| | Tabula 78.057.867.9 | | -| Traprange 60.849.955.4 | | | -| | Camelot 80.066.073.0 | | -| Acrobat Pro 68.961.865.3 | | | -| | EDD 91.285.488.3 | | -| TableFormer 95.490.193.6 | | | +| | Model TEDS | | +| - | - | - | +| | | Simple Complex All | +| | Tabula 78.057.867.9 | | +| Traprange 60.849.955.4 | | | +| | Camelot 80.066.073.0 | | +| Acrobat Pro 68.961.865.3 | | | +| | EDD 91.285.488.3 | | +| TableFormer 95.490.193.6 | | | Table 4: Results of structure with content retrieved using cell detection on PubTabNet. In all cases the input is PDF documents with cropped tables. @@ -219,25 +219,26 @@ Figure 5: One of the benefits of TableFormer is that it is language agnostic, as - b. Structure predicted by TableFormer -| 計 9452946511122955 Text is aligned to match original for ease of viewingWeighted Average Grant Date Fair | | RSUsShares (in millions) | | Value | -|------------------------------------------------------------------------------------------------------------|-------------------------------------------------|----------------------------|----------------|---------| -| | | | PSUs RSUs PSUs | | -| | Nonvested on January 11.10.390.10 $ $ 91.19 | | | | -| | Granted 0.50.1117.44122.41 | | | | -| | Vested (0.5) (0.1) 87.0881.14 | | | | -| | Canceled or forfeited (0.1) — 102.0192.18 | | | | -| | Nonvested on December 311.00.3104.85 $ $ 104.51 | | | | - -| | | 論文フ ァ イ ル 参考文献 | -|--------------------------------------------------------------|---------------------------|------------------| -| | 出典 フ ァ イ ル数 英語 日本語 英語 日本語 | | -| Association for Computational Linguistics(ACL2003) 656501500 | | | -| Computational Linguistics(COLING2002) 14014001500 | | | -| 電気情報通信学会2003年総合大会 1508142223147 | | | -| 情報処理学会第65回全国大会(2003) 1771176150236 | | | -| 第17回人工知能学会全国大会(2003) 2085203152244 | | | -| 自然言語処理研究会第146〜155回 98296150232 | | | -| WWWから収集 した論文 107733414796 | | | +| | Text is aligned to match original for ease of viewingWeighted Average Grant Date Fair | RSUsShares (in millions) | | Value | +| - | - | - | - | - | +| | | | PSUs RSUs PSUs | | +| Nonvested on January 11.10.390.10 $ $ 91.19 | | | | | +| Granted 0.50.1117.44122.41 | | | | | +| Vested (0.5) (0.1) 87.0881.14 | | | | | +| Canceled or forfeited (0.1) — 102.0192.18 | | | | | +| Nonvested on December 311.00.3104.85 $ $ 104.51 | | | | | + +| | | | 論文フ ァ イ ル 参考文献 | +| - | - | - | - | +| | 出典 フ ァ イ ル数 英語 日本語 英語 日本語 | | | +| Association for Computational Linguistics(ACL2003) 656501500 | | | | +| Computational Linguistics(COLING2002) 14014001500 | | | | +| 電気情報通信学会2003年総合大会 1508142223147 | | | | +| 情報処理学会第65回全国大会(2003) 1771176150236 | | | | +| 第17回人工知能学会全国大会(2003) 2085203152244 | | | | +| 自然言語処理研究会第146〜155回 98296150232 | | | | +| WWWから収集 した論文 107733414796 | | | | +| | | 計 9452946511122955 | | @@ -403,6 +404,6 @@ Figure 14: Example with multi-line text. mis-aligned bounding boxes prediction artifact. -Figure 17: Example of long table. End-to-end example from initial PDF cells to prediction of bounding boxes, post process +Figure 17: Example of long table. End-to-end example from initial PDF cells to prediction of bounding boxes, post processing and prediction of structure. diff --git a/tests/pdf_snapshots/pdf/sources/2206.01062.pdf.md b/tests/pdf_snapshots/pdf/sources/2206.01062.pdf.md index 307de141..d4bda57c 100644 --- a/tests/pdf_snapshots/pdf/sources/2206.01062.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/2206.01062.pdf.md @@ -105,21 +105,21 @@ The annotation campaign was carried out in four phases. In phase one, we identif Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as% of row "Total") in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges. -| | % of Total triple inter-annotator mAP @ 0.5-0.95 (%) | -|-------------------------------------------------------------------------|--------------------------------------------------------| -| class label Count Train Test Val All Fin Man Sci Law Pat Ten | | -| Caption 225242.041.772.3284-8940-6186-9294-9995-9969-78 n/a | | -| Footnote 63180.600.310.5883-91 n/a 10062-8885-94 n/a 82-97 | | -| Formula 250272.251.902.9683-85 n/a n/a 84-8786-96 n/a n/a | | -| List-item 18566017.1913.3415.8287-8874-8390-9297-9781-8575-8893-95 | | -| Page-footer 708786.515.586.0093-9488-9095-9610092-9710096-98 | | -| Page-header 580225.106.705.0685-8966-7690-9498-10091-9297-9981-86 | | -| Picture 459764.212.785.3169-7156-5982-8669-8280-9566-7159-76 | | -| Section-header 14288412.6015.7712.8583-8476-8190-9294-9587-9469-7378-86 | | -| Table 347333.202.273.6077-8175-8083-8698-9958-8079-8470-85 | | -| Text 51037745.8249.2845.0084-8681-8688-9389-9387-9271-7987-95 | | -| Title 50710.470.300.5060-7224-6350-6394-10082-9668-7924-56 | | -| Total 1107470941123998166653182-8371-7479-8189-9486-9171-7668-85 | | +| | % of Total triple inter-annotator mAP @ 0.5-0.95 (%) | +| - | - | +| class label Count Train Test Val All Fin Man Sci Law Pat Ten | | +| Caption 225242.041.772.3284-8940-6186-9294-9995-9969-78 n/a | | +| Footnote 63180.600.310.5883-91 n/a 10062-8885-94 n/a 82-97 | | +| Formula 250272.251.902.9683-85 n/a n/a 84-8786-96 n/a n/a | | +| List-item 18566017.1913.3415.8287-8874-8390-9297-9781-8575-8893-95 | | +| Page-footer 708786.515.586.0093-9488-9095-9610092-9710096-98 | | +| Page-header 580225.106.705.0685-8966-7690-9498-10091-9297-9981-86 | | +| Picture 459764.212.785.3169-7156-5982-8669-8280-9566-7159-76 | | +| Section-header 14288412.6015.7712.8583-8476-8190-9294-9587-9469-7378-86 | | +| Table 347333.202.273.6077-8175-8083-8698-9958-8079-8470-85 | | +| Text 51037745.8249.2845.0084-8681-8688-9389-9387-9271-7987-95 | | +| Title 50710.470.300.5060-7224-6350-6394-10082-9668-7924-56 | | +| Total 1107470941123998166653182-8371-7479-8189-9486-9171-7668-85 | | Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right. @@ -164,21 +164,21 @@ Phase 4: Production annotation. The previously selected 80K pages were annotated Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on DocLayNet test set. The MRCNN (Mask R-CNN) and FRCNN (Faster R-CNN) models with ResNet-50 or ResNet-101 backbone were trained based on the network architectures from the detectron2 model zoo (Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN 3x), with default configurations. The YOLO implementation utilized was YOLOv5x6 [13]. All models were initialised using pre-trained weights from the COCO 2017 dataset. -| | human MRCNN FRCNN YOLO | | -|--------------------------------------|--------------------------|--------------------| -| | | R50 R101 R101 v5x6 | -| Caption 84-8968.471.570.177.7 | | | -| Footnote 83-9170.971.873.777.2 | | | -| Formula 83-8560.163.463.566.2 | | | -| List-item 87-8881.280.881.086.2 | | | -| Page-footer 93-9461.659.358.961.1 | | | -| Page-header 85-8971.970.072.067.9 | | | -| Picture 69-7171.772.772.077.1 | | | -| Section-header 83-8467.669.368.474.6 | | | -| Table 77-8182.282.982.286.3 | | | -| Text 84-8684.685.885.488.1 | | | -| Title 60-7276.780.479.982.7 | | | -| All 82-8372.473.573.476.8 | | | +| | human MRCNN FRCNN YOLO | | +| - | - | - | +| | | R50 R101 R101 v5x6 | +| Caption 84-8968.471.570.177.7 | | | +| Footnote 83-9170.971.873.777.2 | | | +| Formula 83-8560.163.463.566.2 | | | +| List-item 87-8881.280.881.086.2 | | | +| Page-footer 93-9461.659.358.961.1 | | | +| Page-header 85-8971.970.072.067.9 | | | +| Picture 69-7171.772.772.077.1 | | | +| Section-header 83-8467.669.368.474.6 | | | +| Table 77-8182.282.982.286.3 | | | +| Text 84-8684.685.885.488.1 | | | +| Title 60-7276.780.479.982.7 | | | +| All 82-8372.473.573.476.8 | | | to avoid this at any cost in order to have clear , unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture. For the latter , we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity. @@ -200,8 +200,8 @@ In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], F Table 3: Performance of a Mask R-CNN R50 network in mAP@0.5-0.95 scores trained on DocLayNet with different class label sets. The reduced label sets were obtained by either down-mapping or dropping labels. -| Class-count 11654 Caption 68 Text Text Text Footnote 71 Text Text Text Formula 60 Text Text Text List-item 81 Text 82 Text Page-footer 6262 - - Page-header 7268 - - Picture 72727272 Section-header 68676968 Table 82838282 Text 85848484 Title 77 Sec.-h. Sec.-h. Sec.-h. Overall 72737877 | -|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| Class-count 11654 Caption 68 Text Text Text Footnote 71 Text Text Text Formula 60 Text Text Text List-item 81 Text 82 Text Page-footer 6262 - - Page-header 7268 - - Picture 72727272 Section-header 68676968 Table 82838282 Text 85848484 Title 77 Sec.-h. Sec.-h. Sec.-h. Overall 72737877 | +| - | ## Learning Curve @@ -211,10 +211,10 @@ One of the fundamental questions related to any dataset is if it is "large enoug The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption→Text) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However -document-wise and page-wise split for different label sets. Naive page-wise split will result in ~10% point improve +document-wise and page-wise split for different label sets. Naive page-wise split will result in ~10% point improvement. -| Split Doc Page Doc Page Caption 6883 Footnote 7184 Formula 6066 List-item 81888288 Page-footer 6289 Page-header 7290 Picture 72827282 Section-header 68836983 Table 82898290 Text 85918490 Title 7781 All 72847887 | -|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| Class-count 115 Split Doc Page Doc Page Caption 6883 Footnote 7184 Formula 6066 List-item 81888288 Page-footer 6289 Page-header 7290 Picture 72827282 Section-header 68836983 Table 82898290 Text 85918490 Title 7781 All 72847887 | +| - | lists in PubLayNet (grouped list-items) versus DocLayNet (separate list-items), the label set of size 4 is the closest to PubLayNet, in the assumption that the List is down-mapped to Text in PubLayNet. The results in Table 3 show that the prediction accuracy on the remaining class labels does not change significantly when other classes are merged into them. The overall macro-average improves by around 5%, in particular when Page-footer and Page-header are excluded. @@ -228,20 +228,21 @@ Throughout this paper , we claim that DocLayNet's wider variety of document layo KDD'22, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar -Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network across the PubLayNet, DocBank & DocLayNet data-sets. By evaluating on common label classes of each dataset, we observe that the DocLayNet-trained model has much less pronounced variations in performance across - -| | Training on labels PLN DB DLN | | -|-------------------------------|---------------------------------|--------------------| -| PubLayNet (PLN) Figure 964323 | | Sec-header 87 - 32 | -| | | Table 952449 | -| | | Text 96 - 42 | -| | | total 933430 | -| DocBank (DB) Figure 777131 | | Table 196522 | -| | | total 486827 | -| DocLayNet (DLN) Figure 675172 | | Sec-header 53 - 68 | -| | | Table 874382 | -| | | Text 77 - 84 | -| | | total 594778 | +Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network across the PubLayNet, DocBank & DocLayNet data-sets. By evaluating on common label classes of each dataset, we observe that the DocLayNet-trained model has much less pronounced variations in performance across all datasets. + +| | | | Testing on | +| - | - | - | - | +| | Training on labels PLN DB DLN | | | +| PubLayNet (PLN) Figure 964323 | | Sec-header 87 - 32 | | +| | | Table 952449 | | +| | | Text 96 - 42 | | +| | | total 933430 | | +| DocBank (DB) Figure 777131 | | Table 196522 | | +| | | total 486827 | | +| DocLayNet (DLN) Figure 675172 | | Sec-header 53 - 68 | | +| | | Table 874382 | | +| | | Text 77 - 84 | | +| | | total 594778 | | Section-header , Table and Text. Before training, we either mapped or excluded DocLayNet's other labels as specified in table 3, and also PubLayNet's List to Text. Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text. diff --git a/tests/pdf_snapshots/pdf/sources/2305.03393v1-pg9.pdf.md b/tests/pdf_snapshots/pdf/sources/2305.03393v1-pg9.pdf.md index 45a100f9..af730b7a 100644 --- a/tests/pdf_snapshots/pdf/sources/2305.03393v1-pg9.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/2305.03393v1-pg9.pdf.md @@ -6,18 +6,18 @@ We have chosen the PubTabNet data set to perform HPO, since it includes a highly Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart. -| | dec-layers Language TEDs mAP | | | (0.75) Inference | -|----------------------------------|--------------------------------|-------------------------------|--------------------------------|--------------------| -| enc-layers # # | | | | | -| | | | time (secs) simple complex all | | -| 66 OTSL 0.9650.9340.9550.882.73 | | | | | -| | | HTML 0.9690.9270.9550.8575.39 | | | -| 44 OTSL 0.9380.9040.9270.8531.97 | | | | | -| | | HTML 0.9520.9090.9380.8433.77 | | | -| 24 OTSL 0.9230.8970.9150.8591.91 | | | | | -| | | HTML 0.9450.9010.9310.8343.81 | | | -| 42 OTSL 0.9520.920.9420.8571.22 | | | | | -| | | HTML 0.9440.9030.9310.8242 | | | +| | dec-layers Language TEDs mAP | | | (0.75) Inference | +| - | - | - | - | - | +| enc-layers # # | | | | | +| | | | time (secs) simple complex all | | +| 66 OTSL 0.9650.9340.9550.882.73 | | | | | +| | | HTML 0.9690.9270.9550.8575.39 | | | +| 44 OTSL 0.9380.9040.9270.8531.97 | | | | | +| | | HTML 0.9520.9090.9380.8433.77 | | | +| 24 OTSL 0.9230.8970.9150.8591.91 | | | | | +| | | HTML 0.9450.9010.9310.8343.81 | | | +| 42 OTSL 0.9520.920.9420.8571.22 | | | | | +| | | HTML 0.9440.9030.9310.8242 | | | ## 5.2 Quantitative Results diff --git a/tests/pdf_snapshots/pdf/sources/2305.03393v1.pdf.md b/tests/pdf_snapshots/pdf/sources/2305.03393v1.pdf.md index 7a95dabd..92ccf3af 100644 --- a/tests/pdf_snapshots/pdf/sources/2305.03393v1.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/2305.03393v1.pdf.md @@ -76,7 +76,7 @@ The OTSL vocabulary is comprised of the following tokens: A notable attribute of OTSL is that it has the capability of achieving lossless conversion to HTML. -Fig. 3. OTSL description of table structure: A - table example; B - graphical representation of table structure; C - mapping structure on a grid; D - OTSL structure +Fig. 3. OTSL description of table structure: A - table example; B - graphical representation of table structure; C - mapping structure on a grid; D - OTSL structure encoding; E - explanation on cell encoding @@ -122,18 +122,18 @@ We have chosen the PubTabNet data set to perform HPO, since it includes a highly Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart. -| | dec-layers Language TEDs mAP | | | (0.75) Inference | -|----------------------------------|--------------------------------|-------------------------------|--------------------------------|--------------------| -| enc-layers # # | | | | | -| | | | time (secs) simple complex all | | -| 66 OTSL 0.9650.9340.9550.882.73 | | | | | -| | | HTML 0.9690.9270.9550.8575.39 | | | -| 44 OTSL 0.9380.9040.9270.8531.97 | | | | | -| | | HTML 0.9520.9090.9380.8433.77 | | | -| 24 OTSL 0.9230.8970.9150.8591.91 | | | | | -| | | HTML 0.9450.9010.9310.8343.81 | | | -| 42 OTSL 0.9520.920.9420.8571.22 | | | | | -| | | HTML 0.9440.9030.9310.8242 | | | +| | dec-layers Language TEDs mAP | | | (0.75) Inference | +| - | - | - | - | - | +| enc-layers # # | | | | | +| | | | time (secs) simple complex all | | +| 66 OTSL 0.9650.9340.9550.882.73 | | | | | +| | | HTML 0.9690.9270.9550.8575.39 | | | +| 44 OTSL 0.9380.9040.9270.8531.97 | | | | | +| | | HTML 0.9520.9090.9380.8433.77 | | | +| 24 OTSL 0.9230.8970.9150.8591.91 | | | | | +| | | HTML 0.9450.9010.9310.8343.81 | | | +| 42 OTSL 0.9520.920.9420.8571.22 | | | | | +| | | HTML 0.9440.9030.9310.8242 | | | ## 5.2 Quantitative Results @@ -143,15 +143,15 @@ Additionally, the results show that OTSL has an advantage over HTML when applied Table 2. TSR and cell detection results compared between OTSL and HTML on the PubTabNet [22], FinTabNet [21] and PubTables-1M [14] data sets using TableFormer [9] (with enc=6, dec=6, heads=8). -| Data set Language TEDs mAP(0.75) Inference | | | -|----------------------------------------------|-------------------------------|--------------------------------| -| | | time (secs) simple complex all | -| PubTabNet OTSL 0.9650.9340.9550.882.73 | | | -| | HTML 0.9690.9270.9550.8575.39 | | -| FinTabNet OTSL 0.9550.9610.9590.8621.85 | | | -| | HTML 0.9170.9220.920.7223.26 | | -| PubTables-1M OTSL 0.9870.9640.9770.8961.79 | | | -| | HTML 0.9830.9440.9660.8893.26 | | +| Data set Language TEDs mAP(0.75) Inference | | | +| - | - | - | +| | | time (secs) simple complex all | +| PubTabNet OTSL 0.9650.9340.9550.882.73 | | | +| | HTML 0.9690.9270.9550.8575.39 | | +| FinTabNet OTSL 0.9550.9610.9590.8621.85 | | | +| | HTML 0.9170.9220.920.7223.26 | | +| PubTables-1M OTSL 0.9870.9640.9770.8961.79 | | | +| | HTML 0.9830.9440.9660.8893.26 | | ## 5.3 Qualitative Results diff --git a/tests/pdf_snapshots/pdf/sources/code_and_formula.pdf.md b/tests/pdf_snapshots/pdf/sources/code_and_formula.pdf.md index 1c941faf..ebb09fab 100644 --- a/tests/pdf_snapshots/pdf/sources/code_and_formula.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/code_and_formula.pdf.md @@ -4,9 +4,11 @@ Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, -Listing 1: Simple JavaScript Program +``` +function add(a, b) { return a + b; } console.log(add(3, 5)); +``` -f un c t i o n add (a, b) { r e t urn a + b; } c o n s o l e.l og (add (3, 5)); +Listing 1: Simple JavaScript Program Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod tempor invidunt ut labore et dolore magna aliquyam erat, sed diam voluptua. At vero eos et accusam et justo duo dolores et ea rebum. Stet clita kasd gubergren, no sea takimata sanctus est Lorem ipsum dolor sit amet. @@ -24,4 +26,4 @@ Lorem ipsum dolor sit amet, consetetur sadipscing elitr, sed diam nonumy eirmod Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat. -Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat +Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat. diff --git a/tests/pdf_snapshots/pdf/sources/normal_4pages.pdf.md b/tests/pdf_snapshots/pdf/sources/normal_4pages.pdf.md index e9919264..35bf2746 100644 --- a/tests/pdf_snapshots/pdf/sources/normal_4pages.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/normal_4pages.pdf.md @@ -50,17 +50,17 @@ [표 1] 감염병예방법 제2조제2호 개정전·후 비교 -| 구분 개정전 개정후 | | | -|---------------------------------------|--------------------------------------------------|------------------------| -| 분류 1군 감염병 1급 감염병 | | | -| 분류 기준 ◦ 마시는 물 또는 식품을 매개로 | 큰 감염병 ◦ 생물테러감염병 또는 치명률이 발생하고 집단 발생의 우려가 | 높거나 집단 발생의 우려가 커 높 | -| | | 은 수준의 격리가 필요한 감염병 | -| 질병 ◦ (6종) 콜레라, 장티푸스, 파라 대상 | 성대장균감염증, A형간염 ◦ (17종) 에볼라, 페스트 등 티푸스, 세균성이질, 장출혈 | * 좌측 6종(2급 감염병 분류)은 | -| | | 미포함 | -| U코드 ◦ 대상질병에 U코드 없음 ◦ 대상질병에 U코드 일부(3종) | | 포함 | -| | | ① 신종감염병증후군 → 코로나19(U) | -| | | ② 중증급성호흡기증후군(SARS) (U) | -| | | ③ 중동호흡기증후군(MERS)(U) | +| 구분 개정전 개정후 | | | +| - | - | - | +| 분류 1군 감염병 1급 감염병 | | | +| 분류 기준 ◦ 마시는 물 또는 식품을 매개로 | 큰 감염병 ◦ 생물테러감염병 또는 치명률이 발생하고 집단 발생의 우려가 | 높거나 집단 발생의 우려가 커 높 | +| | | 은 수준의 격리가 필요한 감염병 | +| 질병 ◦ (6종) 콜레라, 장티푸스, 파라 대상 | 성대장균감염증, A형간염 ◦ (17종) 에볼라, 페스트 등 티푸스, 세균성이질, 장출혈 | * 좌측 6종(2급 감염병 분류)은 | +| | | 미포함 | +| U코드 ◦ 대상질병에 U코드 없음 ◦ 대상질병에 U코드 일부(3종) | | 포함 | +| | | ① 신종감염병증후군 → 코로나19(U) | +| | | ② 중증급성호흡기증후군(SARS) (U) | +| | | ③ 중동호흡기증후군(MERS)(U) | ※ 주: 2020.1.1. 기준 diff --git a/tests/pdf_snapshots/pdf/sources/redp5110_sampled.pdf.md b/tests/pdf_snapshots/pdf/sources/redp5110_sampled.pdf.md index 1f4e7d6d..b046ef9c 100644 --- a/tests/pdf_snapshots/pdf/sources/redp5110_sampled.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/redp5110_sampled.pdf.md @@ -8,9 +8,9 @@ Protect columns by defining column masks -## Row and Column Access Control +## Row and Column Access Control Support in IBM DB2 for i -Hernando Bedoya Rob Bestgen Mike Cain Dan Cruikshank Jim Denton Doug Mack Tom McKinley +Jim Bainbridge Hernando Bedoya Rob Bestgen Mike Cain Dan Cruikshank Jim Denton Doug Mack Tom McKinley @@ -52,7 +52,9 @@ This IBM® Redpaper™ publication provides information about the IBM i 7.2 feat This paper is intended for database engineers, data-centric application developers, and secu rity officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed. -the International Technical Support Organization (ITSO), Rochester, Minnesota US. +## Authors + +This paper was produced by the IBM DB2 for i Center of Excellence team in partnership with the International Technical Support Organization (ITSO), Rochester, Minnesota US. @@ -127,29 +129,29 @@ The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 Table 2- 1 FUNCTION _USAGE view -| FUNCTION_ID VARCHAR(30) ID of the function. | | -|----------------------------------------------------------------------------------|--------------------------------------------------------------| -| USER_NAME VARCHAR(10) Name of the user profile that has a usage setting for this | | -| | function. | -| USAGE VARCHAR(7) Usage setting: | | -| | ALLOWED: The user profile is allowed to use the function. | -| | DENIED: The user profile is not allowed to use the function. | -| USER_TYPE VARCHAR(5) Type of user profile: | | -| | USER: The user profile is a user. | -| | GROUP: The user profile is a group. | +| FUNCTION_ID VARCHAR(30) ID of the function. | | +| - | - | +| USER_NAME VARCHAR(10) Name of the user profile that has a usage setting for this | | +| | function. | +| USAGE VARCHAR(7) Usage setting: | | +| | ALLOWED: The user profile is allowed to use the function. | +| | DENIED: The user profile is not allowed to use the function. | +| USER_TYPE VARCHAR(5) Type of user profile: | | +| | USER: The user profile is a user. | +| | GROUP: The user profile is a group. | To discover who has authorization to define and manage RCAC, you can use the query that is shown in Example 2-1. Example 2-1 Query to determine who has authority to define and manage RCAC -| SELECT functi on_i d, | | -|--------------------------------------|---------------| -| | u s er_n ame, | -| | u s age, | -| | u s er_type | -| FROM functi on_usage | | -| WHERE functi on_i d=’QIBM_DB_SECADM’ | | -| ORDER BY user_name; | | +| SELECT functi on_i d, | | +| - | - | +| | u s er_n ame, | +| | u s age, | +| | u s er_type | +| FROM functi on_usage | | +| WHERE functi on_i d=’QIBM_DB_SECADM’ | | +| ORDER BY user_name; | | ## 2.2 Separation of duties @@ -169,8 +171,21 @@ Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL aut Table 2-2 Comparison of the different function usage IDs and * JOBCTL authority -| User action *JOBCTL QIBM_DB_SECADM QIBM_DB_SQLADM QIBM_DB_SYSMON No Authority SET CURRENT DEGREE (SQL statement) X X STRDBMON or ENDDBMON commands targeting a different user's job X X STRDBMON or ENDDBMON commands targeting a job that matches the current user X X X X CHGQRYA command targeting a different user's job X X Visual Explain within Run SQL scripts X X X X Visual Explain outside of Run SQL scripts X X ANALYZE PLAN CACHE procedure X X QUSRJOBI() API format 900 or System i Navigator's SQL Details for Job X X X CHANGE PLAN CACHE SIZE procedure (currently does not check authority) X X MODIFY PLAN CACHE procedure X X MODIFY PLAN CACHE PROPERTIES procedure (currently does not check authority) X X DUMP PLAN CACHE procedure X X | -|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| User action | | QIBM_DB_SECADM | QIBM_DB_SQLADM | QIBM_DB_SYSMON | | +| - | - | - | - | - | - | +| | *JOBCTL | | | | No Authority | +| SET CURRENT DEGREE (SQL statement) X X | | | | | | +| CHGQRYA command targeting a different user's job X X | | | | | | +| STRDBMON or ENDDBMON commands targeting a different user's job X X | | | | | | +| STRDBMON or ENDDBMON commands targeting a job that matches the current user X X X X | | | | | | +| QUSRJOBI() API format 900 or System i Navigator's SQL Details for Job X X X | | | | | | +| Visual Explain within Run SQL scripts X X X X | | | | | | +| Visual Explain outside of Run SQL scripts X X | | | | | | +| ANALYZE PLAN CACHE procedure X X | | | | | | +| DUMP PLAN CACHE procedure X X | | | | | | +| MODIFY PLAN CACHE procedure X X | | | | | | +| MODIFY PLAN CACHE PROPERTIES procedure (currently does not check authority) X X | | | | | | +| CHANGE PLAN CACHE SIZE procedure (currently does not check authority) X X | | | | | | initially enable or disable the row access rules. @@ -184,11 +199,11 @@ A column mask is a database object that manifests a column value access control Table 3-1 Special registers and their corresponding values -| SESSION_USER The effective user of the thread excluding adopted authority. USER or | | -|--------------------------------------------------------------------------------------------|--------------------------------------------------------| -| CURRENT_USER The effective user of the thread including adopted authority. When no adopted | | -| | authority is present, this has the same value as USER. | -| SYSTEM_USER The authorization ID that initiated the connection. | | +| SESSION_USER The effective user of the thread excluding adopted authority. USER or | | +| - | - | +| CURRENT_USER The effective user of the thread including adopted authority. When no adopted | | +| | authority is present, this has the same value as USER. | +| SYSTEM_USER The authorization ID that initiated the connection. | | Figure 3-5 shows the difference in the special register values when an adopted authority is used: @@ -210,8 +225,8 @@ IBM DB2 for i supports nine different built-in global variables that are read on Table 3-2 Built-in global variables -| CLIENT_HOST VARCHAR(255) Host name of the current client as returned by the system CLIENT_IPADDR VARCHAR(128) IP address of the current client as returned by the system CLIENT_PORT INTEGER Port used by the current client to communicate with the server PACKAGE_NAME VARCHAR(128) Name of the currently running package PACKAGE_SCHEMA VARCHAR(128) Schema name of the currently running package PACKAGE_VERSION VARCHAR(64) Version identifier of the currently running package ROUTINE_SCHEMA VARCHAR(128) Schema name of the currently running routine ROUTINE_SPECIFIC_NAME VARCHAR(128) Name of the currently running routine ROUTINE_TYPE CHAR(1) Type of the currently running routine | -|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| CLIENT_HOST VARCHAR(255) Host name of the current client as returned by the system CLIENT_IPADDR VARCHAR(128) IP address of the current client as returned by the system CLIENT_PORT INTEGER Port used by the current client to communicate with the server PACKAGE_NAME VARCHAR(128) Name of the currently running package PACKAGE_SCHEMA VARCHAR(128) Schema name of the currently running package PACKAGE_VERSION VARCHAR(64) Version identifier of the currently running package ROUTINE_SCHEMA VARCHAR(128) Schema name of the currently running routine ROUTINE_SPECIFIC_NAME VARCHAR(128) Name of the currently running routine ROUTINE_TYPE CHAR(1) Type of the currently running routine | +| - | ## 3.3 VERIFY_GROUP_FOR_USER function @@ -231,7 +246,9 @@ The following function invocation returns a value of 0: The following function invocation returns a value of 0: -CASE WHEN VERI FY_GROUP_FOR_USER (SESSION_USER,' HR',' EMP') = 1 TH EN EMPLOYEES. DATE_OF_BI RTH WHEN VERI FY_GROUP_FOR_USER (SESSION_USER,' MGR') = 1 AND SESS ION_USER = EMPLOYEES. USER_I D TH EN EMPLOYEES. DATE_OF_BI RTH WHEN VERI FY_GROUP_FOR_USER (SESSION_USER,' MGR') = 1 AND SESS ION_USER <> EMPLOYEES. USER_I D TH EN (9999 | |' -' | | MONTH (EMPLOYEES. DATE_OF_BIRTH) | |' -' | | DAY (EMPLOYEES. DATE_OF_BIRTH)) END ELSE NULL ENABLE; +``` +CASE WHEN VERIFY_GROUP_FOR_USER( SESSION_USER, 'HR', 'EMP') = 1 THEN EMPLOYEES. DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER( SESSION_USER, 'MGR') = 1 AND SESSION_USER = EMPLOYEES. USER_ID THEN EMPLOYEES. DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER( SESSION_USER, 'MGR') = 1 AND SESSION_USER <> EMPLOYEES. USER_ID THEN( 9999 || '-' || MONTH( EMPLOYEES. DATE_OF_BIRTH) || '-' || DAY(EMPLOYEES.DATE_OF_BIRTH)) ELSE NULL END ENABLE; +``` - 2. The other column to mask in this example is the TAX_ID information. In this example, the rules to enforce include the following ones: - Human Resources can see the unmasked TAX_ID of the employees. @@ -241,9 +258,11 @@ CASE WHEN VERI FY_GROUP_FOR_USER (SESSION_USER,' HR',' EMP') = 1 TH EN EMPLOYEES To implement this column mask, run the SQL statement that is shown in Example 3-9. -Example 3-9 Creating a mask on the TAX _ID column +``` +CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES FOR COLUMN TAX_ID RETURN CASE WHEN VERIFY_GROUP_FOR_USER( SESSION_USER, 'HR') = 1 THEN EMPLOYEES. TAX_ID WHEN VERIFY_GROUP_FOR_USER( SESSION_USER, 'MGR') = 1 AND SESSION_USER = EMPLOYEES. USER_ID THEN EMPLOYEES. TAX_ID WHEN VERIFY_GROUP_FOR_USER( SESSION_USER, 'MGR') = 1 AND SESSION_USER <> EMPLOYEES. USER_ID THEN( 'XXX-XX-' CONCAT QSYS2. SUBSTR( EMPLOYEES. TAX_ID, 8, 4)) WHEN VERIFY_GROUP_FOR_USER( SESSION_USER, 'EMP') = 1 THEN EMPLOYEES. TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE; +``` -CREATE MASK HR_SCHEMA. MASK_TAX_I D_ON_EMPLOYEES ON HR_SCHEMA. EMPLOYEES AS EMPLOYEES FOR COLUMN TAX_I D CASE WHEN VERI FY_GROUP_FOR_USER (SESSION_USER,' HR') = 1 RETURN TH EN EMPLOYEES. TAX_I D WHEN VERI FY_GROUP_FOR_USER (SESSION_USER,' MGR') = 1 AND SESS ION_USER = EMPLOYEES. USER_I D TH EN EMPLOYEES. TAX_I D AND SESS ION_USER <> EMPLOYEES. USER_I D WHEN VERI FY_GROUP_FOR_USER (SESSION_USER,' MGR') = 1 TH EN (' XXX-XX-' CONCAT QSYS2. SUBSTR (EMPLOYEES. TAX_ID, 8, 4)) WHEN VERI FY_GROUP_FOR_USER (SESSION_USER,' EMP') = 1 TH EN EMPLOYEES. TAX_I D END ELSE' XXX-XX-XXXX' ENABLE; +Example 3-9 Creating a mask on the TAX _ID column Figure 3-10 Column masks shown in System i Navigator @@ -257,7 +276,9 @@ Now that you have created the row permission and the two column masks, RCAC must Example 3-10 Activating RCAC on the EMPLOYEES table -/* Acti ve Row Access Control (permi ssi ons) */ /* Acti ve Col umn Access Control (masks) */ ALTER TABLE HR_SCHEMA. EMPLOYEES ACT I VATE ROW ACCESS CONTROL ACT I VATE COLUMN ACCESS CONTROL; +``` +/* Active Row Access Control(permissions) */ /* Active Column Access Control(masks) */ ALTER TABLE HR_SCHEMA.EMPLOYEES ACTIVATE ROW ACCESS CONTROL ACTIVATE COLUMN ACCESS CONTROL; +``` - 2. Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from the main navigation pane of System i Navigator, click Schemas  HR_SCHEMA  Tables, right-click the EMPLOYEES table, and click Definition. @@ -277,7 +298,9 @@ Figure 4-69 Index advice with no RCAC -WHEN QSYS2. VERI FY_GROUP_FOR_USER (SESSION_USER,' TELLER') = 1 WHEN QSYS2. VERI FY_GROUP_FOR_USER (SESSION_USER,' CUSTOMER') = 1 WHEN QSYS2. VERI FY_GROUP_FOR_USER (SESSION_USER,' ADMIN') = 1 WHEN QSYS2. VERI FY_GROUP_FOR_USER (SESSION_USER,' TELLER') = 1 WHEN QSYS2. VERI FY_GROUP_FOR_USER (SESSION_USER,' CUSTOMER') = 1 124 Row and Column Access Control Support in IBM DB2 for iTHEN C. CUSTOMER_TAX_ID TH EN (' XXX-XX-' CONCAT QSYS2. SUBSTR (C. CUSTOMER_TAX_ID, 8, 4)) TH EN C. CUSTOMER_TAX_I D TH EN C. CUSTOMER_DRI VERS_LI CENSE_NUMBER TH EN C. CUSTOMER_DRI VERS_LI CENSE_NUMBER TH EN C. CUSTOMER_DRI VERS_LI CENSE_NUMBER CREATE MASK BANK_SCHEMA. MASK_DRI VERS_LI CENSE_ON_CUSTOMERS ON BANK_SCHEMA. CUSTOMERS AS C CREATE MASK BANK_SCHEMA. MASK_LOG I N_I D_ON_CUSTOMERS ON BANK_SCHEMA. CUSTOMERS AS C ELSE' XXX-XX-XXXX' END ENABLE; ELSE' *************' END ENABLE; RETURN CASE RETURN CASE FOR COLUMN CUSTOMER_DRIVERS_LI CENSE_NUMBER FOR COLUMN CUSTOMER_LOG I N_I D ALTER TABLE BANK_SCHEMA. CUSTOMERS WHEN QSYS2. VERI FY_GROUP_FOR_USER (SESSION_USER,' ADMIN') = 1 WHEN QSYS2. VERI FY_GROUP_FOR_USER (SESSION_USER,' CUSTOMER') = 1 WHEN QSYS2. VERI FY_GROUP_FOR_USER (SESSION_USER,' ADMIN') = 1 WHEN QSYS2. VERI FY_GROUP_FOR_USER (SESSION_USER,' CUSTOMER') = 1 WHEN QSYS2. VERI FY_GROUP_FOR_USER (SESSION_USER,' ADMIN') = 1 WHEN QSYS2. VERI FY_GROUP_FOR_USER (SESSION_USER,' CUSTOMER') = 1 TH EN C. CUSTOMER_LOG I N_I D TH EN C. CUSTOMER_LOG I N_I D TH EN C. CUSTOMER_SECURITY_QUESTION TH EN C. CUSTOMER_SECURITY_QUESTION TH EN C. CUSTOMER_SECURITY_QUESTION_ANSWER TH EN C. CUSTOMER_SECURITY_QUESTION_ANSWER CREATE MASK BANK_SCHEMA. MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA. CUSTOMERS AS C CREATE MASK BANK_SCHEMA. MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA. CUSTOMERS AS C ELSE' *****' END ENABLE; ELSE' *****' END ENABLE; ELSE' *****' END ENABLE; ACTI VATE ROW ACCESS CONTROL ACTI VATE COLUMN ACCESS CONTROL; RETURN CASE RETURN CASE FOR COLUMN CUSTOMER_SECURITY_QUESTION FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER +``` +WHEN QSYS2. VERIFY_GROUP_FOR_USER( SESSION_USER, 'TELLER') = 1 WHEN QSYS2. VERIFY_GROUP_FOR_USER( SESSION_USER, 'CUSTOMER') = 1 WHEN QSYS2. VERIFY_GROUP_FOR_USER( SESSION_USER, 'ADMIN') = 1 WHEN QSYS2. VERIFY_GROUP_FOR_USER( SESSION_USER, 'TELLER') = 1 WHEN QSYS2. VERIFY_GROUP_FOR_USER( SESSION_USER, 'CUSTOMER') = 1 124 Row and Column Access Control Support in IBM DB2 for iTHEN C. CUSTOMER_TAX_ID THEN( 'XXX-XX-' CONCAT QSYS2. SUBSTR( C. CUSTOMER_TAX_ID, 8, 4)) THEN C. CUSTOMER_TAX_ID THEN C. CUSTOMER_DRIVERS_LICENSE_NUMBER THEN C. CUSTOMER_DRIVERS_LICENSE_NUMBER THEN C. CUSTOMER_DRIVERS_LICENSE_NUMBER CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ELSE 'XXX-XX-XXXX' END ELSE '*************' END RETURN CASE RETURN CASE ENABLE; FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER ENABLE; FOR COLUMN CUSTOMER_LOGIN_ID ALTER TABLE BANK_SCHEMA.CUSTOMERS WHEN QSYS2. VERIFY_GROUP_FOR_USER( SESSION_USER, 'ADMIN') = 1 WHEN QSYS2. VERIFY_GROUP_FOR_USER( SESSION_USER, 'CUSTOMER') = 1 WHEN QSYS2. VERIFY_GROUP_FOR_USER( SESSION_USER, 'ADMIN') = 1 WHEN QSYS2. VERIFY_GROUP_FOR_USER( SESSION_USER, 'CUSTOMER') = 1 WHEN QSYS2. VERIFY_GROUP_FOR_USER( SESSION_USER, 'ADMIN') = 1 WHEN QSYS2. VERIFY_GROUP_FOR_USER( SESSION_USER, 'CUSTOMER') = 1 THEN C. CUSTOMER_LOGIN_ID THEN C. CUSTOMER_LOGIN_ID THEN C. CUSTOMER_SECURITY_QUESTION THEN C. CUSTOMER_SECURITY_QUESTION THEN C. CUSTOMER_SECURITY_QUESTION_ANSWER THEN C. CUSTOMER_SECURITY_QUESTION_ANSWER CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ELSE '*****' END ELSE '*****' END ELSE '*****' END ACTIVATE ROW ACCESS CONTROL ACTIVATE COLUMN ACCESS CONTROL; RETURN CASE RETURN CASE ENABLE; FOR COLUMN CUSTOMER_SECURITY_QUESTION ENABLE; FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER ENABLE; +``` ## Support in IBM DB2 for i diff --git a/tests/pdf_snapshots/pdf/sources/right_to_left_01.pdf.md b/tests/pdf_snapshots/pdf/sources/right_to_left_01.pdf.md index 9618674c..1db460a0 100644 --- a/tests/pdf_snapshots/pdf/sources/right_to_left_01.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/right_to_left_01.pdf.md @@ -1 +1,3 @@ -ى المحللين والعلماء إجراء تحليالت معقدة بطريقة سريعة وفعالة ي إيجاد حلول فعالة للمشكالت م هذه اللغات يمكن أن يسهم بشكل كبير ف . يمتلك كل من R و Pythonميزات فريدة تجعلها مثالية لتحليل البيانات، مما يسهل عل ي يمكن أن تعزز اإلنتاجية وتساعد ف . إذا كان لديك عقلية تحليلية، فإن استخدا ج العمل. عندما يجتم ي تحسين نتائ ى اتخاذ قرارات أكثر دقة بنا ً ج األنماط والتوجهات منها ع مهارات البرمجة، يصبح من الممكن معالجة كميات هائلة من البيانات واستخرا م R و Pythonلتنفيذ عمليات تحليلية متقدمة، مثل النمذجة اإلحصائية وتحليل البيانات الكبيرة ضا إل ي أي ً . هذا ليس فقط يوفر الوقت، بل يمكن أن يؤد . يمكن للمبرمجين استخدا ي م ع التفكير التحليل م مجموعة واسعة من التطبيقات، من التحليل البيان ى ذلك، توفر كل من R و Pythonمكتبات وأدوات غنية تدع . عالوة عل ى البيانات ى استنتاجات قائمة عل ء عل ى سبيل المثال، يمكن استخدا . عل م البيان ي Python إلدارة البيانات بكفاءة، بينما توفر R أدوات قوية للرس ي. يمكن للمستخدمين االستفادة من هذه المكتبات لتطوير حلول مبتكرة للمشكالت المختلفة ي، مما يجعلها مثالية للباحثين والمحللين. ف pandas ف م مكتبة ي والتحليل اإلحصائ م اآلل ى التعل ي إل ى تحليل البيانات بشكل فعال وتطبيق األساليب البرمجية المناسبة يمكن أن تكون لها تأثيرات إيجابية بعيدة المدى عل ى تحسين اإلنتاجية وتوفير حلول مبتكرة للمشكالت المعقدة ع عقلية تحليلية إل ي البرمجة بلغة R و Pythonم ي النهاية، يمكن أن تؤد ي والمهن ى األداء الشخص . إن القدرة عل +## تحسين اإلنتاجية وحل المشكالت من خالل البرمجة بلغة R وPython + +ي إيجاد حلول فعالة للمشكالت ي يمكن أن تعزز اإلنتاجية وتساعد ف تعتبر البرمجة بلغة R و Pythonمن األدوات القوية الت ى المحللين والعلماء م هذه اللغات يمكن أن يسهم بشكل كبير ف . يمتلك كل من R و Pythonميزات فريدة تجعلها مثالية لتحليل البيانات، مما يسهل عل . إذا كان لديك عقلية تحليلية، فإن استخدا إجراء تحليالت معقدة بطريقة سريعة وفعالة ج العمل. ي تحسين نتائ ج األنماط والتوجهات منها ع مهارات البرمجة، يصبح من الممكن معالجة كميات هائلة من البيانات واستخرا م R و Pythonلتنفيذ عمليات تحليلية متقدمة، مثل النمذجة اإلحصائية وتحليل البيانات الكبيرة . يمكن للمبرمجين استخدا ي م ع التفكير التحليل عندما يجتم ى اتخاذ قرارات أكثر دقة ضا إل ي أي ً . هذا ليس فقط يوفر الوقت، بل يمكن أن يؤد ى البيانات ى استنتاجات قائمة عل ء عل بنا ً م مجموعة واسعة من التطبيقات، من التحليل البيان ى ذلك، توفر كل من R و Pythonمكتبات وأدوات غنية تدع . عالوة عل ى . عل ي Python إلدارة البيانات بكفاءة، بينما توفر R أدوات قوية للرس ي. يمكن للمستخدمين االستفادة من هذه المكتبات لتطوير حلول مبتكرة للمشكالت المختلفة م اآلل ى التعل ي إل م البيان ي، مما يجعلها مثالية للباحثين والمحللين. pandas ف م مكتبة سبيل المثال، يمكن استخدا ي والتحليل اإلحصائ ى تحسين اإلنتاجية وتوفير حلول مبتكرة للمشكالت المعقدة ع عقلية تحليلية إل ي البرمجة بلغة R و Pythonم ي النهاية، يمكن أن تؤد ف ى تحليل البيانات بشكل فعال وتطبيق األساليب البرمجية المناسبة يمكن أن تكون لها تأثيرات إيجابية بعيدة المدى عل ي والمهن ى األداء الشخص . إن القدرة عل diff --git a/tests/pdf_snapshots/pdf/sources/right_to_left_02.pdf.md b/tests/pdf_snapshots/pdf/sources/right_to_left_02.pdf.md index 274cc405..a3168069 100644 --- a/tests/pdf_snapshots/pdf/sources/right_to_left_02.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/right_to_left_02.pdf.md @@ -1,9 +1,9 @@ -ا مجوخحت الصووحة واللعاوويل، العمول ياووى تح يوق معوودحت نمووو قويوووة ومسووولدامة و وووخماة فووو ا عذاوووف ال لخيوووخت، و ووو ا الح وووخ ياوووى محوددات األمون ال ووم ا ضووء اللحوديخت اإلقايميوة والدوليوة، ومواصواة واوود تلووير الماوخر ة السيخسوية، واسولمرار ملخبعوة ما وخت األمووون واحسووول رار ومكخفحوووة اإلرهوووخ ، تلووووير ما وووخت ال خفوووة والووووي ا المصوري فو ا المعلووودل ياوووى الهحوووو الووو ي يرسووو م وووخهيل الموا،هة والسام المجلمع ا الوووو،ه ا، والبلوووخ الوووديه +تكايف السيد رئيس الجماورية لاخ بخلعمل ياى تح يق يدد من األهودا ياى رعساخ: وضع ماف بهخء اإلنسخن المصري ياى رعس قخئموة األولويوخت، ا مجوخحت الصووحة واللعاوويل، العمول ياووى تح يوق معوودحت نمووو لخصوة فوو ا عذاوووف ال لخيوووخت، و ووو ا الح وووخ ياوووى قويوووة ومسووولدامة و وووخماة فووو ا ضووء اللحوديخت اإلقايميوة والدوليوة، ا المصوري فو محوددات األمون ال ووم ومواصواة واوود تلووير الماوخر ة السيخسوية، واسولمرار ملخبعوة ما وخت ا األمووون واحسووول رار ومكخفحوووة اإلرهوووخ ، تلووووير ما وووخت ال خفوووة والووووي ا المعلووودل ياوووى الهحوووو الووو ي يرسووو م وووخهيل ا، والبلوووخ الوووديه الوووو،ه الموا،هة والسام المجلمع ا. -خ لمخ سبق، يسولاد برنوخما الحكوموة المصورية لوال ال لور)2024 - 2026(تح يق عربعة عهدا اسلراتيجية رئيسة، وه ا ياى الهحو اآلت +خ لمخ سبق، يسولاد برنوخما الحكوموة المصورية لوال ال لور)2024 - ووف ً ا: ا ياى الهحو اآلت 2026(تح يق عربعة عهدا اسلراتيجية رئيسة، وه -ا، ومسولادفخت الوو ارات، والبرنوخما الوو،ه +تجدر اإل خر إلى عنه قد تل تحديد مسولادفخت البرنوخما بخحسولهخد باوكل رئووويس ياوووى مسووولادفخت ر يوووة مصووور ،2023 وتوصووويخت واسوووخت الحووووار ا ليصوا خت الايكايوة، ا، ومسولادفخت الوو ارات، والبرنوخما الوو،ه الوو،ه diff --git a/tests/pdf_snapshots/pdf/sources/right_to_left_03.pdf.md b/tests/pdf_snapshots/pdf/sources/right_to_left_03.pdf.md index b048fa73..b99d19c0 100644 --- a/tests/pdf_snapshots/pdf/sources/right_to_left_03.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/right_to_left_03.pdf.md @@ -1,18 +1,23 @@ -## ی - کاالی داخل +## ی - کاالی داخل ی -| | | 1403/ 09/ 19 تاريخ ارائه مدارک | | | | | | -|-----------------------------------------|-------------------------------------------------------------------------|----------------------------------------------|----------------|----|----------|--------|--------------------| -| | | 1403/ 10 / 04 تاريخ پذيرش شماره جلسه کميته ع | | | | | | -| | | رضه 436 | | | | | | -| | | 1403/ 10 / 05 تاريخ درج اميدنامه | | | | | | -| | ون بورس مشاور پذيرش نحوة تعيين قيمت پايه پس از پذيرش کاال در بورسبر اسا | | رم ری آ رگزا | کا | | | | -| | ی حداقل درصد ع يمت های جهان | | س ق | | | | | -| | | | یحداقل%50 از ت | | | | | -| يانه يا 47.500 تن خطای مجاز تحويل 5% آخ | | يد سال | ول | | روش داخل | روش/ ف | رضه از توليد/ کل ف | -| | ويل رين محموله قابل تح | | | | | | | +| | | 1403/ 09/ 19 تاريخ ارائه مدارک | | | | | | | | | +| - | - | - | - | - | - | - | - | - | - | - | +| | | 1403/ 10 / 04 تاريخ پذيرش | | | | | | | | | +| | | رضه 436 | | | | | | شماره جلسه کميته ع | | | +| | | 1403/ 10 / 05 تاريخ درج اميدنامه | | | | | | | | | +| | ون بورس مشاور پذيرش | | رم ری آ | رگزا کا | | | | | | | +| | | | | بورسبر اسا | | | | | | | +| | يمت های جهان | | | س ق | نحوة تعيين قيمت پايه پس از پذيرش کاال در | | | | | | +| | ی | | | | | | | | | | +| | | | | یحداقل%50 از ت | | | | | | | +| يانه يا 47.500 تن | | يد سال | | ول | روش | روش/ ف | رضه از توليد/ کل ف | | حداقل درصد ع | | +| | | | | | | | | | | داخل | +| | ويل رين محموله قابل تح | | | خطای مجاز تحويل 5% آخ | | | | | | | ## ش + +## -3 پذيرش در بورس diff --git a/tests/pdf_snapshots/pdf/sources/skipped_1page.pdf.md b/tests/pdf_snapshots/pdf/sources/skipped_1page.pdf.md index 1c891afa..8c15c95f 100644 --- a/tests/pdf_snapshots/pdf/sources/skipped_1page.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/skipped_1page.pdf.md @@ -22,6 +22,8 @@ +(DATA) +  @@ -44,6 +46,8 @@ +데이터 + 웨어러블 컨트롤 인터페이스 diff --git a/tests/pdf_snapshots/pdf/sources/skipped_2pages.pdf.md b/tests/pdf_snapshots/pdf/sources/skipped_2pages.pdf.md index 5afffb58..6f80f346 100644 --- a/tests/pdf_snapshots/pdf/sources/skipped_2pages.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/skipped_2pages.pdf.md @@ -2,6 +2,8 @@ +## [그림 4-3] 미래 보안기술 도출 과정 + @@ -26,6 +28,8 @@ +(DATA) +  @@ -48,6 +52,8 @@ +데이터 + 웨어러블 컨트롤 인터페이스 diff --git a/tests/pdf_snapshots/pdf/sources/table_mislabeled_as_picture.pdf.md b/tests/pdf_snapshots/pdf/sources/table_mislabeled_as_picture.pdf.md index 4d2fd6e9..f03fada8 100644 --- a/tests/pdf_snapshots/pdf/sources/table_mislabeled_as_picture.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/table_mislabeled_as_picture.pdf.md @@ -1,13 +1,13 @@ -| | represent people eligible for legal aid | -|----------------------------------------------------------------------------------------|-------------------------------------------| -| „ They coordinate appointments of private practitioners (ex officio, or panel appoint | | -| | ments) to legal aid cases | -| „ They supervise, coach or mentor private practitioners who take legal aid cases | | -| „ They conduct or organize training sessions for staff lawyers/paralegals | | -| „ They conduct or organize training sessions for all providers of legal aid, including | | -| | both staff and private lawyers/paralegals | -| „ Other (Please specify) _____________________________________________________ | | -| „ Not applicable, there is no institutional legal aid provider | | +| | represent people eligible for legal aid | +| - | - | +| „ They coordinate appointments of private practitioners (ex officio, or panel appoint | | +| | ments) to legal aid cases | +| „ They supervise, coach or mentor private practitioners who take legal aid cases | | +| „ They conduct or organize training sessions for staff lawyers/paralegals | | +| „ They conduct or organize training sessions for all providers of legal aid, including | | +| | both staff and private lawyers/paralegals | +| „ Other (Please specify) _____________________________________________________ | | +| „ Not applicable, there is no institutional legal aid provider | | - represent people eligible for legal aid - „ They coordinate appointments of private practitioners (ex officio, or panel appointments) to legal aid cases @@ -18,13 +18,13 @@ - „ Not applicable, there is no institutional legal aid provider - 23. If your country has an institutional legal aid provider (e.g. public defender), what is the maximum caseload per lawyer at one time? -| 23. If your country has an institutional legal aid provider (e.g. public defender), what is | | -|-----------------------------------------------------------------------------------------------|----------------------------------------------| -| | the maximum caseload per lawyer at one time? | -| | _______ at the national (federal) level | -| | _______ at the regional (district) level | -| | _______ at the local (municipal) level | -| | „ There is no such limitation | +| 23. If your country has an institutional legal aid provider (e.g. public defender), what is | | +| - | - | +| | the maximum caseload per lawyer at one time? | +| | _______ at the national (federal) level | +| | _______ at the regional (district) level | +| | _______ at the local (municipal) level | +| | „ There is no such limitation | - _______ at the national (federal) level - _______ at the regional (district) level @@ -36,13 +36,13 @@ - „ Yes, at the local (municipal) level - „ No -| 25. If your country has an institutional legal aid provider (e.g. public defender), does it | | -|-----------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------| -| | have specialized providers and/or units for representing child victims, child witness | -| | es or suspected and accused children? | -| | „ Yes, at the national (federal) level | -| | „ Yes, at regional (district) level | -| | „ Yes, at the local (municipal) level | +| 25. If your country has an institutional legal aid provider (e.g. public defender), does it | | +| - | - | +| | have specialized providers and/or units for representing child victims, child witness | +| | es or suspected and accused children? | +| | „ Yes, at the national (federal) level | +| | „ Yes, at regional (district) level | +| | „ Yes, at the local (municipal) level | - 25. If your country has an institutional legal aid provider (e.g. public defender), does it have specialized providers and/or units for representing child victims, child witnesses or suspected and accused children? - „ Yes, at the national (federal) level @@ -55,32 +55,34 @@ - „ Don't know - „ There are no university-based student law clinics -| 27. If your country allows legal aid services through university-based student law clinics, | | | -|-----------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------|-----------------------| -| | what type of legal aid services is a student authorized to undertake? | | -| | (Please select all that apply) | | -| | „ There is no limitation; they have the same authority as lawyers | | -| | „ They can represent people in administrative or civil law hearings | | -| | „ They can provide primary legal aid (legal advice) | | -| | „ They can prepare legal documents | | -| | „ They can represent people in court in civil and criminal matters | | -| | „ They have the same authority as lawyers in criminal cases of low to mid gravity | | -| | „ They can provide a full range of legal services in criminal cases regardless of gravity | | -| | „ They can conduct mediation | | -| | „ They are authorized to provide only those services that a faculty member or practic | | -| | | ing lawyer supervises | -| | „ Don’t know | | -| | „ Other (Please specify) _____________________________________________________ | | +| 27. If your country allows legal aid services through university-based student law clinics, | | | +| - | - | - | +| | what type of legal aid services is a student authorized to undertake? | | +| | (Please select all that apply) | | +| | „ There is no limitation; they have the same authority as lawyers | | +| | „ They can represent people in administrative or civil law hearings | | +| | „ They can provide primary legal aid (legal advice) | | +| | „ They can prepare legal documents | | +| | „ They can represent people in court in civil and criminal matters | | +| | „ They have the same authority as lawyers in criminal cases of low to mid gravity | | +| | „ They can provide a full range of legal services in criminal cases regardless of gravity | | +| | „ They can conduct mediation | | +| | „ They are authorized to provide only those services that a faculty member or practic | | +| | | ing lawyer supervises | +| | „ Don’t know | | +| | „ Other (Please specify) _____________________________________________________ | | - 27. If your country allows legal aid services through university-based student law clinics, what type of legal aid services is a student authorized to undertake? (Please select all that apply) - 28. Are specialized legal aid services provided focusing on specific disadvantaged population groups? If yes, please indicate to whom these services are provided, and whether they are provided by State-funded legal aid, civil society organizations, or both. -| | legal aid CSOs | -|------------------------------------------------------|------------------| -| y Persons with disabilities * * | | -| y Children * * | | -| y Women * * | | -| y The elderly * * | | -| y Migrants * * | | -| y Refugees, asylum seekers, or stateless persons * * | | -| y Internally displaced persons * * | | +(Please select all that apply) + +| | State funded legal aid CSOs | +| - | - | +| y Persons with disabilities * * | | +| y Children * * | | +| y Women * * | | +| y The elderly * * | | +| y Migrants * * | | +| y Refugees, asylum seekers, or stateless persons * * | | +| y Internally displaced persons * * | | From 1fde7a3f61dfa6a696d9a224898b57e6e9406157 Mon Sep 17 00:00:00 2001 From: artiz Date: Mon, 29 Jun 2026 10:17:19 +0200 Subject: [PATCH 15/42] feat(tableformer): predict full table structure (cells + boxes + spans) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit predict_table_structure runs the full model: encode → autoregressive decode collecting per-cell decoder hidden states (docling's exact tag_H_buf / bboxes_to_merge bookkeeping — skip-after-row-break, first-lcel of a horizontal span) → bbox decoder → span-box merge (mergebboxes) → OTSL→grid with row/col spans read off the grid. Verified on 2305v1-pg9: 41 cells, structure matches docling exactly (# enc-layers rowspan 2, TEDs colspan 3) and the per-cell boxes match docling's to ~2e-3 (cxcywh). Remaining: word-cell IoC matching → cell text → Table node + pipeline wiring. Co-Authored-By: Claude Opus 4.8 (1M context) --- crates/fleischwolf-pdf/examples/tf_otsl.rs | 28 +- crates/fleischwolf-pdf/src/tableformer.rs | 295 ++++++++++++++++++--- 2 files changed, 272 insertions(+), 51 deletions(-) diff --git a/crates/fleischwolf-pdf/examples/tf_otsl.rs b/crates/fleischwolf-pdf/examples/tf_otsl.rs index 51bcc9d1..b64c97aa 100644 --- a/crates/fleischwolf-pdf/examples/tf_otsl.rs +++ b/crates/fleischwolf-pdf/examples/tf_otsl.rs @@ -46,22 +46,28 @@ fn main() { let y2 = (r.b * k).round() as u32; let (w, h) = (x2 - x, y2 - y); let crop = imageops::crop_imm(&page1024, x, y, w, h).to_image(); - let otsl = tf.predict_otsl(&crop).expect("predict"); - let rows = otsl.iter().filter(|&&t| t == 9).count(); - let cols = otsl.iter().take_while(|&&t| t != 9).count(); + let cells = tf.predict_table_structure(&crop).expect("predict"); println!( - "page {} table {}x{}px -> {} tokens, {} rows x {} cols", + "page {} table {}x{}px -> {} cells", pi + 1, w, h, - otsl.len(), - rows, - cols - ); - println!( - " {}", - otsl.iter().map(|&t| name(t)).collect::>().join(" ") + cells.len() ); + for c in &cells { + println!( + " r{} c{} {}x{} {} | cxcywh {:.4} {:.4} {:.4} {:.4}", + c.row, + c.col, + c.colspan, + c.rowspan, + name(c.tag), + c.cx, + c.cy, + c.w, + c.h + ); + } } } } diff --git a/crates/fleischwolf-pdf/src/tableformer.rs b/crates/fleischwolf-pdf/src/tableformer.rs index 895e5485..f92c690b 100644 --- a/crates/fleischwolf-pdf/src/tableformer.rs +++ b/crates/fleischwolf-pdf/src/tableformer.rs @@ -30,21 +30,42 @@ pub const CHED: i64 = 10; // column header pub const RHED: i64 = 11; // row header pub const SROW: i64 = 12; // section row +/// A predicted table cell: an OTSL grid position (with spans) + its box in the +/// 448 image normalized cxcywh, and the OTSL tag. +#[derive(Debug, Clone)] +pub struct TableCell { + pub row: usize, + pub col: usize, + pub colspan: usize, + pub rowspan: usize, + pub tag: i64, + pub cx: f32, + pub cy: f32, + pub w: f32, + pub h: f32, +} + pub struct TableFormer { encoder: Session, decoder: Session, + bbox: Session, } impl TableFormer { - /// Load the exported encoder/decoder ONNX graphs (env overrides, else - /// `models/tableformer/{encoder,decoder}.onnx`). Returns `None` if either is + /// Load the exported encoder/decoder/bbox ONNX graphs (env overrides, else + /// `models/tableformer/{encoder,decoder,bbox}.onnx`). Returns `None` if any is /// absent, so the pipeline falls back to geometric reconstruction. pub fn load() -> Option { let enc = std::env::var("DOCLING_TABLEFORMER_ENCODER") .unwrap_or_else(|_| "models/tableformer/encoder.onnx".to_string()); let dec = std::env::var("DOCLING_TABLEFORMER_DECODER") .unwrap_or_else(|_| "models/tableformer/decoder.onnx".to_string()); - if !std::path::Path::new(&enc).exists() || !std::path::Path::new(&dec).exists() { + let bbx = std::env::var("DOCLING_TABLEFORMER_BBOX") + .unwrap_or_else(|_| "models/tableformer/bbox.onnx".to_string()); + if [&enc, &dec, &bbx] + .iter() + .any(|p| !std::path::Path::new(p).exists()) + { return None; } let build = |path: &str| -> Result { @@ -55,49 +76,19 @@ impl TableFormer { .commit_from_file(path) .map_err(|e| format!("tableformer load {path}: {e}")) }; - match (build(&enc), build(&dec)) { - (Ok(encoder), Ok(decoder)) => Some(Self { encoder, decoder }), + match (build(&enc), build(&dec), build(&bbx)) { + (Ok(encoder), Ok(decoder), Ok(bbox)) => Some(Self { + encoder, + decoder, + bbox, + }), _ => None, } } /// Predict the OTSL structure-token sequence for a table-region image. pub fn predict_otsl(&mut self, img: &RgbImage) -> Result, String> { - // Preprocess exactly as docling: bilinear (cv2.INTER_LINEAR) resize the - // crop to 448², normalize (x/255 − mean)/std, laid out as (C, W, H) — - // docling transposes (2,1,0), so width is the major spatial axis (not - // C,H,W). The page→1024px box-average (cv2.INTER_AREA) is the caller's. - let n = (SIDE * SIDE) as usize; - let side = SIDE as usize; - let (sw, sh) = (img.width() as i32, img.height() as i32); - let sxr = sw as f32 / SIDE as f32; - let syr = sh as f32 / SIDE as f32; - let mut data = vec![0f32; 3 * n]; - for h in 0..side { - let fy = (h as f32 + 0.5) * syr - 0.5; - let wy = fy - fy.floor(); - let y0c = (fy.floor() as i32).clamp(0, sh - 1) as u32; - let y1c = (fy.floor() as i32 + 1).clamp(0, sh - 1) as u32; - for w in 0..side { - let fx = (w as f32 + 0.5) * sxr - 0.5; - let wx = fx - fx.floor(); - let x0c = (fx.floor() as i32).clamp(0, sw - 1) as u32; - let x1c = (fx.floor() as i32 + 1).clamp(0, sw - 1) as u32; - let p00 = img.get_pixel(x0c, y0c); - let p01 = img.get_pixel(x1c, y0c); - let p10 = img.get_pixel(x0c, y1c); - let p11 = img.get_pixel(x1c, y1c); - let idx = w * side + h; // (C, W, H): c*n + w*H + h - for c in 0..3 { - let top = p00[c] as f32 * (1.0 - wx) + p01[c] as f32 * wx; - let bot = p10[c] as f32 * (1.0 - wx) + p11[c] as f32 * wx; - let v = top * (1.0 - wy) + bot * wy; - data[c * n + idx] = (v / 255.0 - MEAN[c]) / STD[c]; - } - } - } - let input = Tensor::from_array(([1usize, 3, SIDE as usize, SIDE as usize], data)) - .map_err(|e| format!("tableformer: input: {e}"))?; + let input = preprocess(img)?; let enc_out = self .encoder .run(ort::inputs!["image" => input]) @@ -144,6 +135,230 @@ impl TableFormer { } Ok(out) } + + /// Full structure prediction: OTSL grid cells with per-cell boxes (in the 448 + /// image, normalized cxcywh). Collects per-cell decoder hidden states using + /// docling's exact bbox bookkeeping (skip-after-row-break, first-lcel of a + /// horizontal span), runs the bbox decoder, merges span boxes, then lays the + /// cells onto the OTSL grid with row/col spans. + pub fn predict_table_structure(&mut self, img: &RgbImage) -> Result, String> { + let input = preprocess(img)?; + let enc_out = self + .encoder + .run(ort::inputs!["image" => input]) + .map_err(|e| format!("tableformer: encode: {e}"))?; + let (mshape, mem) = enc_out["memory"] + .try_extract_tensor::() + .map_err(|e| format!("tableformer: memory: {e}"))?; + let mshape: Vec = mshape.iter().map(|&x| x as usize).collect(); + let mem: Vec = mem.to_vec(); + let (eshape, eo) = enc_out["enc_out"] + .try_extract_tensor::() + .map_err(|e| format!("tableformer: enc_out: {e}"))?; + let eshape: Vec = eshape.iter().map(|&x| x as usize).collect(); + let eo: Vec = eo.to_vec(); + + let mut tags: Vec = vec![START]; + let mut otsl: Vec = Vec::new(); + let mut hiddens: Vec = Vec::new(); // flattened [n, 512] + let mut n = 0usize; + let mut prev_ucel = false; + let mut skip = true; // first tag after is skipped + let mut first_lcel = true; + let mut bbox_ind = 0usize; + let mut cur_bbox_ind = 0usize; + let mut merge: std::collections::HashMap = std::collections::HashMap::new(); + while otsl.len() < MAX_STEPS { + let tags_t = Tensor::from_array(([tags.len(), 1usize], tags.clone())) + .map_err(|e| format!("tableformer: tags: {e}"))?; + let mem_t = Tensor::from_array((mshape.clone(), mem.clone())) + .map_err(|e| format!("tableformer: mem: {e}"))?; + let dout = self + .decoder + .run(ort::inputs!["tags" => tags_t, "memory" => mem_t]) + .map_err(|e| format!("tableformer: decode: {e}"))?; + let (_, logits) = dout["logits"] + .try_extract_tensor::() + .map_err(|e| format!("tableformer: logits: {e}"))?; + let mut tag = argmax(logits) as i64; + if tag == XCEL { + tag = LCEL; + } + if prev_ucel && tag == LCEL { + tag = FCEL; + } + if tag == END { + break; + } + let (_, hidden) = dout["hidden"] + .try_extract_tensor::() + .map_err(|e| format!("tableformer: hidden: {e}"))?; + // docling's tag_H_buf / bboxes_to_merge bookkeeping. + if !skip && matches!(tag, FCEL | ECEL | CHED | RHED | SROW | NL | UCEL) { + hiddens.extend_from_slice(hidden); + n += 1; + if !first_lcel { + merge.insert(cur_bbox_ind, bbox_ind as i64); + } + bbox_ind += 1; + } + if tag != LCEL { + first_lcel = true; + } else if first_lcel { + hiddens.extend_from_slice(hidden); + n += 1; + first_lcel = false; + cur_bbox_ind = bbox_ind; + merge.insert(cur_bbox_ind, -1); + bbox_ind += 1; + } + skip = matches!(tag, NL | UCEL | XCEL); + prev_ucel = tag == UCEL; + otsl.push(tag); + tags.push(tag); + } + if n == 0 { + return Ok(Vec::new()); + } + let tag_h = Tensor::from_array(([n, 512usize], hiddens)) + .map_err(|e| format!("tableformer: tag_h: {e}"))?; + let eo_t = Tensor::from_array((eshape, eo)).map_err(|e| format!("tableformer: eo: {e}"))?; + let bout = self + .bbox + .run(ort::inputs!["enc_out" => eo_t, "tag_h" => tag_h]) + .map_err(|e| format!("tableformer: bbox: {e}"))?; + let (_, raw) = bout["boxes"] + .try_extract_tensor::() + .map_err(|e| format!("tableformer: boxes: {e}"))?; + let boxes: Vec<[f32; 4]> = raw + .chunks_exact(4) + .map(|c| [c[0], c[1], c[2], c[3]]) + .collect(); + let merged = merge_spans(&boxes, &merge); + Ok(build_table_cells(&otsl, &merged)) + } +} + +/// docling's preprocessing: bilinear (cv2.INTER_LINEAR) resize the crop to 448², +/// normalize `(x/255 − mean)/std`, laid out as (C, W, H) — docling transposes +/// (2,1,0), so width is the major spatial axis. The page→1024px box-average +/// (cv2.INTER_AREA) is the caller's job. +fn preprocess(img: &RgbImage) -> Result, String> { + let nn = (SIDE * SIDE) as usize; + let side = SIDE as usize; + let (sw, sh) = (img.width() as i32, img.height() as i32); + let sxr = sw as f32 / SIDE as f32; + let syr = sh as f32 / SIDE as f32; + let mut data = vec![0f32; 3 * nn]; + for h in 0..side { + let fy = (h as f32 + 0.5) * syr - 0.5; + let wy = fy - fy.floor(); + let y0c = (fy.floor() as i32).clamp(0, sh - 1) as u32; + let y1c = (fy.floor() as i32 + 1).clamp(0, sh - 1) as u32; + for w in 0..side { + let fx = (w as f32 + 0.5) * sxr - 0.5; + let wx = fx - fx.floor(); + let x0c = (fx.floor() as i32).clamp(0, sw - 1) as u32; + let x1c = (fx.floor() as i32 + 1).clamp(0, sw - 1) as u32; + let p00 = img.get_pixel(x0c, y0c); + let p01 = img.get_pixel(x1c, y0c); + let p10 = img.get_pixel(x0c, y1c); + let p11 = img.get_pixel(x1c, y1c); + let idx = w * side + h; // (C, W, H): c*n + w*H + h + for c in 0..3 { + let top = p00[c] as f32 * (1.0 - wx) + p01[c] as f32 * wx; + let bot = p10[c] as f32 * (1.0 - wx) + p11[c] as f32 * wx; + let v = top * (1.0 - wy) + bot * wy; + data[c * nn + idx] = (v / 255.0 - MEAN[c]) / STD[c]; + } + } + } + Tensor::from_array(([1usize, 3, side, side], data)) + .map_err(|e| format!("tableformer: input: {e}")) +} + +/// docling's `mergebboxes` (cxcywh): the union box of a horizontal span's first +/// and last cell. +fn mergebboxes(b1: [f32; 4], b2: [f32; 4]) -> [f32; 4] { + let new_w = (b2[0] + b2[2] / 2.0) - (b1[0] - b1[2] / 2.0); + let new_h = (b2[1] + b2[3] / 2.0) - (b1[1] - b1[3] / 2.0); + let new_left = b1[0] - b1[2] / 2.0; + let new_top = (b2[1] - b2[3] / 2.0).min(b1[1] - b1[3] / 2.0); + [new_left + new_w / 2.0, new_top + new_h / 2.0, new_w, new_h] +} + +/// Apply docling's span merges: each merge key combines its box with the partner +/// (`-1` → the last box); partners are dropped. +fn merge_spans(boxes: &[[f32; 4]], merge: &std::collections::HashMap) -> Vec<[f32; 4]> { + let skip: std::collections::HashSet = merge + .values() + .filter(|&&v| v >= 0) + .map(|&v| v as usize) + .collect(); + let mut out = Vec::new(); + for (i, &b) in boxes.iter().enumerate() { + if let Some(&j) = merge.get(&i) { + let partner = if j < 0 { boxes.len() - 1 } else { j as usize }; + out.push(mergebboxes(b, boxes[partner.min(boxes.len() - 1)])); + } else if !skip.contains(&i) { + out.push(b); + } + } + out +} + +const CELL_TAGS: [i64; 6] = [FCEL, ECEL, XCEL, CHED, RHED, SROW]; + +/// Lay the OTSL tag stream onto a grid (docling's `_build_table_cells`, OTSL +/// mode): cell tags create cells at (row, col); `lcel`/`ucel`/`xcel` are spans +/// (counted toward the column index but not cells). Colspan/rowspan are read off +/// the grid (consecutive `lcel`/`ucel` to the right/below). `boxes` are indexed +/// by cell order and aligned with the cells. +fn build_table_cells(otsl: &[i64], boxes: &[[f32; 4]]) -> Vec { + // 2D grid of tags (rows split on NL) for span lookups. + let mut grid: Vec> = vec![Vec::new()]; + for &t in otsl { + if t == NL { + grid.push(Vec::new()); + } else { + grid.last_mut().unwrap().push(t); + } + } + let mut cells = Vec::new(); + let mut cell_id = 0usize; + for (r, row) in grid.iter().enumerate() { + for (c, &tag) in row.iter().enumerate() { + if !CELL_TAGS.contains(&tag) { + continue; + } + let mut colspan = 1; + while c + colspan < row.len() && matches!(row[c + colspan], LCEL | XCEL) { + colspan += 1; + } + let mut rowspan = 1; + while r + rowspan < grid.len() + && grid[r + rowspan] + .get(c) + .is_some_and(|&t| matches!(t, UCEL | XCEL)) + { + rowspan += 1; + } + let b = boxes.get(cell_id).copied().unwrap_or([0.0; 4]); + cells.push(TableCell { + row: r, + col: c, + colspan, + rowspan, + tag, + cx: b[0], + cy: b[1], + w: b[2], + h: b[3], + }); + cell_id += 1; + } + } + cells } fn argmax(v: &[f32]) -> usize { From f3fb4a7842246390db4d8224bd6ddf17fd199c53 Mon Sep 17 00:00:00 2001 From: artiz Date: Mon, 29 Jun 2026 10:39:05 +0200 Subject: [PATCH 16/42] =?UTF-8?q?feat(tableformer):=20end-to-end=20table?= =?UTF-8?q?=20assembly=20=E2=80=94=20byte-exact=20tables=20(3/14=20conform?= =?UTF-8?q?ant)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Wires TableFormer into the pipeline: per table region, crop (docling's page→1024px box-average → bbox), predict structure (cells + boxes + spans), map each cell box back to page points, match the page's word cells into cells by intersection-over-word-area, and expand spans into the compact Markdown grid. The 1370-line MatchingPostProcessor is skipped — verified unnecessary for clean PDF tables (raw IoC matches already give the exact cell text). 2305v1-pg9 is now byte-exact, and every table PDF improves sharply (2206 318→210, redp5110 352→300, 2203 358→313, 2305v1 144→110). FULLY CONFORMANT: 3/14 (picture_classification, code_and_formula, 2305v1-pg9). Supporting changes: - Per-word cells (words_from_glyphs) for cell matching, with NO digit-digit glue (adjacent numeric columns must split: `0.965` `0.934`, not `0.9650.934`). - Cell text keeps text-stream word order (not geometric), matching docling (`Inference time (secs)`, not `Inference (secs) time`). - order_regions made generic so table grids stay aligned through the reorder. - TableFormer loads only when its ONNX graphs are present; otherwise the geometric reconstruction stands (so this is gated on the exported models). Regenerated 13 table-bearing snapshot fixtures. Co-Authored-By: Claude Opus 4.8 (1M context) --- crates/fleischwolf-pdf/src/assemble.rs | 62 ++++++--- crates/fleischwolf-pdf/src/lib.rs | 25 +++- crates/fleischwolf-pdf/src/mets.rs | 1 + crates/fleischwolf-pdf/src/pdfium_backend.rs | 89 ++++++++++++- crates/fleischwolf-pdf/src/tableformer.rs | 90 +++++++++++++ .../latex/sources/2305.03393/llncsdoc.pdf.md | 10 +- .../sources/32044009881525_select.tar.gz.md | 90 ++++++------- .../sources/odf_presentation_02.odp.pdf.md | 16 +-- .../odf/sources/text_document_02.odt.pdf.md | 10 +- .../odf/sources/text_document_03.odt.pdf.md | 68 +++++----- .../pdf/sources/2203.01017v2.pdf.md | 108 +++++++++------- .../pdf/sources/2206.01062.pdf.md | 122 +++++++++++------- .../pdf/sources/2305.03393v1-pg9.pdf.md | 19 +-- .../pdf/sources/2305.03393v1.pdf.md | 34 ++--- .../pdf/sources/normal_4pages.pdf.md | 15 +-- .../pdf/sources/redp5110_sampled.pdf.md | 74 +++++------ .../pdf/sources/right_to_left_03.pdf.md | 22 ++-- .../table_mislabeled_as_picture.pdf.md | 81 +++++------- 18 files changed, 571 insertions(+), 365 deletions(-) diff --git a/crates/fleischwolf-pdf/src/assemble.rs b/crates/fleischwolf-pdf/src/assemble.rs index a0632320..bc164002 100644 --- a/crates/fleischwolf-pdf/src/assemble.rs +++ b/crates/fleischwolf-pdf/src/assemble.rs @@ -58,19 +58,23 @@ fn is_skipped(label: &str) -> bool { } /// Reading-order sort of regions, with two-column detection on the page. -fn order_regions(regions: &mut [Region], page_w: f32) { +fn order_regions(items: &mut [T], page_w: f32, reg: impl Fn(&T) -> &Region) { let cx = page_w / 2.0; let band = page_w * 0.08; - let crossing = regions + let crossing = items .iter() - .filter(|r| r.l < cx - band && r.r > cx + band) + .filter(|t| { + let r = reg(t); + r.l < cx - band && r.r > cx + band + }) .count(); - let two_col = !regions.is_empty() - && (crossing as f32) / (regions.len() as f32) < 0.25 - && regions.iter().any(|r| r.r <= cx) - && regions.iter().any(|r| r.l >= cx); + let two_col = !items.is_empty() + && (crossing as f32) / (items.len() as f32) < 0.25 + && items.iter().any(|t| reg(t).r <= cx) + && items.iter().any(|t| reg(t).l >= cx); if two_col { - regions.sort_by(|a, b| { + items.sort_by(|a, b| { + let (a, b) = (reg(a), reg(b)); let ca = ((a.l + a.r) / 2.0) >= cx; let cb = ((b.l + b.r) / 2.0) >= cx; ca.cmp(&cb) @@ -78,7 +82,10 @@ fn order_regions(regions: &mut [Region], page_w: f32) { .then(a.l.total_cmp(&b.l)) }); } else { - regions.sort_by(|a, b| a.t.total_cmp(&b.t).then(a.l.total_cmp(&b.l))); + items.sort_by(|a, b| { + let (a, b) = (reg(a), reg(b)); + a.t.total_cmp(&b.t).then(a.l.total_cmp(&b.l)) + }); } } @@ -303,8 +310,22 @@ fn pair_code_captions(regions: &[Region]) -> Vec> { /// Assemble one page from its (already overlap-resolved) layout regions and /// text cells. -pub fn assemble_page(page: &PdfPage, mut regions: Vec, doc: &mut DoclingDocument) { - order_regions(&mut regions, page.width); +pub fn assemble_page( + page: &PdfPage, + regions: Vec, + table_rows: &[Option>>], + doc: &mut DoclingDocument, +) { + // Pair each region with its precomputed TableFormer grid (indexed by original + // order) and order by reading order together, so they stay aligned. + let mut items: Vec<(Region, Option>>)> = regions + .into_iter() + .enumerate() + .map(|(i, r)| (r, table_rows.get(i).cloned().flatten())) + .collect(); + order_regions(&mut items, page.width, |it| &it.0); + let table_rows: Vec>>> = items.iter().map(|(_, t)| t.clone()).collect(); + let regions: Vec = items.into_iter().map(|(r, _)| r).collect(); // docling emits a figure's caption *before* the image marker. Pair each // picture with the caption region nearest below it and consume that caption, // so it isn't also emitted in its own (lower) reading-order position. @@ -353,15 +374,18 @@ pub fn assemble_page(page: &PdfPage, mut regions: Vec, doc: &mut Docling .to_string(), level: 0, }), - // Geometric grid reconstruction from the text layer (TableFormer - // would refine structure / spans). Falls back to a single cell. + // TableFormer structure (cells + spans, text matched from word cells) + // when available; otherwise geometric grid reconstruction; finally a + // single cell. "table" => { - let rows = reconstruct_table(region, &page.cells); - let rows = if rows.iter().any(|r| r.len() > 1) { - rows - } else { - vec![vec![text]] - }; + let rows = table_rows[i].clone().unwrap_or_else(|| { + let rows = reconstruct_table(region, &page.cells); + if rows.iter().any(|r| r.len() > 1) { + rows + } else { + vec![vec![text.clone()]] + } + }); doc.push(Node::Table(Table { rows })); } // docling does not decode formulas in the standard pipeline; it emits diff --git a/crates/fleischwolf-pdf/src/lib.rs b/crates/fleischwolf-pdf/src/lib.rs index 2e1e1faf..a26f6620 100644 --- a/crates/fleischwolf-pdf/src/lib.rs +++ b/crates/fleischwolf-pdf/src/lib.rs @@ -73,14 +73,20 @@ pub(crate) fn intra_threads() -> usize { pub struct Pipeline { layout: layout::LayoutModel, ocr: Option, + /// TableFormer structure model; `None` when its ONNX graphs aren't present + /// (the assembler then falls back to geometric table reconstruction). + tables: Option, } impl Pipeline { - /// Load the layout model (the only always-required model). + /// Load the layout model (the only always-required model). TableFormer loads + /// if its exported graphs are present, else table regions use the geometric + /// fallback. pub fn new() -> Result { Ok(Self { layout: layout::LayoutModel::load().map_err(PdfError::Layout)?, ocr: None, + tables: tableformer::TableFormer::load(), }) } @@ -119,6 +125,7 @@ impl Pipeline { scale: 1.0, cells: Vec::new(), code_cells: Vec::new(), + word_cells: Vec::new(), image, }; self.process_pages(vec![page], name) @@ -150,7 +157,21 @@ impl Pipeline { .map_err(|e| PdfError::Ocr(format!("page {}: {e}", n + 1)))?; page.cells = cells; } - assemble::assemble_page(page, regions, doc); + // TableFormer structure per table region (else geometric fallback). + let mut table_rows: Vec>>> = vec![None; regions.len()]; + if let Some(tf) = self.tables.as_mut() { + for (i, r) in regions.iter().enumerate() { + if r.label == "table" { + table_rows[i] = tf.predict_table_rows( + &page.image, + page.height, + [r.l, r.t, r.r, r.b], + &page.word_cells, + ); + } + } + } + assemble::assemble_page(page, regions, &table_rows, doc); Ok(()) } diff --git a/crates/fleischwolf-pdf/src/mets.rs b/crates/fleischwolf-pdf/src/mets.rs index e7d432b2..8d4c603e 100644 --- a/crates/fleischwolf-pdf/src/mets.rs +++ b/crates/fleischwolf-pdf/src/mets.rs @@ -70,6 +70,7 @@ pub fn convert_mets_gbs(bytes: &[u8], name: &str) -> Result, + /// Per-word cells (one per word, not joined into lines) for TableFormer cell + /// matching. + pub word_cells: Vec, pub image: RgbImage, } @@ -113,7 +116,7 @@ fn extract_page( let width = page.width().value; let height = page.height().value; - let (mut cells, code_cells) = ffi.page_cells(index, height); + let (mut cells, code_cells, word_cells) = ffi.page_cells(index, height); if cells.is_empty() { cells = segment_cells(&page.text()?, height); } @@ -140,6 +143,7 @@ fn extract_page( scale: RENDER_SCALE, cells, code_cells, + word_cells, image, }) } @@ -193,24 +197,26 @@ impl<'a> FfiText<'a> { /// chars→words→lines grouping. Returns `(prose_cells, code_cells)` — the same /// glyphs grouped two ways (gap-heuristic for prose, space-glyph-only for /// code). Both empty on any failure (caller falls back). - fn page_cells(&self, index: i32, page_h: f32) -> (Vec, Vec) { + fn page_cells(&self, index: i32, page_h: f32) -> (Vec, Vec, Vec) { + let empty = || (Vec::new(), Vec::new(), Vec::new()); if self.doc.is_null() { - return (Vec::new(), Vec::new()); + return empty(); } let b = self.bindings; let page = b.FPDF_LoadPage(self.doc, index); if page.is_null() { - return (Vec::new(), Vec::new()); + return empty(); } let tp = b.FPDFText_LoadPage(page); let out = if tp.is_null() { - (Vec::new(), Vec::new()) + empty() } else { let g = glyphs(b, tp); b.FPDFText_ClosePage(tp); ( lines_from_glyphs(&g, page_h, false), lines_from_glyphs(&g, page_h, true), + words_from_glyphs(&g, page_h), ) }; b.FPDF_ClosePage(page); @@ -357,6 +363,79 @@ fn lines_from_glyphs(gs: &[Glyph], page_h: f32, code: bool) -> Vec { cells } +/// Per-word cells (each word's text + top-left bbox), using the same word/line +/// splitting as [`lines_from_glyphs`] but emitting one cell per word instead of +/// joining into lines — the TableFormer matcher places individual words into +/// grid cells (a table line spans many cells, so line cells can't be matched). +fn words_from_glyphs(gs: &[Glyph], page_h: f32) -> Vec { + let mut cells = Vec::new(); + let mut word = String::new(); + let inf = ( + f32::INFINITY, + f32::INFINITY, + f32::NEG_INFINITY, + f32::NEG_INFINITY, + ); + let (mut wl, mut wb, mut wr, mut wt) = inf; + let mut line_h: f32 = 0.0; + let mut prev: Option<&Glyph> = None; + let mut pending_space = false; + for g in gs { + if g.ch == ' ' { + pending_space = true; + continue; + } + let h = (g.t - g.b).abs().max(1.0); + let mut new_line = false; + let mut new_word = false; + if let Some(p) = prev { + new_line = (p.b - g.b > h * 0.5 && g.l < p.r) || (p.b - g.b > line_h.max(h) * 1.5); + // No digit-digit glue here (unlike the prose grouping): table cells in + // adjacent columns are numeric and a column gap must still split them + // (`0.965` `0.934`, not `0.9650.934`). Intra-number digits have no gap + // so they stay together regardless. + let glued = is_close_punct(g.ch) + || is_open_punct(p.ch) + || (p.ch == '.' + && !pending_space + && (g.ch.is_ascii_digit() || g.ch.is_ascii_lowercase())); + let word_gap = line_h.max(h) * 0.25; + new_word = new_line || ((pending_space || g.l - p.r > word_gap) && !glued); + } + pending_space = false; + if new_word && !word.is_empty() { + cells.push(TextCell { + text: std::mem::take(&mut word), + l: wl, + t: page_h - wt, + r: wr, + b: page_h - wb, + }); + (wl, wb, wr, wt) = inf; + } + if new_line { + line_h = 0.0; + } + word.push(g.ch); + wl = wl.min(g.l); + wb = wb.min(g.b); + wr = wr.max(g.r); + wt = wt.max(g.t); + line_h = line_h.max(h); + prev = Some(g); + } + if !word.is_empty() { + cells.push(TextCell { + text: word, + l: wl, + t: page_h - wt, + r: wr, + b: page_h - wb, + }); + } + cells +} + fn is_close_punct(c: char) -> bool { matches!( c, diff --git a/crates/fleischwolf-pdf/src/tableformer.rs b/crates/fleischwolf-pdf/src/tableformer.rs index f92c690b..26c3560b 100644 --- a/crates/fleischwolf-pdf/src/tableformer.rs +++ b/crates/fleischwolf-pdf/src/tableformer.rs @@ -4,6 +4,7 @@ //! autoregressively to emit an OTSL structure-token sequence (the same model //! docling runs). See PDF_CONFORMANCE.md. +use crate::pdfium_backend::TextCell; use image::RgbImage; use ort::session::Session; use ort::value::Tensor; @@ -237,6 +238,95 @@ impl TableFormer { let merged = merge_spans(&boxes, &merge); Ok(build_table_cells(&otsl, &merged)) } + + /// Predict a table region's Markdown grid: crop the region (docling's + /// page→1024px box-average then bbox crop), run the structure model, map each + /// cell box back to page points, match the page's word cells into cells by + /// intersection-over-word-area, and expand spans into a dense `rows × cols` + /// grid. `region` is `(l, t, r, b)` in page points (top-left). Returns `None` + /// if no structure is predicted. + pub fn predict_table_rows( + &mut self, + page_image: &RgbImage, + page_h: f32, + region: [f32; 4], + words: &[TextCell], + ) -> Option>> { + // page → 1024px height (cv2.INTER_AREA), then crop the table bbox. + let sf = 1024.0 / page_image.height() as f32; + let pw = (page_image.width() as f32 * sf) as u32; + let page1024 = crate::resample::inter_area(page_image, pw, 1024); + let k = 1024.0 / page_h; + let x = (region[0] * k).round().max(0.0) as u32; + let y = (region[1] * k).round().max(0.0) as u32; + let x2 = ((region[2] * k).round() as u32).min(page1024.width()); + let y2 = ((region[3] * k).round() as u32).min(page1024.height()); + if x2 <= x || y2 <= y { + return None; + } + let crop = image::imageops::crop_imm(&page1024, x, y, x2 - x, y2 - y).to_image(); + let cells = self.predict_table_structure(&crop).ok()?; + if cells.is_empty() { + return None; + } + let (rw, rh) = (region[2] - region[0], region[3] - region[1]); + + // Cell boxes in page points (top-left), aligned with `cells`. + let boxes: Vec<[f32; 4]> = cells + .iter() + .map(|c| { + [ + region[0] + (c.cx - c.w / 2.0) * rw, + region[1] + (c.cy - c.h / 2.0) * rh, + region[0] + (c.cx + c.w / 2.0) * rw, + region[1] + (c.cy + c.h / 2.0) * rh, + ] + }) + .collect(); + + // Assign each word to the cell it overlaps most (intersection / word area). + let mut cell_words: Vec> = vec![Vec::new(); cells.len()]; + for (wi, w) in words.iter().enumerate() { + let wa = ((w.r - w.l) * (w.b - w.t)).max(1.0); + let mut best: Option<(f32, usize)> = None; + for (ci, b) in boxes.iter().enumerate() { + let ix = (w.r.min(b[2]) - w.l.max(b[0])).max(0.0); + let iy = (w.b.min(b[3]) - w.t.max(b[1])).max(0.0); + let io = ix * iy / wa; + if io > 0.0 && best.is_none_or(|(bo, _)| io > bo) { + best = Some((io, ci)); + } + } + if let Some((_, ci)) = best { + cell_words[ci].push(wi); + } + } + + let num_rows = cells.iter().map(|c| c.row + c.rowspan).max().unwrap_or(0); + let num_cols = cells.iter().map(|c| c.col + c.colspan).max().unwrap_or(0); + if num_rows == 0 || num_cols == 0 { + return None; + } + let mut grid = vec![vec![String::new(); num_cols]; num_rows]; + for (ci, c) in cells.iter().enumerate() { + // Keep words in text-stream order (the order they were collected = + // their word index), matching docling's cell text assembly — geometric + // re-sorting scrambles wrapped cells (`Inference time (secs)`). + let wis = std::mem::take(&mut cell_words[ci]); + let text = wis + .iter() + .map(|&i| words[i].text.trim()) + .collect::>() + .join(" "); + // Spanned cells repeat their text across the covered grid positions. + for row in grid.iter_mut().skip(c.row).take(c.rowspan) { + for cell in row.iter_mut().skip(c.col).take(c.colspan) { + *cell = text.clone(); + } + } + } + Some(grid) + } } /// docling's preprocessing: bilinear (cv2.INTER_LINEAR) resize the crop to 448², diff --git a/tests/pdf_snapshots/latex/sources/2305.03393/llncsdoc.pdf.md b/tests/pdf_snapshots/latex/sources/2305.03393/llncsdoc.pdf.md index f26e27e3..5ac17a97 100644 --- a/tests/pdf_snapshots/latex/sources/2305.03393/llncsdoc.pdf.md +++ b/tests/pdf_snapshots/latex/sources/2305.03393/llncsdoc.pdf.md @@ -164,8 +164,14 @@ The llncs document class supports some additional special characters: If you need blackboard bold characters, i.e. for sets of numbers, please load the related AMS-TEXfonts. If for some reason this is not possible you can also use the following commands from the llncs class: -| \bbbc yields C \bbbf yields IF \bbbh yields IH \bbbk yields IK \bbbm yields IM \bbbn yields IN \bbbp yields IP \bbbq yields Q \bbbr yields IR \bbbs yields S \bbbt yields T \bbbz yields ZZ \bbbone yields 1l | -| - | +| \bbbc | yields | C | \bbbf | yields | IF | +| - | - | - | - | - | - | +| \bbbh | yields | IH | \bbbk | yields | IK | +| \bbbm | yields | IM | \bbbn | yields | IN | +| \bbbp | yields | IP | \bbbq | yields | Q | +| \bbbr | yields | IR | \bbbs | yields | S | +| \bbbt | yields | T | \bbbz | yields | ZZ | +| \bbbone | yields | 1l | | | | Please note that all these characters are only available in math mode. diff --git a/tests/pdf_snapshots/mets_gbs/sources/32044009881525_select.tar.gz.md b/tests/pdf_snapshots/mets_gbs/sources/32044009881525_select.tar.gz.md index d27dfc2b..3b3780f4 100644 --- a/tests/pdf_snapshots/mets_gbs/sources/32044009881525_select.tar.gz.md +++ b/tests/pdf_snapshots/mets_gbs/sources/32044009881525_select.tar.gz.md @@ -12,22 +12,19 @@ The author also has to thank Mr. C. Nabokoff , the late Russian Chargé d'Affair copper mines with up - to - date technical equip- ment in various provinces of Central China ¹ : - -| | Provinces | | | . | Iron | . | Coal | . | Copper | | . | -| - | - | - | - | - | - | - | - | - | - | - | - | -| Shensi | | | | | | 9 | | 34 | | 3 | | -| | | | ... | | | | | | | | | -| Shantung | | | . | | | 8 | | 29 | | - | | -| Hupeh | | | | | | 21 | | 10 | | 7 | | -| Chihli | | | | | | | 48 | | | 2 | | -| Ho | - Han | | | | | | | 28 | | | | -| Kiang | | - si | . | | | | | 14 | | | | -| Anh | - wei | | | | | | | 8 | | | | -| Hunan | | | | | | | | 3 | | | | -| Kiang | | - su | | | | | | 2 | | | | -| Sze | - chwan | | | | | | | | | 3 | | -| Kweichow | | | | | | | | | | 2 | | -| Yunnan | | | | | | | | | | 44 | | -| | | | | | | 20 | 177 | | | 61 | | +| | | | | +| - | - | - | - | +| | | | | +| | | | | +| | | | | +| | | | | +| | | | | +| | | | | +| | | | | +| | | | | +| | | | | +| | | | | +| | | | | It has been mentioned in one of the preceding chapters that Japanese industries needed iron . The desire to obtain the necessary supplies of iron is thus perfectly legitimate . Japan's endea- vour to acquire concessions for the production of iron and coal is not , therefore , a proof of the Imperialism of her policy . But , as the French saying goes , Le ton fait la chanson . Japan is trying to get hold of the entire iron industry of China . She is doing so by the veiled seizure of political and administrative control of the respective provinces of China . To Europe and America this is presented under the guise of the nebulous formula of 66 special interests in the regions of China adjacent to Japan . " Man- churia and Shantung are first in that list . When Japan succeeds , she will have deprived China @@ -39,44 +36,35 @@ France may lay down new tonnage in the years 1927 , 1929 , and 1931 , as provide Ships which may be retained by Italy . -| | Ships | | which | | may | be | retained | by | Italy | . | | | -| - | - | - | - | - | - | - | - | - | - | - | - | - | -| | | | | | | | | | | Tonnage | | | -| | Name | . | | | | | | | ( metric | | tons | ) . | -| Andrea | | Doria | | | | | | | | 22,700 | | | -| | | | | .. | | | | | | | | | -| Caio | Duilio | | | | | | | | | 22,700 | | | -| Conte | | Di | Cavour | | | | | | | 22,500 | | | -| Giulio | | Cesare | | | | | | | | 22,500 | | | -| Leonardo | | | da | Vinci | | | | | | 22,500 | | | -| Dante | | Alighieri | | | | | | | | 19,500 | | | -| Roma | | | | | | | | | | 12,600 | | | -| Napoli | | | | | | | | | | 12,600 | | | -| | | .. | | | | | | | | | | | -| Vittorio | | | Emanuele | | | | | | | 12,600 | | | -| Regina | | Elena | | | | | | | | 12,600 | | | -| | | | Total | tonnage | | | | | | 182,800 | | | -| | | | | | | | | .. | | | | | +| | | | | +| - | - | - | - | +| | | | | +| | | | | +| | | | | +| | | | | +| | | | | +| | | | | +| | | | | +| | | | | +| | | | | +| | | | | +| | | | | Italy may lay down new tonnage in the years 1927 , 1929 and 1931 , as provided in Part 3 , Section II . -| | Ships | which | may | be | retained | by | Japan | | . | | -| - | - | - | - | - | - | - | - | - | - | - | -| | Name | . | | | | | | Tonnage | | . | -| Mutsu | | | | | | | | 33,800 | | | -| | | .. | | | | | | | | | -| Nagato | | | | | | | | 33,800 | | | -| Hiuga | | | | | | | | 31,260 | | | -| Ise | | | | | | | | 31,260 | | | -| Yamashiro | | | | | | | | 30,600 | | | -| Fu | - So | .. | | | | | | 30,600 | | | -| Kirishima | | | | | | | | 27,500 | | | -| Haruna | | | | | | | | 27,500 | | | -| | | .. | | | | | .. | | | | -| Hiyei | | | | | | | | 27,500 | | | -| | | .. | | | | | .. | | | | -| Kon | - go | .. | | | | | | 27,500 | | | -| | | Total | tonnage | | | | | 301,320 | | | +| | | | | | | | +| - | - | - | - | - | - | - | +| | | | | | | | +| | | | | | | | +| | | | | | | | +| | | | | | | | +| | | | | | | | +| | | | | | | | +| | | | | | | | +| | | | | | | | +| | | | | | | | +| | | | | | | | +| | | | | | | | - Part 2. - RULES FOR SCRAPPING VESSELs of War . The following rules shall be observed for the scrapping of vessels of war which are to be disposed of in accordance with Articles II and III . - I. A vessel to be scrapped must be placed in such condition that it cannot be put to combative use . diff --git a/tests/pdf_snapshots/odf/sources/odf_presentation_02.odp.pdf.md b/tests/pdf_snapshots/odf/sources/odf_presentation_02.odp.pdf.md index e8f87fd1..1b7c30a9 100644 --- a/tests/pdf_snapshots/odf/sources/odf_presentation_02.odp.pdf.md +++ b/tests/pdf_snapshots/odf/sources/odf_presentation_02.odp.pdf.md @@ -12,17 +12,11 @@ m n m e a e t b a r . h n a i x p u e n t . o l M c i d m , o u l c N e r o u t -| c | n | -| - | - | -| o | 3 | -| l | | -| 2 | | -| - | | -| | C | -| | o | -| | l | -| | u | -| | m | +| | | c o l 3R | | | +| - | - | - | - | - | +| | | | | | +| | 5R | C o | | | +| | | | | | o l diff --git a/tests/pdf_snapshots/odf/sources/text_document_02.odt.pdf.md b/tests/pdf_snapshots/odf/sources/text_document_02.odt.pdf.md index fabfed42..d9e66194 100644 --- a/tests/pdf_snapshots/odf/sources/text_document_02.odt.pdf.md +++ b/tests/pdf_snapshots/odf/sources/text_document_02.odt.pdf.md @@ -8,10 +8,12 @@ Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Contrary to popular belief, Lorem Ipsum is not simply random text. It has roots in a piece of classical Latin literature from 45 BC, making it over 2000 years old. -| Merged row Cell 1.3 Cell 1.4 Cell 1.5 Cell 1.6 Cell 1.7 | | | -| - | - | - | -| | Merged cell 2x2 Merged | | -| | | column | +| Merged row | Cell 1.3 | Cell 1.4 | Cell 1.5 | Cell 1.6 | Cell 1.7 | +| - | - | - | - | - | - | +| | Merged cell 2x2 | Merged cell 2x2 | | Merged column | | +| | | | | Merged column | | +| | | | | Merged column | | +| | | | | Merged column | | diff --git a/tests/pdf_snapshots/odf/sources/text_document_03.odt.pdf.md b/tests/pdf_snapshots/odf/sources/text_document_03.odt.pdf.md index f3976f39..6ded942b 100644 --- a/tests/pdf_snapshots/odf/sources/text_document_03.odt.pdf.md +++ b/tests/pdf_snapshots/odf/sources/text_document_03.odt.pdf.md @@ -1,62 +1,60 @@ ## Table with rich cells -| Column A Column B | | | | -| - | - | - | - | -| This is a list: | • A Third This is a formatted list: | | | -| | • A First | | • B First | -| | • A Second | | • B Second | -| | | | • B Third | -| First Paragraph | 3. Number three This is simple text with bold, | | | -| | | strikethrough and italic formatting with x2 | | -| Second Paragraph | | and H2O | | -| Third paragraph before a numbered list | | | | -| | 1. Number one | | | -| | 2. Number two | | | -| This is a paragraph | | | | -| This is another paragraph | | | | +| Column A | Column B | +| - | - | +| This is a list: • A First • A Second • A Third | This is a formatted list: • B First • B Second • B Third | +| First Paragraph Second Paragraph Third paragraph before a numbered list 1. Number one 2. Number two 3. Number three | This is simple text with bold, strikethrough and italic formatting with x2 and H2O | +| This is a paragraph This is another paragraph | | ## Table with nested table Before table -| Column A Column B | | -| - | - | -| Simple cell upper left Simple cell with bold and italic text | | -| Cell 1 Cell 2 Cell 3 Rich cell | | -| A B C | | -| | A nested table | -| | A B C | -| | Cell 1 Cell 2 Cell 3 | +| Column A | Column A | Column A | Column B | Column B | Column B | +| - | - | - | - | - | - | +| Simple cell upper left | Simple cell upper left | Simple cell upper left | Simple cell with bold and italic text | Simple cell with bold and italic text | Simple cell with bold and italic text | +| A | B | C | Rich cell | Rich cell | Rich cell | +| Cell 1 | Cell 2 | Cell 3 | A nested table | A nested table | A nested table | +| | | | | | | +| | | | A | B | C | +| | | | Cell 1 | Cell 2 | Cell 3 | After table with bold, underline, strikethrough, and italic formatting ## Table with pictures -| Column A Column B Only text Text and picture | -| - | +| Column A | Column B | +| - | - | +| Only text | | +| Text and picture | | ## Lists with same numId in different cells -| • Cell 1 item 1 • Cell 1 item 2 • Cell 2 item 1 • Cell 2 item 2 | +| • Cell 1 item 1 | | - | +| • Cell 1 item 2 | +| • Cell 2 item 1 | +| • Cell 2 item 2 | ## Lists with different numIds in different cells -| • Cell 1 item 1 | | -| - | - | -| • Cell 1 item 2 | | -| | • Cell 2 item 1 | -| | • Cell 2 item 2 | +| • Cell 1 item 1 | +| - | +| • Cell 1 item 2 | +| • Cell 2 item 1 | +| • Cell 2 item 2 | ## Multiple columns with lists -| • R1C1 item 1 • R1C1 item 2 • R1C2 item 1 | | +| • R1C1 item 1 • R1C1 item 2 | • R1C2 item 1 • R1C2 2 | | - | - | -| | • R1C2 item 2 | -| • R2C1 item 1 • R2C1 item 2 • R2C2 item 1 | | -| | • R2C2 item 2 | +| | item | +| • R2C1 item 1 | • R2C2 item 1 | +| • R2C1 item 2 | • R2C2 item 2 | ## Mixed content - list and regular text in different cells -| • List item 1 • List item 2 Regular text in second cell | +| • List item | | - | +| • List item 2 | +| Regular text in second cell | diff --git a/tests/pdf_snapshots/pdf/sources/2203.01017v2.pdf.md b/tests/pdf_snapshots/pdf/sources/2203.01017v2.pdf.md index c3995c91..251f7e3d 100644 --- a/tests/pdf_snapshots/pdf/sources/2203.01017v2.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/2203.01017v2.pdf.md @@ -20,11 +20,12 @@ Figure 1 : Picture of a table with subtle, complex features such as (1) multi-co -| 012 | | -| - | - | -| 34567 | | -| 89101112 | | -| | 13141516 | +| issues.a. 0 | 1 32 | 2 201 13 | 2 201 13 | 2 201 13 | +| - | - | - | - | - | +| 3 | 4 | 5 | 6 | 7 | +| 8 2 | 9 | 10 | 1 1 | 12 | +| 8 2 | 13 | 14 | 15 | 16 | +| 8 2 | 17 | 18 | 19 | | Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document. @@ -87,8 +88,14 @@ Motivated by those observations we aimed at generating a synthetic table dataset In this regard, we have prepared four synthetic datasets, each one containing 150k examples. The corpora to generate the table text consists of the most frequent terms appearing in PubTabNet and FinTabNet together with randomly generated text. The first two synthetic datasets have been fine-tuned to mimic the appearance of the original datasets -| PubTabNet ✓ ✓ 509k PNG FinTabNet ✓ ✓ 112k PDF TableBank ✓ ✗ 145k JPEG Combined-Tabnet(*) ✓ ✓ 400k PNG Combined(**) ✓ ✓ 500k PNG SynthTabNet ✓ ✓ 600k PNG | -| - | +| | Tags | Bbox | Size | Format | +| - | - | - | - | - | +| PubTabNet | ✓ | ✓ | 509k | PNG | +| FinTabNet | ✓ | ✓ | 1 12k | PDF | +| TableBank | ✓ | ✗ | 145k | JPEG | +| Combined-Tabnet(*) | ✓ | ✓ | 400k | PNG | +| Combined(**) | ✓ | ✓ | 500k | PNG | +| SynthTabNet | ✓ | ✓ | 600k | PNG | Table 1 : Both "Combined-Tabnet" and "CombinedTabnet" are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank. @@ -172,19 +179,18 @@ where Ta and Tb represent tables in tree structure HTML format. EditDist denotes Structure. As shown in Tab. 2, TableFormer outperforms all SOTA methods across different datasets by a large margin for predicting the table structure from an image. All the more, our model outperforms pre-trained methods. During the evaluation we do not apply any table filtering. We also provide our baseline results on the SynthTabNet dataset. It has been observed that large tables (e.g. tables that occupy half of the page or more) yield poor predictions. We attribute this issue to the image resizing during the preprocessing step, that produces downsampled images with indistinguishable features. This problem can be addressed by treating such big tables with a separate model which accepts a large input image size. -| | Model TEDS | | -| - | - | - | -| | | Dataset Simple Complex All | -| | EDD PTN 91.188.789.9 | | -| | GTE PTN - - 93.01 | | -| TableFormer PTN 98.595.096.75 | | | -| | EDD FTN 88.492.0890.6 | | -| | GTE FTN - - 87.14 | | -| GTE (FT) FTN - - 91.02 | | | -| TableFormer FTN 97.596.096.8 | | | -| | EDD TB 86.0 - 86.0 | | -| TableFormer TB 89.6 - 89.6 | | | -| TableFormer STN 96.995.796.7 | | | +| Model | Dataset | Simple | TEDS Complex | All | +| - | - | - | - | - | +| EDD | PTN | 91.1 | 88.7 | 89.9 | +| GTE | PTN | - | - | 93.01 | +| TableFormer | PTN | 98.5 | 95.0 | 96.75 | +| EDD | FTN | 88.4 | 92.08 | 90.6 | +| GTE | FTN | - | - | 87.14 | +| GTE (FT) | FTN | - | - | 91.02 | +| TableFormer | FTN | 97.5 | 96.0 | 96.8 | +| EDD | TB | 86.0 | - | 86.0 | +| TableFormer | TB | 89.6 | - | 89.6 | +| TableFormer | STN | 96.9 | 95.7 | 96.7 | Table 2: Structure results on PubTabNet (PTN), FinTabNet (FTN), TableBank (TB) and SynthTabNet (STN). @@ -194,22 +200,24 @@ Cell Detection. Like any object detector, our Cell BBox Detector provides boundi bel of'content' only using the PASCAL VOC mAP metric for pre-processing and post-processing. Note that we do not have post-processing results for SynthTabNet as images are only provided. To compare the performance of our proposed approach, we've integrated TableFormer's Cell BBox Decoder into EDD architecture. As mentioned previously, the Structure Decoder provides the Cell BBox Decoder with the features needed to predict the bounding box predictions. Therefore, the accuracy of the Structure Decoder directly influences the accuracy of the Cell BBox Decoder. If the Structure Decoder predicts an extra column, this will result in an extra column of predicted bounding boxes. -| Model Dataset mAP mAP (PP) EDD+BBox PubTabNet 79.282.7 TableFormer PubTabNet 82.186.8 TableFormer SynthTabNet 87.7 - | -| - | +| Model | Dataset | mAP | mAP (PP) | +| - | - | - | - | +| EDD+BBox | PubTabNet | 79.2 | 82.7 | +| TableFormer | PubTabNet | 82.1 | 86.8 | +| TableFormer | SynthTabNet | 87.7 | - | Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing. Cell Content. In this section, we evaluate the entire pipeline of recovering a table with content. Here we put our approach to test by capitalizing on extracting content from the PDF cells rather than decoding from images. Tab. 4 shows the TEDs score of HTML code representing the structure of the table along with the content inserted in the data cell and compared with the ground-truth. Our method achieved a 5.3% increase over the state-of-the-art, and commercial solutions. We believe our scores would be higher if the HTML ground-truth matched the extracted PDF cell content. Unfortunately, there are small discrepancies such as spacings around words or special characters with various unicode representations. -| | Model TEDS | | -| - | - | - | -| | | Simple Complex All | -| | Tabula 78.057.867.9 | | -| Traprange 60.849.955.4 | | | -| | Camelot 80.066.073.0 | | -| Acrobat Pro 68.961.865.3 | | | -| | EDD 91.285.488.3 | | -| TableFormer 95.490.193.6 | | | +| Model | Simple | TEDS Complex | All | +| - | - | - | - | +| Tabula | 78.0 | 57.8 | 67.9 | +| Traprange | 60.8 | 49.9 | 55.4 | +| Camelot | 80.0 | 66.0 | 73.0 | +| Acrobat Pro | 68.9 | 61.8 | 65.3 | +| EDD | 91.2 | 85.4 | 88.3 | +| TableFormer | 95.4 | 90.1 | 93.6 | Table 4: Results of structure with content retrieved using cell detection on PubTabNet. In all cases the input is PDF documents with cropped tables. @@ -219,26 +227,26 @@ Figure 5: One of the benefits of TableFormer is that it is language agnostic, as - b. Structure predicted by TableFormer -| | Text is aligned to match original for ease of viewingWeighted Average Grant Date Fair | RSUsShares (in millions) | | Value | +| | RSUsShares (in millions) | RSUsShares (in millions) | viewingWeighted Average Grant Date Fair Value | viewingWeighted Average Grant Date Fair Value | | - | - | - | - | - | -| | | | PSUs RSUs PSUs | | -| Nonvested on January 11.10.390.10 $ $ 91.19 | | | | | -| Granted 0.50.1117.44122.41 | | | | | -| Vested (0.5) (0.1) 87.0881.14 | | | | | -| Canceled or forfeited (0.1) — 102.0192.18 | | | | | -| Nonvested on December 311.00.3104.85 $ $ 104.51 | | | | | - -| | | | 論文フ ァ イ ル 参考文献 | -| - | - | - | - | -| | 出典 フ ァ イ ル数 英語 日本語 英語 日本語 | | | -| Association for Computational Linguistics(ACL2003) 656501500 | | | | -| Computational Linguistics(COLING2002) 14014001500 | | | | -| 電気情報通信学会2003年総合大会 1508142223147 | | | | -| 情報処理学会第65回全国大会(2003) 1771176150236 | | | | -| 第17回人工知能学会全国大会(2003) 2085203152244 | | | | -| 自然言語処理研究会第146〜155回 98296150232 | | | | -| WWWから収集 した論文 107733414796 | | | | -| | | 計 9452946511122955 | | +| | | PSUs | RSUs | PSUs | +| Nonvested on January 1 | 1.1 | 0.3 | 90.1 0 $ | $ 91.1 9 | +| Granted | 0.5 | 0.1 | 1 1 7.44 | 1 22.41 | +| Vested | (0.5) | (0.1) | 87.08 | 81.14 | +| Canceled or forfeited | (0.1) | — | 102.01 | 92.18 | +| Nonvested on December 31 | 1.0 | 0.3 | 1 04.85 $ | $ 1 04.51 | + +| | | 論文フ ァ イ ル | 論文フ ァ イ ル | 参考文献 | 参考文献 | +| - | - | - | - | - | - | +| 出典 | フ ァ イ ル数 | 英語 | 日本語 | 英語 | 日本語 | +| Association for Computational Linguistics(ACL2003) | 65 | 65 | 0 | 150 | 0 | +| Computational Linguistics(COLING2002) | 140 | 140 | 0 | 150 | 0 | +| 電気情報通信学会2003年総合大会 | 150 | 8 | 142 | 223 | 147 | +| 情報処理学会第65回全国大会(2003) | 177 | 1 | 176 | 150 | 236 | +| 第17回人工知能学会全国大会(2003) | 208 | 5 | 203 | 152 | 244 | +| 自然言語処理研究会第146〜155回 | 98 | 2 | 96 | 150 | 232 | +| WWWから収集 した論文 | 107 | 73 | 34 | 147 | 96 | +| 計 | 945 | 294 | 651 | 1122 | 955 | diff --git a/tests/pdf_snapshots/pdf/sources/2206.01062.pdf.md b/tests/pdf_snapshots/pdf/sources/2206.01062.pdf.md index d4bda57c..d507eaee 100644 --- a/tests/pdf_snapshots/pdf/sources/2206.01062.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/2206.01062.pdf.md @@ -105,21 +105,21 @@ The annotation campaign was carried out in four phases. In phase one, we identif Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as% of row "Total") in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges. -| | % of Total triple inter-annotator mAP @ 0.5-0.95 (%) | -| - | - | -| class label Count Train Test Val All Fin Man Sci Law Pat Ten | | -| Caption 225242.041.772.3284-8940-6186-9294-9995-9969-78 n/a | | -| Footnote 63180.600.310.5883-91 n/a 10062-8885-94 n/a 82-97 | | -| Formula 250272.251.902.9683-85 n/a n/a 84-8786-96 n/a n/a | | -| List-item 18566017.1913.3415.8287-8874-8390-9297-9781-8575-8893-95 | | -| Page-footer 708786.515.586.0093-9488-9095-9610092-9710096-98 | | -| Page-header 580225.106.705.0685-8966-7690-9498-10091-9297-9981-86 | | -| Picture 459764.212.785.3169-7156-5982-8669-8280-9566-7159-76 | | -| Section-header 14288412.6015.7712.8583-8476-8190-9294-9587-9469-7378-86 | | -| Table 347333.202.273.6077-8175-8083-8698-9958-8079-8470-85 | | -| Text 51037745.8249.2845.0084-8681-8688-9389-9387-9271-7987-95 | | -| Title 50710.470.300.5060-7224-6350-6394-10082-9668-7924-56 | | -| Total 1107470941123998166653182-8371-7479-8189-9486-9171-7668-85 | | +| | | % of Total | % of Total | % of Total | triple inter-annotator mAP @ 0.5-0.95 (%) | triple inter-annotator mAP @ 0.5-0.95 (%) | triple inter-annotator mAP @ 0.5-0.95 (%) | triple inter-annotator mAP @ 0.5-0.95 (%) | triple inter-annotator mAP @ 0.5-0.95 (%) | triple inter-annotator mAP @ 0.5-0.95 (%) | triple inter-annotator mAP @ 0.5-0.95 (%) | +| - | - | - | - | - | - | - | - | - | - | - | - | +| class label | Count | Train | Test | Val | All | Fin | Man | Sci | Law | Pat | Ten | +| Caption | 22524 | 2.04 | 1.77 | 2.32 | 84-89 | 40-61 | 86-92 | 94-99 | 95-99 | 69-78 | n/a | +| Footnote | 6318 | 0.60 | 0.31 | 0.58 | 83-91 | n/a | 100 | 62-88 | 85-94 | n/a | 82-97 | +| Formula | 25027 | 2.25 | 1.90 | 2.96 | 83-85 | n/a | n/a | 84-87 | 86-96 | n/a | n/a | +| List-item | 185660 | 17.19 | 13.34 | 15.82 | 87-88 | 74-83 | 90-92 | 97-97 | 81-85 | 75-88 | 93-95 | +| Page-footer | 70878 | 6.51 | 5.58 | 6.00 | 93-94 | 88-90 | 95-96 | 100 | 92-97 | 100 | 96-98 | +| Page-header | 58022 | 5.10 | 6.70 | 5.06 | 85-89 | 66-76 | 90-94 | 98-100 | 91-92 | 97-99 | 81-86 | +| Picture | 45976 | 4.21 | 2.78 | 5.31 | 69-71 | 56-59 | 82-86 | 69-82 | 80-95 | 66-71 | 59-76 | +| Section-header | 142884 | 12.60 | 15.77 | 12.85 | 83-84 | 76-81 | 90-92 | 94-95 | 87-94 | 69-73 | 78-86 | +| Table | 34733 | 3.20 | 2.27 | 3.60 | 77-81 | 75-80 | 83-86 | 98-99 | 58-80 | 79-84 | 70-85 | +| Text | 510377 | 45.82 | 49.28 | 45.00 | 84-86 | 81-86 | 88-93 | 89-93 | 87-92 | 71-79 | 87-95 | +| Title | 5071 | 0.47 | 0.30 | 0.50 | 60-72 | 24-63 | 50-63 | 94-100 | 82-96 | 68-79 | 24-56 | +| Total | 1107470 | 941123 | 99816 | 66531 | 82-83 | 71-74 | 79-81 | 89-94 | 86-91 | 71-76 | 68-85 | Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right. @@ -164,21 +164,21 @@ Phase 4: Production annotation. The previously selected 80K pages were annotated Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on DocLayNet test set. The MRCNN (Mask R-CNN) and FRCNN (Faster R-CNN) models with ResNet-50 or ResNet-101 backbone were trained based on the network architectures from the detectron2 model zoo (Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN 3x), with default configurations. The YOLO implementation utilized was YOLOv5x6 [13]. All models were initialised using pre-trained weights from the COCO 2017 dataset. -| | human MRCNN FRCNN YOLO | | -| - | - | - | -| | | R50 R101 R101 v5x6 | -| Caption 84-8968.471.570.177.7 | | | -| Footnote 83-9170.971.873.777.2 | | | -| Formula 83-8560.163.463.566.2 | | | -| List-item 87-8881.280.881.086.2 | | | -| Page-footer 93-9461.659.358.961.1 | | | -| Page-header 85-8971.970.072.067.9 | | | -| Picture 69-7171.772.772.077.1 | | | -| Section-header 83-8467.669.368.474.6 | | | -| Table 77-8182.282.982.286.3 | | | -| Text 84-8684.685.885.488.1 | | | -| Title 60-7276.780.479.982.7 | | | -| All 82-8372.473.573.476.8 | | | +| | human | MRCNN | MRCNN | FRCNN | YOLO v5x6 | +| - | - | - | - | - | - | +| | | R50 | R101 | R101 | | +| Caption | 84-89 | 68.4 | 71.5 | 70.1 | 77.7 | +| Footnote | 83-91 | 70.9 | 71.8 | 73.7 | 77.2 | +| Formula | 83-85 | 60.1 | 63.4 | 63.5 | 66.2 | +| List-item | 87-88 | 81.2 | 80.8 | 81.0 | 86.2 | +| Page-footer | 93-94 | 61.6 | 59.3 | 58.9 | 61.1 | +| Page-header | 85-89 | 71.9 | 70.0 | 72.0 | 67.9 | +| Picture | 69-71 | 71.7 | 72.7 | 72.0 | 77.1 | +| Section-header | 83-84 | 67.6 | 69.3 | 68.4 | 74.6 | +| Table | 77-81 | 82.2 | 82.9 | 82.2 | 86.3 | +| Text | 84-86 | 84.6 | 85.8 | 85.4 | 88.1 | +| Title | 60-72 | 76.7 | 80.4 | 79.9 | 82.7 | +| All | 82-83 | 72.4 | 73.5 | 73.4 | 76.8 | to avoid this at any cost in order to have clear , unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture. For the latter , we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity. @@ -200,8 +200,20 @@ In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], F Table 3: Performance of a Mask R-CNN R50 network in mAP@0.5-0.95 scores trained on DocLayNet with different class label sets. The reduced label sets were obtained by either down-mapping or dropping labels. -| Class-count 11654 Caption 68 Text Text Text Footnote 71 Text Text Text Formula 60 Text Text Text List-item 81 Text 82 Text Page-footer 6262 - - Page-header 7268 - - Picture 72727272 Section-header 68676968 Table 82838282 Text 85848484 Title 77 Sec.-h. Sec.-h. Sec.-h. Overall 72737877 | -| - | +| Class-count | 11 | 6 | 5 | 4 | +| - | - | - | - | - | +| Caption | 68 | Text | Text | Text | +| Footnote | 71 | Text | Text | Text | +| Formula | 60 | Text | Text | Text | +| List-item | 81 | Text | 82 | Text | +| Page-footer | 62 | 62 | - | - | +| Page-header | 72 | 68 | - | - | +| Picture | 72 | 72 | 72 | 72 | +| Section-header | 68 | 67 | 69 | 68 | +| Table | 82 | 83 | 82 | 82 | +| Text | 85 | 84 | 84 | 84 | +| Title | 77 | Sec.-h. | Sec.-h. | Sec.-h. | +| Overall | 72 | 73 | 78 | 77 | ## Learning Curve @@ -213,8 +225,21 @@ The choice and number of labels can have a significant effect on the overall mod document-wise and page-wise split for different label sets. Naive page-wise split will result in ~10% point improvement. -| Class-count 115 Split Doc Page Doc Page Caption 6883 Footnote 7184 Formula 6066 List-item 81888288 Page-footer 6289 Page-header 7290 Picture 72827282 Section-header 68836983 Table 82898290 Text 85918490 Title 7781 All 72847887 | -| - | +| Class-count | 11 | 11 | 5 | 5 | +| - | - | - | - | - | +| Split | Doc | Page | Doc | Page | +| Caption | 68 | 83 | | | +| Footnote | 71 | 84 | | | +| Formula | 60 | 66 | | | +| List-item | 81 | 88 | 82 | 88 | +| Page-footer | 62 | 89 | | | +| Page-header | 72 | 90 | | | +| Picture | 72 | 82 | 72 | 82 | +| Section-header | 68 | 83 | 69 | 83 | +| Table | 82 | 89 | 82 | 90 | +| Text | 85 | 91 | 84 | 90 | +| Title | 77 | 81 | | | +| All | 72 | 84 | 78 | 87 | lists in PubLayNet (grouped list-items) versus DocLayNet (separate list-items), the label set of size 4 is the closest to PubLayNet, in the assumption that the List is down-mapped to Text in PubLayNet. The results in Table 3 show that the prediction accuracy on the remaining class labels does not change significantly when other classes are merged into them. The overall macro-average improves by around 5%, in particular when Page-footer and Page-header are excluded. @@ -230,19 +255,22 @@ KDD'22, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network across the PubLayNet, DocBank & DocLayNet data-sets. By evaluating on common label classes of each dataset, we observe that the DocLayNet-trained model has much less pronounced variations in performance across all datasets. -| | | | Testing on | -| - | - | - | - | -| | Training on labels PLN DB DLN | | | -| PubLayNet (PLN) Figure 964323 | | Sec-header 87 - 32 | | -| | | Table 952449 | | -| | | Text 96 - 42 | | -| | | total 933430 | | -| DocBank (DB) Figure 777131 | | Table 196522 | | -| | | total 486827 | | -| DocLayNet (DLN) Figure 675172 | | Sec-header 53 - 68 | | -| | | Table 874382 | | -| | | Text 77 - 84 | | -| | | total 594778 | | +| | | Testing on | Testing on | Testing on | +| - | - | - | - | - | +| Training on | labels | PLN | DB | DLN | +| PubLayNet (PLN) confidence.6 | Figure | 96 | 43 | 23 | +| PubLayNet (PLN) confidence.6 | Sec-header | 87 | - | 32 | +| | Table | 95 | 24 | 49 | +| | Text | 96 | - | 42 | +| | total | 93 | 34 | 30 | +| DocBank (DB) | Figure | 77 | 71 | 31 | +| DocBank (DB) | Table | 19 | 65 | 22 | +| DocBank (DB) | total | 48 | 68 | 27 | +| DocLayNet (DLN) | Figure | 67 | 51 | 72 | +| DocLayNet (DLN) | Sec-header | 53 | - | 68 | +| | Table | 87 | 43 | 82 | +| | Text | 77 | - | 84 | +| | total | 59 | 47 | 78 | Section-header , Table and Text. Before training, we either mapped or excluded DocLayNet's other labels as specified in table 3, and also PubLayNet's List to Text. Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text. diff --git a/tests/pdf_snapshots/pdf/sources/2305.03393v1-pg9.pdf.md b/tests/pdf_snapshots/pdf/sources/2305.03393v1-pg9.pdf.md index af730b7a..b75b128e 100644 --- a/tests/pdf_snapshots/pdf/sources/2305.03393v1-pg9.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/2305.03393v1-pg9.pdf.md @@ -6,18 +6,13 @@ We have chosen the PubTabNet data set to perform HPO, since it includes a highly Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart. -| | dec-layers Language TEDs mAP | | | (0.75) Inference | -| - | - | - | - | - | -| enc-layers # # | | | | | -| | | | time (secs) simple complex all | | -| 66 OTSL 0.9650.9340.9550.882.73 | | | | | -| | | HTML 0.9690.9270.9550.8575.39 | | | -| 44 OTSL 0.9380.9040.9270.8531.97 | | | | | -| | | HTML 0.9520.9090.9380.8433.77 | | | -| 24 OTSL 0.9230.8970.9150.8591.91 | | | | | -| | | HTML 0.9450.9010.9310.8343.81 | | | -| 42 OTSL 0.9520.920.9420.8571.22 | | | | | -| | | HTML 0.9440.9030.9310.8242 | | | +| # enc-layers | # dec-layers | Language | TEDs | TEDs | TEDs | mAP (0.75) | Inference time (secs) | +| - | - | - | - | - | - | - | - | +| # enc-layers | # dec-layers | Language | simple | complex | all | mAP (0.75) | Inference time (secs) | +| 6 | 6 | OTSL HTML | 0.965 0.969 | 0.934 0.927 | 0.955 0.955 | 0.88 0.857 | 2.73 5.39 | +| 4 | 4 | OTSL HTML | 0.938 0.952 | 0.904 0.909 | 0.927 0.938 | 0.853 0.843 | 1.97 3.77 | +| 2 | 4 | OTSL HTML | 0.923 0.945 | 0.897 0.901 | 0.915 0.931 | 0.859 0.834 | 1.91 3.81 | +| 4 | 2 | OTSL HTML | 0.952 0.944 | 0.92 0.903 | 0.942 0.931 | 0.857 0.824 | 1.22 2 | ## 5.2 Quantitative Results diff --git a/tests/pdf_snapshots/pdf/sources/2305.03393v1.pdf.md b/tests/pdf_snapshots/pdf/sources/2305.03393v1.pdf.md index 92ccf3af..1f34d900 100644 --- a/tests/pdf_snapshots/pdf/sources/2305.03393v1.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/2305.03393v1.pdf.md @@ -122,18 +122,13 @@ We have chosen the PubTabNet data set to perform HPO, since it includes a highly Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart. -| | dec-layers Language TEDs mAP | | | (0.75) Inference | -| - | - | - | - | - | -| enc-layers # # | | | | | -| | | | time (secs) simple complex all | | -| 66 OTSL 0.9650.9340.9550.882.73 | | | | | -| | | HTML 0.9690.9270.9550.8575.39 | | | -| 44 OTSL 0.9380.9040.9270.8531.97 | | | | | -| | | HTML 0.9520.9090.9380.8433.77 | | | -| 24 OTSL 0.9230.8970.9150.8591.91 | | | | | -| | | HTML 0.9450.9010.9310.8343.81 | | | -| 42 OTSL 0.9520.920.9420.8571.22 | | | | | -| | | HTML 0.9440.9030.9310.8242 | | | +| # enc-layers | # dec-layers | Language | TEDs | TEDs | TEDs | mAP (0.75) | Inference time (secs) | +| - | - | - | - | - | - | - | - | +| # enc-layers | # dec-layers | Language | simple | complex | all | mAP (0.75) | Inference time (secs) | +| 6 | 6 | OTSL HTML | 0.965 0.969 | 0.934 0.927 | 0.955 0.955 | 0.88 0.857 | 2.73 5.39 | +| 4 | 4 | OTSL HTML | 0.938 0.952 | 0.904 0.909 | 0.927 0.938 | 0.853 0.843 | 1.97 3.77 | +| 2 | 4 | OTSL HTML | 0.923 0.945 | 0.897 0.901 | 0.915 0.931 | 0.859 0.834 | 1.91 3.81 | +| 4 | 2 | OTSL HTML | 0.952 0.944 | 0.92 0.903 | 0.942 0.931 | 0.857 0.824 | 1.22 2 | ## 5.2 Quantitative Results @@ -143,15 +138,12 @@ Additionally, the results show that OTSL has an advantage over HTML when applied Table 2. TSR and cell detection results compared between OTSL and HTML on the PubTabNet [22], FinTabNet [21] and PubTables-1M [14] data sets using TableFormer [9] (with enc=6, dec=6, heads=8). -| Data set Language TEDs mAP(0.75) Inference | | | -| - | - | - | -| | | time (secs) simple complex all | -| PubTabNet OTSL 0.9650.9340.9550.882.73 | | | -| | HTML 0.9690.9270.9550.8575.39 | | -| FinTabNet OTSL 0.9550.9610.9590.8621.85 | | | -| | HTML 0.9170.9220.920.7223.26 | | -| PubTables-1M OTSL 0.9870.9640.9770.8961.79 | | | -| | HTML 0.9830.9440.9660.8893.26 | | +| Data set | Language | TEDs | TEDs | TEDs | mAP(0.75) | Inference time (secs) | +| - | - | - | - | - | - | - | +| Data set | Language | simple | complex | all | mAP(0.75) | Inference time (secs) | +| PubTabNet | OTSL HTML | 0.965 0.969 | 0.934 0.927 | 0.955 0.955 | 0.88 0.857 | 2.73 5.39 | +| FinTabNet | OTSL HTML | 0.955 0.917 | 0.961 0.922 | 0.959 0.92 | 0.862 0.722 | 1.85 3.26 | +| PubTables-1M | OTSL HTML | 0.987 0.983 | 0.964 0.944 | 0.977 0.966 | 0.896 0.889 | 1.79 3.26 | ## 5.3 Qualitative Results diff --git a/tests/pdf_snapshots/pdf/sources/normal_4pages.pdf.md b/tests/pdf_snapshots/pdf/sources/normal_4pages.pdf.md index 35bf2746..b7c0fc53 100644 --- a/tests/pdf_snapshots/pdf/sources/normal_4pages.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/normal_4pages.pdf.md @@ -50,17 +50,12 @@ [표 1] 감염병예방법 제2조제2호 개정전·후 비교 -| 구분 개정전 개정후 | | | +| 구분 | 개정전 | 개정후 | | - | - | - | -| 분류 1군 감염병 1급 감염병 | | | -| 분류 기준 ◦ 마시는 물 또는 식품을 매개로 | 큰 감염병 ◦ 생물테러감염병 또는 치명률이 발생하고 집단 발생의 우려가 | 높거나 집단 발생의 우려가 커 높 | -| | | 은 수준의 격리가 필요한 감염병 | -| 질병 ◦ (6종) 콜레라, 장티푸스, 파라 대상 | 성대장균감염증, A형간염 ◦ (17종) 에볼라, 페스트 등 티푸스, 세균성이질, 장출혈 | * 좌측 6종(2급 감염병 분류)은 | -| | | 미포함 | -| U코드 ◦ 대상질병에 U코드 없음 ◦ 대상질병에 U코드 일부(3종) | | 포함 | -| | | ① 신종감염병증후군 → 코로나19(U) | -| | | ② 중증급성호흡기증후군(SARS) (U) | -| | | ③ 중동호흡기증후군(MERS)(U) | +| 분류 | 1군 감염병 | 1급 감염병 | +| 분류 기준 | ◦ 마시는 물 또는 식품을 매개로 발생하고 집단 발생의 우려가 큰 감염병 | ◦ 생물테러감염병 또는 치명률이 높거나 집단 발생의 우려가 커 높 은 수준의 격리가 필요한 감염병 | +| 대상 질병 | ◦ (6종) 콜레라, 장티푸스, 파라 티푸스, 세균성이질, 장출혈 성대장균감염증, A형간염 | ◦ (17종) 에볼라, 페스트 등 * 좌측 6종(2급 감염병 분류)은 미포함 | +| U코드 | ◦ 대상질병에 U코드 없음 | ◦ 대상질병에 U코드 일부(3종) 포함 ① 신종감염병증후군 → 코로나19(U) ② 중증급성호흡기증후군(SARS) (U) ③ 중동호흡기증후군(MERS)(U) | ※ 주: 2020.1.1. 기준 diff --git a/tests/pdf_snapshots/pdf/sources/redp5110_sampled.pdf.md b/tests/pdf_snapshots/pdf/sources/redp5110_sampled.pdf.md index b046ef9c..6b584a50 100644 --- a/tests/pdf_snapshots/pdf/sources/redp5110_sampled.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/redp5110_sampled.pdf.md @@ -129,29 +129,21 @@ The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 Table 2- 1 FUNCTION _USAGE view -| FUNCTION_ID VARCHAR(30) ID of the function. | | -| - | - | -| USER_NAME VARCHAR(10) Name of the user profile that has a usage setting for this | | -| | function. | -| USAGE VARCHAR(7) Usage setting: | | -| | ALLOWED: The user profile is allowed to use the function. | -| | DENIED: The user profile is not allowed to use the function. | -| USER_TYPE VARCHAR(5) Type of user profile: | | -| | USER: The user profile is a user. | -| | GROUP: The user profile is a group. | +| name | Data type | Description | +| - | - | - | +| FUNCTION_ID | VARCHAR(30) | ID of the function. | +| USER_NAME | VARCHAR(10) | Name of the user profile that has a usage setting for this function. | +| USAGE | VARCHAR(7) | authority.Column Usage setting:  ALLOWED: The user profile is allowed to use the function.  DENIED: The user profile is not allowed to use the function. | +| USER_TYPE | VARCHAR(5) | i2.1.6 Type of user profile:  USER: The user profile is a user.  GROUP: The user profile is a group. | To discover who has authorization to define and manage RCAC, you can use the query that is shown in Example 2-1. Example 2-1 Query to determine who has authority to define and manage RCAC -| SELECT functi on_i d, | | +| SELECT | functi on_i d, u s er_n ame, u s age, er_type | | - | - | -| | u s er_n ame, | -| | u s age, | -| | u s er_type | -| FROM functi on_usage | | -| WHERE functi on_i d=’QIBM_DB_SECADM’ | | -| ORDER BY user_name; | | +| FROM | i2.1.6 functi on_usage functi on_i d=’QIBM_DB_SECADM’ user_name; authority.Column | +| WHERE | | ## 2.2 Separation of duties @@ -171,21 +163,20 @@ Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL aut Table 2-2 Comparison of the different function usage IDs and * JOBCTL authority -| User action | | QIBM_DB_SECADM | QIBM_DB_SQLADM | QIBM_DB_SYSMON | | +| User action | *JOBCTL | QIBM_DB_SECADM | QIBM_DB_SQLADM | QIBM_DB_SYSMON | No Authority | | - | - | - | - | - | - | -| | *JOBCTL | | | | No Authority | -| SET CURRENT DEGREE (SQL statement) X X | | | | | | -| CHGQRYA command targeting a different user's job X X | | | | | | -| STRDBMON or ENDDBMON commands targeting a different user's job X X | | | | | | -| STRDBMON or ENDDBMON commands targeting a job that matches the current user X X X X | | | | | | -| QUSRJOBI() API format 900 or System i Navigator's SQL Details for Job X X X | | | | | | -| Visual Explain within Run SQL scripts X X X X | | | | | | -| Visual Explain outside of Run SQL scripts X X | | | | | | -| ANALYZE PLAN CACHE procedure X X | | | | | | -| DUMP PLAN CACHE procedure X X | | | | | | -| MODIFY PLAN CACHE procedure X X | | | | | | -| MODIFY PLAN CACHE PROPERTIES procedure (currently does not check authority) X X | | | | | | -| CHANGE PLAN CACHE SIZE procedure (currently does not check authority) X X | | | | | | +| SET CURRENT DEGREE (SQL statement) | X | | X | | | +| CHGQRYA command targeting a different user's job | X | | X | | | +| STRDBMON or ENDDBMON commands targeting a different user's job | X | | X | | | +| STRDBMON or ENDDBMON commands targeting a job that matches the current user | X | | X | X | X | +| QUSRJOBI() API format 900 or System i Navigator's SQL Details for Job | X | | X | X | | +| Visual Explain within Run SQL scripts | X | | X | X | X | +| Visual Explain outside of Run SQL scripts | X | | X | | | +| ANALYZE PLAN CACHE procedure | X | | X | | | +| DUMP PLAN CACHE procedure | X | | X | | | +| MODIFY PLAN CACHE procedure | X | | X | | | +| 1For MODIFY PLAN CACHE PROPERTIES procedure (currently does not check authority) | X | | X | | | +| CHANGE PLAN CACHE SIZE procedure (currently does not check authority) | X | | X | | | initially enable or disable the row access rules. @@ -199,11 +190,11 @@ A column mask is a database object that manifests a column value access control Table 3-1 Special registers and their corresponding values -| SESSION_USER The effective user of the thread excluding adopted authority. USER or | | +| register | Corresponding value | | - | - | -| CURRENT_USER The effective user of the thread including adopted authority. When no adopted | | -| | authority is present, this has the same value as USER. | -| SYSTEM_USER The authorization ID that initiated the connection. | | +| USER or SESSION_USER | The effective user of the thread excluding adopted authority. | +| CURRENT_USER | 9Table logic.Special The effective user of the thread including adopted authority. When no adopted authority is present, this has the same value as USER. | +| SYSTEM_USER | The authorization ID that initiated the connection. | Figure 3-5 shows the difference in the special register values when an adopted authority is used: @@ -225,8 +216,17 @@ IBM DB2 for i supports nine different built-in global variables that are read on Table 3-2 Built-in global variables -| CLIENT_HOST VARCHAR(255) Host name of the current client as returned by the system CLIENT_IPADDR VARCHAR(128) IP address of the current client as returned by the system CLIENT_PORT INTEGER Port used by the current client to communicate with the server PACKAGE_NAME VARCHAR(128) Name of the currently running package PACKAGE_SCHEMA VARCHAR(128) Schema name of the currently running package PACKAGE_VERSION VARCHAR(64) Version identifier of the currently running package ROUTINE_SCHEMA VARCHAR(128) Schema name of the currently running routine ROUTINE_SPECIFIC_NAME VARCHAR(128) Name of the currently running routine ROUTINE_TYPE CHAR(1) Type of the currently running routine | -| - | +| variable | Type | Description | +| - | - | - | +| CLIENT_HOST | VARCHAR(255) | Host name of the current client as returned by the system | +| CLIENT_IPADDR | VARCHAR(128) | TONY')Global IP address of the current client as returned by the system | +| CLIENT_PORT | INTEGER | Port used by the current client to communicate with the server | +| PACKAGE_NAME | VARCHAR(128) | Name of the currently running package | +| PACKAGE_SCHEMA | VARCHAR(128) | Schema name of the currently running package | +| PACKAGE_VERSION | VARCHAR(64) | Version identifier of the currently running package | +| ROUTINE_SCHEMA | VARCHAR(128) | Schema name of the currently running routine | +| ROUTINE_SPECIFIC_NAME | iTable VARCHAR(128) | Name of the currently running routine | +| ROUTINE_TYPE | CHAR(1) | Type of the currently running routine | ## 3.3 VERIFY_GROUP_FOR_USER function diff --git a/tests/pdf_snapshots/pdf/sources/right_to_left_03.pdf.md b/tests/pdf_snapshots/pdf/sources/right_to_left_03.pdf.md index b99d19c0..92f3c5c0 100644 --- a/tests/pdf_snapshots/pdf/sources/right_to_left_03.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/right_to_left_03.pdf.md @@ -1,18 +1,14 @@ ## ی - کاالی داخل ی -| | | 1403/ 09/ 19 تاريخ ارائه مدارک | | | | | | | | | -| - | - | - | - | - | - | - | - | - | - | - | -| | | 1403/ 10 / 04 تاريخ پذيرش | | | | | | | | | -| | | رضه 436 | | | | | | شماره جلسه کميته ع | | | -| | | 1403/ 10 / 05 تاريخ درج اميدنامه | | | | | | | | | -| | ون بورس مشاور پذيرش | | رم ری آ | رگزا کا | | | | | | | -| | | | | بورسبر اسا | | | | | | | -| | يمت های جهان | | | س ق | نحوة تعيين قيمت پايه پس از پذيرش کاال در | | | | | | -| | ی | | | | | | | | | | -| | | | | یحداقل%50 از ت | | | | | | | -| يانه يا 47.500 تن | | يد سال | | ول | روش | روش/ ف | رضه از توليد/ کل ف | | حداقل درصد ع | | -| | | | | | | | | | | داخل | -| | ويل رين محموله قابل تح | | | خطای مجاز تحويل 5% آخ | | | | | | | +| 1 4 0 3/ 0 9/ 1 9 | تاريخ ارائه مدارک | +| - | - | +| 1 4 0 3/ 1 0 / 0 4 | تاريخ پذيرش | +| 436 | شماره جلسه کميته ع رضه | +| 1 4 0 3/ 1 0 / 0 5 | تاريخ درج اميدنامه | +| کا رگزا ری آ رم ون بورس | مشاور پذيرش | +| اسا س ق يمت های جهان ی | نحوة تعيين قيمت پايه پس از پذيرش کاال در بورسبر | +| از ت ول يد سال يانه يا 47.5 0 0 تن | حداقل درصد ع رضه از توليد/ کل ف روش/ ف روش داخل یحداقل%50 | +| 5% آخ رين محموله قابل تح ويل | خطای مجاز تحويل | diff --git a/tests/pdf_snapshots/pdf/sources/table_mislabeled_as_picture.pdf.md b/tests/pdf_snapshots/pdf/sources/table_mislabeled_as_picture.pdf.md index f03fada8..53980fc4 100644 --- a/tests/pdf_snapshots/pdf/sources/table_mislabeled_as_picture.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/table_mislabeled_as_picture.pdf.md @@ -1,13 +1,11 @@ -| | represent people eligible for legal aid | +| | 209„ They work in parallel to State funded private practitioners who take assignments to represent people eligible for legal aid | | - | - | -| „ They coordinate appointments of private practitioners (ex officio, or panel appoint | | -| | ments) to legal aid cases | -| „ They supervise, coach or mentor private practitioners who take legal aid cases | | -| „ They conduct or organize training sessions for staff lawyers/paralegals | | -| „ They conduct or organize training sessions for all providers of legal aid, including | | -| | both staff and private lawyers/paralegals | -| „ Other (Please specify) _____________________________________________________ | | -| „ Not applicable, there is no institutional legal aid provider | | +| | They coordinate appointments of private practitioners (ex officio, or panel appoint ments) to legal aid cases | +| | They supervise, coach or mentor private practitioners who take legal aid cases | +| | They conduct or organize training sessions for staff lawyers/paralegals | +| | They conduct or organize training sessions for all providers of legal aid, including both staff and private lawyers/paralegals | +| | Other (Please specify) | +| | Not applicable, there is no institutional legal aid provider | - represent people eligible for legal aid - „ They coordinate appointments of private practitioners (ex officio, or panel appointments) to legal aid cases @@ -18,13 +16,8 @@ - „ Not applicable, there is no institutional legal aid provider - 23. If your country has an institutional legal aid provider (e.g. public defender), what is the maximum caseload per lawyer at one time? -| 23. If your country has an institutional legal aid provider (e.g. public defender), what is | | +| 23. | 209„ If your country has an institutional legal aid provider (e.g. public defender), what is the maximum caseload per lawyer at one time? _______ at the national (federal) level | | - | - | -| | the maximum caseload per lawyer at one time? | -| | _______ at the national (federal) level | -| | _______ at the regional (district) level | -| | _______ at the local (municipal) level | -| | „ There is no such limitation | - _______ at the national (federal) level - _______ at the regional (district) level @@ -36,13 +29,12 @@ - „ Yes, at the local (municipal) level - „ No -| 25. If your country has an institutional legal aid provider (e.g. public defender), does it | | +| 25. | 209„ If your country has an institutional legal aid provider (e.g. public defender), does it have specialized providers and/or units for representing child victims, child witness es or suspected and accused children? | | - | - | -| | have specialized providers and/or units for representing child victims, child witness | -| | es or suspected and accused children? | -| | „ Yes, at the national (federal) level | -| | „ Yes, at regional (district) level | -| | „ Yes, at the local (municipal) level | +| | Yes, at the national (federal) level | +| „ | Yes, at regional (district) level | +| „ | Yes, at the local (municipal) level | +| „ | No | - 25. If your country has an institutional legal aid provider (e.g. public defender), does it have specialized providers and/or units for representing child victims, child witnesses or suspected and accused children? - „ Yes, at the national (federal) level @@ -55,34 +47,31 @@ - „ Don't know - „ There are no university-based student law clinics -| 27. If your country allows legal aid services through university-based student law clinics, | | | -| - | - | - | -| | what type of legal aid services is a student authorized to undertake? | | -| | (Please select all that apply) | | -| | „ There is no limitation; they have the same authority as lawyers | | -| | „ They can represent people in administrative or civil law hearings | | -| | „ They can provide primary legal aid (legal advice) | | -| | „ They can prepare legal documents | | -| | „ They can represent people in court in civil and criminal matters | | -| | „ They have the same authority as lawyers in criminal cases of low to mid gravity | | -| | „ They can provide a full range of legal services in criminal cases regardless of gravity | | -| | „ They can conduct mediation | | -| | „ They are authorized to provide only those services that a faculty member or practic | | -| | | ing lawyer supervises | -| | „ Don’t know | | -| | „ Other (Please specify) _____________________________________________________ | | +| 27. | 209„ If your country allows legal aid services through university-based student law clinics, what type of legal aid services is a student authorized to undertake? (Please select all that apply) | +| - | - | +| „ | There is no limitation; they have the same authority as lawyers | +| „ | They can represent people in administrative or civil law hearings | +| „ | They can provide primary legal aid (legal advice) | +| „ | They can prepare legal documents | +| „ | They can represent people in court in civil and criminal matters | +| „ | They have the same authority as lawyers in criminal cases of low to mid gravity | +| „ | They can provide a full range of legal services in criminal cases regardless of gravity | +| „ | They can conduct mediation | +| „ | They are authorized to provide only those services that a faculty member or practic ing lawyer supervises | +| „ | Don’t know | +| „ | Other (Please specify) _____________________________________________________ | - 27. If your country allows legal aid services through university-based student law clinics, what type of legal aid services is a student authorized to undertake? (Please select all that apply) - 28. Are specialized legal aid services provided focusing on specific disadvantaged population groups? If yes, please indicate to whom these services are provided, and whether they are provided by State-funded legal aid, civil society organizations, or both. (Please select all that apply) -| | State funded legal aid CSOs | -| - | - | -| y Persons with disabilities * * | | -| y Children * * | | -| y Women * * | | -| y The elderly * * | | -| y Migrants * * | | -| y Refugees, asylum seekers, or stateless persons * * | | -| y Internally displaced persons * * | | +| | 209„ | State funded legal aid | CSOs | +| - | - | - | - | +| y | Persons with disabilities | * | * | +| y | Children | * | * | +| y | Women | * | * | +| y | The elderly | * | * | +| y | Migrants | * | * | +| y | Refugees, asylum seekers, or stateless persons | * | * | +| y | Internally displaced persons | * | * | From e2cc628e8f6f0fcf62a441688210676fdcf6a62c Mon Sep 17 00:00:00 2001 From: artiz Date: Mon, 29 Jun 2026 11:38:04 +0200 Subject: [PATCH 17/42] feat(pdf): RTL reading order + Arabic shaping + gap-aware cell join MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Improves right-to-left (Arabic) extraction and tightens table-PDF assembly: - RTL reading order: cells on a line read right→left, so an Arabic-majority region now sorts each line band by descending left edge (was always LTR). - RTL line wrapping: line splitting is now direction-aware — RTL wraps reset x rightward (`g.l > p.r`), so wrapped Arabic paragraphs no longer merge into one overlapping cell. - RTL word gaps: split at the right-to-left inter-word gap (`p.l - g.r`) rather than trusting pdfium's space glyphs, which it spuriously inserts inside Arabic words. - Arabic lam-alef: pdfium decomposes the إ/أ/آ-lam ligatures in visual order (`alef, lam`); swap mid-word back to logical `lam, alef`. Restricted to the hamza/madda variants — plain `alef+lam` is ambiguous (article vs ligature). - Arabic↔Latin boundary: insert the space pdfium runs together (`وPython` → `و Python`). - Gap-aware cell join (all scripts): same-band cells join without a space when abutting (so a word split across segments isn't broken), with a space across a real gap or line break. right_to_left_01 4→2, right_to_left_03 76→70, and the table papers tighten (2206 210→200, redp5110 300→298, 2203 313→309). The 3 exact PDFs are unchanged. No-op for non-Arabic text. Regenerated affected snapshot fixtures. Co-Authored-By: Claude Opus 4.8 (1M context) --- .../fleischwolf-pdf/examples/dump_regions.rs | 12 ++- crates/fleischwolf-pdf/src/assemble.rs | 102 ++++++++++++++++-- crates/fleischwolf-pdf/src/pdfium_backend.rs | 30 +++++- .../latex/sources/2305.03393/llncsdoc.pdf.md | 4 +- .../relative_expert_load_multi_7-12.pdf.md | 2 +- .../sources/32044009881525_select.tar.gz.md | 32 +++--- .../sources/odf_presentation_01.odp.pdf.md | 8 +- .../sources/odf_presentation_02.odp.pdf.md | 4 +- .../pdf/sources/2203.01017v2.pdf.md | 8 +- .../pdf/sources/2206.01062.pdf.md | 56 +++++----- .../pdf/sources/normal_4pages.pdf.md | 2 +- .../pdf/sources/redp5110_sampled.pdf.md | 18 ++-- .../pdf/sources/right_to_left_01.pdf.md | 4 +- .../pdf/sources/right_to_left_02.pdf.md | 6 +- .../pdf/sources/right_to_left_03.pdf.md | 18 ++-- .../scanned/sources/nemotron_multipage.pdf.md | 4 +- .../sources/ocr_test_rotated_270.pdf.md | 2 +- .../sources/ocr_test_rotated_90.pdf.md | 2 +- .../sample_with_rotation_mismatch.pdf.md | 14 +-- 19 files changed, 219 insertions(+), 109 deletions(-) diff --git a/crates/fleischwolf-pdf/examples/dump_regions.rs b/crates/fleischwolf-pdf/examples/dump_regions.rs index 9fc25878..45853e85 100644 --- a/crates/fleischwolf-pdf/examples/dump_regions.rs +++ b/crates/fleischwolf-pdf/examples/dump_regions.rs @@ -40,9 +40,15 @@ fn main() { tail ); } - // cells near the page bottom, to spot orphans below the last region - for c in page.cells.iter().filter(|c| c.t > 595.0 && c.t < 660.0) { - println!(" CELL t={:6.1} b={:6.1} | {}", c.t, c.b, c.text); + // raw line cells in extraction order (to inspect RTL ordering) + if std::env::var("DUMP_CELLS").is_ok() { + for (ci, c) in page.cells.iter().enumerate() { + let snip: String = c.text.chars().take(50).collect(); + println!( + " CELL[{ci}] t={:6.1} l={:6.1} r={:6.1} | {}", + c.t, c.l, c.r, snip + ); + } } } } diff --git a/crates/fleischwolf-pdf/src/assemble.rs b/crates/fleischwolf-pdf/src/assemble.rs index bc164002..f0a880cb 100644 --- a/crates/fleischwolf-pdf/src/assemble.rs +++ b/crates/fleischwolf-pdf/src/assemble.rs @@ -101,7 +101,8 @@ fn order_regions(items: &mut [T], page_w: f32, reg: impl Fn(&T) -> &Region) { /// `{ ahn }`, `Name 1 .`, `[ 9 ]`), and a geometric gap heuristic diverges from /// it more than a plain single-space join does. fn clean_text(text: &str) -> String { - text.replace("\u{2} ", "") + let out = text + .replace("\u{2} ", "") .replace("\u{ad} ", "") .replace(['\u{2}', '\u{ad}'], "") // any stray wrap hyphens not at a join .replace(['\u{2018}', '\u{2019}'], "'") // ‘ ’ → ' @@ -110,7 +111,60 @@ fn clean_text(text: &str) -> String { .replace('\u{2026}', "...") // … → ... .split_whitespace() .collect::>() - .join(" ") + .join(" "); + fix_arabic_lam_alef(&out) +} + +/// pdfium decomposes the Arabic lam-alef ligature (لا / لإ / لأ / لآ) into its +/// glyph constituents in *visual* order — `alef-variant, lam` — but docling keeps +/// logical order, `lam, alef-variant`. Swap a mid-word `alef-variant + lam` back +/// to `lam + alef-variant`. "Mid-word" (the previous char is an Arabic letter) +/// distinguishes the ligature from the definite article `ال` (word-initial +/// `alef + lam`), which must stay. No-op for non-Arabic text. +fn fix_arabic_lam_alef(s: &str) -> String { + let is_arabic_letter = |c: char| ('\u{0620}'..='\u{064A}').contains(&c); + let chars: Vec = s.chars().collect(); + if !chars.iter().any(|&c| is_arabic_letter(c)) { + return s.to_string(); // no-op for non-Arabic text + } + // Pass 1: swap mid-word `alef-variant + lam` → `lam + alef-variant`. Only the + // hamza/madda alef variants (إ أ آ) are safe: the definite article is always + // plain `ا + ل`, so plain `alef + lam` is ambiguous (a legitimate `فعالة` vs a + // reversed `لا` ligature look identical) — leaving plain alef alone avoids + // corrupting legitimate words. + let mut a: Vec = Vec::with_capacity(chars.len()); + let mut i = 0; + while i < chars.len() { + let c = chars[i]; + if matches!(c, '\u{0622}' | '\u{0623}' | '\u{0625}') + && chars.get(i + 1) == Some(&'\u{0644}') + && i > 0 + && is_arabic_letter(chars[i - 1]) + { + a.push('\u{0644}'); + a.push(c); + i += 2; + continue; + } + a.push(c); + i += 1; + } + // Pass 2: insert a space at Arabic↔Latin boundaries (bidi script switch) that + // pdfium runs together — docling separates the embedded Latin run (`وPython` + // → `و Python`). + let mut out: Vec = Vec::with_capacity(a.len()); + for (j, &c) in a.iter().enumerate() { + if j > 0 { + let p = a[j - 1]; + if (is_arabic_letter(p) && c.is_ascii_alphabetic()) + || (p.is_ascii_alphabetic() && is_arabic_letter(c)) + { + out.push(' '); + } + } + out.push(c); + } + out.into_iter().collect() } /// Cells assigned to a region (best container), in reading order, joined. @@ -123,19 +177,49 @@ fn region_text(region: &Region, cells: &[TextCell]) -> String { }) .collect(); // Quantize the top coordinate into ~line bands so cells on the same line - // sort left-to-right; this is a strict total order (a raw fuzzy comparator - // is not transitive and makes Rust's sort panic). + // sort in reading order; this is a strict total order (a raw fuzzy comparator + // is not transitive and makes Rust's sort panic). For a right-to-left + // (Arabic-majority) region, cells on a line read right→left, so sort the band + // by descending left edge. let band = inside .iter() .map(|c| (c.b - c.t).abs()) .fold(0.0f32, f32::max) .max(1.0); - inside.sort_by_key(|c| ((c.t / band).round() as i64, (c.l * 10.0) as i64)); - let joined = inside + let arabic = inside .iter() - .map(|c| c.text.trim()) - .collect::>() - .join(" "); + .flat_map(|c| c.text.chars()) + .filter(|&c| ('\u{0600}'..='\u{06FF}').contains(&c)) + .count(); + let latin = inside + .iter() + .flat_map(|c| c.text.chars()) + .filter(|c| c.is_ascii_alphabetic()) + .count(); + let rtl = arabic > latin; + inside.sort_by_key(|c| { + let x = (c.l * 10.0) as i64; + ((c.t / band).round() as i64, if rtl { -x } else { x }) + }); + // Join cells in reading order. Cells on different line-bands join with a + // space (line break). Cells on the same band join with a space only if there + // is a real horizontal gap between them — an RTL line is split into adjacent + // segments mid-word (`الت`|`ي` → `التي`), so abutting same-band cells must not + // get a spurious space. + let mut joined = String::new(); + let mut prev: Option<&&TextCell> = None; + for c in &inside { + if let Some(p) = prev { + let same_band = ((p.t / band).round() as i64) == ((c.t / band).round() as i64); + let h = (c.b - c.t).abs().max((p.b - p.t).abs()).max(1.0); + let gap = if rtl { p.l - c.r } else { c.l - p.r }; + if !same_band || gap > h * 0.25 { + joined.push(' '); + } + } + joined.push_str(c.text.trim()); + prev = Some(c); + } clean_text(&joined) } diff --git a/crates/fleischwolf-pdf/src/pdfium_backend.rs b/crates/fleischwolf-pdf/src/pdfium_backend.rs index e01f0d1c..a68090d1 100644 --- a/crates/fleischwolf-pdf/src/pdfium_backend.rs +++ b/crates/fleischwolf-pdf/src/pdfium_backend.rs @@ -318,7 +318,15 @@ fn lines_from_glyphs(gs: &[Glyph], page_h: f32, code: bool) -> Vec { // *large* drop (≥1.5× the line height — a skipped line, e.g. a centered // page-number footer below a short last word) is always a new line, // even without the x-reset. - new_line = (p.b - g.b > h * 0.5 && g.l < p.r) || (p.b - g.b > line_h.max(h) * 1.5); + // LTR wraps reset x leftward (`g.l < p.r`); RTL (Arabic) wraps reset + // rightward (the new line begins at the far right). A large drop + // (≥1.5× line height) is a new line regardless of x. + let x_reset = if is_arabic(g.ch) || is_arabic(p.ch) { + g.l > p.r + } else { + g.l < p.r + }; + new_line = (p.b - g.b > h * 0.5 && x_reset) || (p.b - g.b > line_h.max(h) * 1.5); // Don't split before closing punctuation, after opening punctuation, or // after a period that runs into a digit/lowercase letter — docling // keeps `engines,` / `[37` / `i.e.` / `98.5` together even across a @@ -332,6 +340,12 @@ fn lines_from_glyphs(gs: &[Glyph], page_h: f32, code: bool) -> Vec { let word_gap = line_h.max(h) * 0.25; new_word = if code { new_line || pending_space + } else if is_arabic(g.ch) || is_arabic(p.ch) { + // RTL runs right-to-left, so the inter-word gap is `p.l - g.r`. A + // real word space has a gap; pdfium also emits spurious zero-gap + // space glyphs inside words (`التي`), so require the gap rather + // than trusting a bare space glyph. + new_line || (p.l - g.r > word_gap && !glued) } else { new_line || ((pending_space || g.l - p.r > word_gap) && !glued) }; @@ -389,7 +403,15 @@ fn words_from_glyphs(gs: &[Glyph], page_h: f32) -> Vec { let mut new_line = false; let mut new_word = false; if let Some(p) = prev { - new_line = (p.b - g.b > h * 0.5 && g.l < p.r) || (p.b - g.b > line_h.max(h) * 1.5); + // LTR wraps reset x leftward (`g.l < p.r`); RTL (Arabic) wraps reset + // rightward (the new line begins at the far right). A large drop + // (≥1.5× line height) is a new line regardless of x. + let x_reset = if is_arabic(g.ch) || is_arabic(p.ch) { + g.l > p.r + } else { + g.l < p.r + }; + new_line = (p.b - g.b > h * 0.5 && x_reset) || (p.b - g.b > line_h.max(h) * 1.5); // No digit-digit glue here (unlike the prose grouping): table cells in // adjacent columns are numeric and a column gap must still split them // (`0.965` `0.934`, not `0.9650.934`). Intra-number digits have no gap @@ -436,6 +458,10 @@ fn words_from_glyphs(gs: &[Glyph], page_h: f32) -> Vec { cells } +fn is_arabic(c: char) -> bool { + ('\u{0600}'..='\u{06FF}').contains(&c) +} + fn is_close_punct(c: char) -> bool { matches!( c, diff --git a/tests/pdf_snapshots/latex/sources/2305.03393/llncsdoc.pdf.md b/tests/pdf_snapshots/latex/sources/2305.03393/llncsdoc.pdf.md index 5ac17a97..997814e9 100644 --- a/tests/pdf_snapshots/latex/sources/2305.03393/llncsdoc.pdf.md +++ b/tests/pdf_snapshots/latex/sources/2305.03393/llncsdoc.pdf.md @@ -159,7 +159,7 @@ From a technical point of view, the llncs document class does not require any sp The llncs document class supports some additional special characters: ``` -\grole yields > < \getsto yields ← → \lid yields < = \gid yields > = +\grole yields >< \getsto yields ← → \lid yields <= \gid yields >= ``` If you need blackboard bold characters, i.e. for sets of numbers, please load the related AMS-TEXfonts. If for some reason this is not possible you can also use the following commands from the llncs class: @@ -192,7 +192,7 @@ cas e (env.) Other theorem-like environments render the text in roman, while the example (env.) ``` -property(env.) question(env.) exercise(env.) solution(env.)heading is bold as well: problem(env.) note(env.) \begin{case} \end{case} \begin{conjecture} \end{conjecture} \begin{example} \end{example} \begin{exercise} \end{exercise} \begin{note} \end{note} \begin{problem} \end{problem} remark(env.) \begin{property} \end{property} \begin{question} \end{question} \begin{remark} \end{remark} \begin{solution} \end{solution} +property(env.)question(env.)exercise(env.)solution(env.)heading is bold as well:problem(env.)note(env.) \begin{case} \end{case}\begin{conjecture} \end{conjecture}\begin{example} \end{example}\begin{exercise} \end{exercise}\begin{note} \end{note}\begin{problem} \end{problem} remark(env.) \begin{property} \end{property}\begin{question} \end{question}\begin{remark} \end{remark}\begin{solution} \end{solution} ``` claim (env.) Finally, there are also two unnumbered environments that have the run-in headproof (env.) ing in italics and the text in upright roman. diff --git a/tests/pdf_snapshots/latex/sources/2412.19437/figures/relative_expert_load_multi_7-12.pdf.md b/tests/pdf_snapshots/latex/sources/2412.19437/figures/relative_expert_load_multi_7-12.pdf.md index ff1c8ef0..bd13cbc0 100644 --- a/tests/pdf_snapshots/latex/sources/2412.19437/figures/relative_expert_load_multi_7-12.pdf.md +++ b/tests/pdf_snapshots/latex/sources/2412.19437/figures/relative_expert_load_multi_7-12.pdf.md @@ -1,3 +1,3 @@ -DM Mathematics Aux-Loss-Based Layer 12 12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758596061626364Wikipedia (en) Github +DM Mathematics Aux-Loss-Based Layer 1212345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758596061626364Wikipedia (en)Github diff --git a/tests/pdf_snapshots/mets_gbs/sources/32044009881525_select.tar.gz.md b/tests/pdf_snapshots/mets_gbs/sources/32044009881525_select.tar.gz.md index 3b3780f4..94626d8f 100644 --- a/tests/pdf_snapshots/mets_gbs/sources/32044009881525_select.tar.gz.md +++ b/tests/pdf_snapshots/mets_gbs/sources/32044009881525_select.tar.gz.md @@ -1,16 +1,16 @@ -recently become prevalent that he who speaks of military power is a " militarist . " This , how- reverse assertion ever , is as great a fallacy as the that he who talks of nothing but peace is a 66 pacifist . " +recently become prevalent that he who speaks of military power is a "militarist." This, how- reverse assertion ever, is as great a fallacy as the that he who talks of nothing but peace is a 66 pacifist." -Truth , even bitter truth , is better than the most high - minded fallacy . +Truth, even bitter truth, is better than the most high-minded fallacy. -The author has visited Japan , Siberia , China , the Philippines , the Malay States , and Hawaii in 1919 and 1920 , and his personal impressions and investigations form the basis of the present book . The list of works which he has perused in the course of his investigation of the problem of the Pacific is hereto appended . +The author has visited Japan, Siberia, China, the Philippines, the Malay States, and Hawaii in 1919 and 1920, and his personal impressions and investigations form the basis of the present book. The list of works which he has perused in the course of his investigation of the problem of the Pacific is hereto appended. -The author wishes to acknowledge his debt to Admiral A. D. Bubnov , who has contributed Chapters VII - X . Admiral Bubnov took part in the Russo - Japanese War , was Professor of the Naval Staff College at Petrograd , and Chief of the Naval Section of the Staff of the Supreme Commander - in - Chief in the Great War . The Admiral is an authoritative student of the questions of naval strategy discussed in the chapters that belong to his pen . +The author wishes to acknowledge his debt to Admiral A. D. Bubnov, who has contributed Chapters VII- X. Admiral Bubnov took part in the Russo-Japanese War, was Professor ofthe Naval Staff College at Petrograd, and Chief of the Naval Section of the Staff of the Supreme Commander-in-Chief in the Great War. The Admiral is an authoritative student of the questions of naval strategy discussed in the chapters that belong to his pen. -The author also has to thank Mr. C. Nabokoff , the late Russian Chargé d'Affaires in London , book . for undertaking the translation of his +The author also has to thank Mr. C. Nabokoff, the late Russian Chargé d'Affaires in London, book. for undertaking the translation of his ## 62 THE PROBLEM OF THE PACIFIC -copper mines with up - to - date technical equip- ment in various provinces of Central China ¹ : - +copper mines with up-to-date technical equip- ment in various provinces of Central China ¹:- | | | | | | - | - | - | - | @@ -26,15 +26,15 @@ copper mines with up - to - date technical equip- ment in various provinces of C | | | | | | | | | | -It has been mentioned in one of the preceding chapters that Japanese industries needed iron . The desire to obtain the necessary supplies of iron is thus perfectly legitimate . Japan's endea- vour to acquire concessions for the production of iron and coal is not , therefore , a proof of the Imperialism of her policy . But , as the French saying goes , Le ton fait la chanson . Japan is trying to get hold of the entire iron industry of China . She is doing so by the veiled seizure of political and administrative control of the respective provinces of China . To Europe and America this is presented under the guise of the nebulous formula of 66 special interests in the regions of China adjacent to Japan . " Man- churia and Shantung are first in that list . When Japan succeeds , she will have deprived China +It has been mentioned in one of the preceding chapters that Japanese industries needed iron. The desire to obtain the necessary supplies of iron is thus perfectly legitimate. Japan's endea- vour to acquire concessions for the production of iron and coal is not, therefore, a proof of the Imperialism of her policy. But, as the French saying goes, Le ton fait la chanson. Japan is trying to get hold of the entire iron industry of China. She is doing so by the veiled seizure of political and administrative control of the respective provinces of China. To Europe and America this is presented under the guise of the nebulous formula of 66special interests in the regions of China adjacent to Japan." Man- churia and Shantung are first in that list. When Japan succeeds, she will have deprived China -1 These data relate to 1915 . +1 These data relate to 1915. ## 252 THE PROBLEM OF THE PACIFIC -France may lay down new tonnage in the years 1927 , 1929 , and 1931 , as provided in Part 3 , Section II . +France may lay down new tonnage in the years 1927, 1929, and 1931, as provided in Part 3, Section II. -Ships which may be retained by Italy . +Ships which may be retained by Italy. | | | | | | - | - | - | - | @@ -50,7 +50,7 @@ Ships which may be retained by Italy . | | | | | | | | | | -Italy may lay down new tonnage in the years 1927 , 1929 and 1931 , as provided in Part 3 , Section II . +Italy may lay down new tonnage in the years 1927, 1929 and 1931, as provided in Part 3, Section II. | | | | | | | | | - | - | - | - | - | - | - | @@ -66,8 +66,8 @@ Italy may lay down new tonnage in the years 1927 , 1929 and 1931 , as provided i | | | | | | | | | | | | | | | | -- Part 2. - RULES FOR SCRAPPING VESSELs of War . The following rules shall be observed for the scrapping of vessels of war which are to be disposed of in accordance with Articles II and III . -- I. A vessel to be scrapped must be placed in such condition that it cannot be put to combative use . -- II . This result must be finally effected in any one of the following ways : -- ( a ) Permanent sinking of the vessel ; -- ( b ) Breaking the vessel up . This shall always involve the destruction or removal of all machinery , boilers and armour , and all deck , side and bottom plating ; +- Part 2.-RULES FOR SCRAPPING VESSELs of War. The following rules shall be observed for the scrapping of vessels of war which are to be disposed of in accordance with Articles II and III. +- I. A vessel to be scrapped must be placed in such condition that it cannot be put to combative use. +- II. This result must be finally effected in any one of the following ways: +- (a) Permanent sinking of the vessel ; +- (b) Breaking the vessel up. This shall always involve the destruction or removal of all machinery, boilers and armour, and all deck, side and bottom plating; diff --git a/tests/pdf_snapshots/odf/sources/odf_presentation_01.odp.pdf.md b/tests/pdf_snapshots/odf/sources/odf_presentation_01.odp.pdf.md index c0500079..d9d106ac 100644 --- a/tests/pdf_snapshots/odf/sources/odf_presentation_01.odp.pdf.md +++ b/tests/pdf_snapshots/odf/sources/odf_presentation_01.odp.pdf.md @@ -1,15 +1,15 @@ -## e e a e g r s s s ● e 1 +## e e a e gr ss s ● e 1 ``` -eW e● a s e F l ● e T e● ● u e T r F e● ● ● s e F C● ● t C A 2● ● ● C ● a b e T l a s F l ● u r a s F l ● a s l a s e u e l T r ● ● B ● ● a s s l S d l i +eWe●aseFl● eTe●● ueTrFe●●● seFC●● tCA2●●● C● abeTl asFl● ur asFl● asl aseuelTr●● B●● assl Sdli ``` - S -A• :• S e t I m B e t I m o e o m i n f +A•:•S etImB etIm oeominf -- o t I t I m +- o tI tI m - e e e m m - n f o diff --git a/tests/pdf_snapshots/odf/sources/odf_presentation_02.odp.pdf.md b/tests/pdf_snapshots/odf/sources/odf_presentation_02.odp.pdf.md index 1b7c30a9..be0df7dd 100644 --- a/tests/pdf_snapshots/odf/sources/odf_presentation_02.odp.pdf.md +++ b/tests/pdf_snapshots/odf/sources/odf_presentation_02.odp.pdf.md @@ -1,8 +1,8 @@ -m d e b r a m s m i a u t a e i q l i u s , a p m m v c s v u t e a c e e u s a a f t v i v a l l u a , r t n r i u u e m u s c c n u d a u s c p l a r r . . o t c u e m p s b i u M M r m c o b i e u a t s e a a c s u l c r u m s e t m u r e o n a c t i . s . o . p s i c e d n u M s N d i e i n i m n s a o d u o . r d a e u n u a a l l V o l e s n s i r l e +m d e br a m s m i a ut a e i qli us ,a p m m v c s v u te a c e e us a af tv iv a ll u a ,r tn ri u u e m us c c n u da us c pl arr . . ot cu e m p s bi u M M r m c o bi e u at se a a c s ul cr u m s et m ur e on a ct i. s . o . p si ce d n u M s N di e i n im n s a od u o. r d a e u n u a all V ol e s n si r l e -m n m e a e t b a r . h n a i x p u e n t . o l M c i d m , o u l c N e r o u t i f v i t r m m u m r a l . I e a t t t i n b i v t n s l i e o e n o l c q i t i e , r q +m n m e a e t b ar . h n ai x p u en t. ol M ci d m , o ul c N er o u ti f vi tr m m u m r al .I e at tti n bi v tn sli e o en ol c q i ti e , r q diff --git a/tests/pdf_snapshots/pdf/sources/2203.01017v2.pdf.md b/tests/pdf_snapshots/pdf/sources/2203.01017v2.pdf.md index 251f7e3d..33af4239 100644 --- a/tests/pdf_snapshots/pdf/sources/2203.01017v2.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/2203.01017v2.pdf.md @@ -6,7 +6,7 @@ Ahmed Nassar, Nikolaos Livathinos, Maksym Lysak, Peter Staar IBM Research ## Abstract -Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper , we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First , we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second , we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables. +Tables organize valuable content in a concise and compact representation. This content is extremely valuable for systems such as search engines, Knowledge Graph's, etc, since they enhance their predictive capabilities. Unfortunately, tables come in a large variety of shapes and sizes. Furthermore, they can have complex column/row-header configurations, multiline rows, different variety of separation lines, missing entries, etc. As such, the correct identification of the table-structure from an image is a nontrivial task. In this paper, we present a new table-structure identification model. The latter improves the latest end-toend deep learning model (i.e. encoder-dual-decoder from PubTabNet) in two significant ways. First, we introduce a new object detection decoder for table-cells. In this way, we can obtain the content of the table-cells from programmatic PDF's directly from the PDF source and avoid the training of the custom OCR decoders. This architectural change leads to more accurate table-content extraction and allows us to tackle non-english tables. Second, we replace the LSTM decoders with transformer based decoders. This upgrade improves significantly the previous state-of-the-art tree-editing-distance-score (TEDS) from 91% to 98.5% on simple tables and from 88.7% to 95% on complex tables. ## 1. Introduction @@ -117,7 +117,7 @@ Figure 3: TableFormer takes in an image of the PDF and creates bounding box and -Figure 4: Given an input image of a table, the Encoder produces fixed-length features that represent the input image. The features are then passed to both the Structure Decoder and Cell BBox Decoder. During training, the Structure Decoder receives'tokenized tags' of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. Simultaneously, the Cell BBox Decoder selects features referring to the data cells ('','<') and passes them through an attention network, an MLP , and a +Figure 4: Given an input image of a table, the Encoder produces fixed-length features that represent the input image. The features are then passed to both the Structure Decoder and Cell BBox Decoder. During training, the Structure Decoder receives'tokenized tags' of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. Simultaneously, the Cell BBox Decoder selects features referring to the data cells ('','<') and passes them through an attention network, an MLP, and a @@ -297,7 +297,7 @@ Computer Vision and Pattern Recognition, pages 658-666, 2019. 6 - [27] Sebastian Schreiber, Stefan Agne, Ivo Wolf, Andreas Dengel, and Sheraz Ahmed. Deepdesrt: Deep learning for detection and structure recognition of tables in document images. In 201714th IAPR international conference on document analysis and recognition (ICDAR), volume 1, pages 1162-1167. IEEE, 2017. 3 - [28] Faisal Shafait and Ray Smith. Table detection in heterogeneous documents. In Proceedings of the 9th IAPR International Workshop on Document Analysis Systems, pages 65- 72, 2010. 2 - [29] Shoaib Ahmed Siddiqui, Imran Ali Fateh, Syed Tahseen Raza Rizvi, Andreas Dengel, and Sheraz Ahmed. Deeptabstr: Deep learning based table structure recognition. In 2019 International Conference on Document Analysis and Recognition (ICDAR), pages 1403-1409. IEEE, 2019. 3 -- [30] Peter W J Staar, Michele Dolfi, Christoph Auer, and Costas Bekas. Corpus conversion service: A machine learning platform to ingest documents at scale. In Proceedings of the 24th ACM SIGKDD, KDD' 18, pages 774-782, New York, NY , USA, 2018. ACM. 1 +- [30] Peter W J Staar, Michele Dolfi, Christoph Auer, and Costas Bekas. Corpus conversion service: A machine learning platform to ingest documents at scale. In Proceedings of the 24th ACM SIGKDD, KDD' 18, pages 774-782, New York, NY, USA, 2018. ACM. 1 - [31] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. Attention is all you need. In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, editors, Advances in Neural Information Processing Systems 30, pages 5998-6008. Curran Associates, Inc., 2017. 5 - [32] Oriol Vinyals, Alexander Toshev, Samy Bengio, and Dumitru Erhan. Show and tell: A neural image caption generator. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015. 2 - [33] Wenyuan Xue, Qingyong Li, and Dacheng Tao. Res2tim: reconstruct syntactic structures from table images. In 2019 International Conference on Document Analysis and Recognition (ICDAR), pages 749-755. IEEE, 2019. 3 @@ -382,7 +382,7 @@ phan cell. 9f. Otherwise create a new structural cell and match it wit the orphan cell. -Aditional images with examples of TableFormer predictions and post- processing can be found below. +Aditional images with examples of TableFormer predictions and post-processing can be found below. diff --git a/tests/pdf_snapshots/pdf/sources/2206.01062.pdf.md b/tests/pdf_snapshots/pdf/sources/2206.01062.pdf.md index d507eaee..bd552496 100644 --- a/tests/pdf_snapshots/pdf/sources/2206.01062.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/2206.01062.pdf.md @@ -4,7 +4,7 @@ Ahmed S. Nassar IBM Research Rueschlikon, Switzerland ## ABSTRACT -Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper , we present DocLayNet , a new , publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis. +Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet, a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis. ## CCS CONCEPTS @@ -12,7 +12,7 @@ Accurate document layout analysis is a key requirement for highquality PDF docum Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). -KDD'22 , August 14-18 , 2022 , Washington, DC , USA +KDD'22, August 14-18, 2022, Washington, DC, USA © 2022 Copyright held by the owner/author(s). @@ -44,7 +44,7 @@ PDF document conversion, layout segmentation, object-detection, data set, Machin ## ACM Reference Format: -Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar , and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'22), August 14-18 , 2022 , Washington, DC , USA. ACM, New York, NY , USA, 9 pages. https://doi.org/10.1145/ +Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'22), August 14-18, 2022, Washington, DC, USA. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/ KDD'22, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar @@ -52,9 +52,9 @@ KDD'22, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer Despite the substantial improvements achieved with machine-learning (ML) approaches and deep neural networks in recent years, document conversion remains a challenging problem, as demonstrated by the numerous public competitions held on this topic [1-4]. The challenge originates from the huge variability in PDF documents regarding layout, language and formats (scanned, programmatic or a combination of both). Engineering a single ML model that can be applied on all types of documents and provides high-quality layout segmentation remains to this day extremely challenging [5]. To highlight the variability in document layouts, we show a few example documents from the DocLayNet dataset in Figure 1. -A key problem in the process of document conversion is to understand the structure of a single document page, i.e. which segments of text should be grouped together in a unit. To train models for this task, there are currently two large datasets available to the community, PubLayNet [6] and DocBank [7]. They were introduced in 2019 and 2020 respectively and significantly accelerated the implementation of layout detection and segmentation models due to their sizes of 300K and 500K ground-truth pages. These sizes were achieved by leveraging an automation approach. The benefit of automated ground-truth generation is obvious: one can generate large ground-truth datasets at virtually no cost. However , the automation introduces a constraint on the variability in the dataset, because corresponding structured source data must be available. PubLayNet and DocBank were both generated from scientific document repositories (PubMed and arXiv), which provide XML or LATEX sources. Those scientific documents present a limited variability in their layouts, because they are typeset in uniform templates provided by the publishers. Obviously, documents such as technical manuals, annual company reports, legal text, government tenders, etc. have very different and partially unique layouts. As a consequence, the layout predictions obtained from models trained on PubLayNet or DocBank is very reasonable when applied on scientific documents. However , for more artistic or free-style layouts, we see sub-par prediction quality from these models, which we demonstrate in Section 5. +A key problem in the process of document conversion is to understand the structure of a single document page, i.e. which segments of text should be grouped together in a unit. To train models for this task, there are currently two large datasets available to the community, PubLayNet [6] and DocBank [7]. They were introduced in 2019 and 2020 respectively and significantly accelerated the implementation of layout detection and segmentation models due to their sizes of 300K and 500K ground-truth pages. These sizes were achieved by leveraging an automation approach. The benefit of automated ground-truth generation is obvious: one can generate large ground-truth datasets at virtually no cost. However, the automation introduces a constraint on the variability in the dataset, because corresponding structured source data must be available. PubLayNet and DocBank were both generated from scientific document repositories (PubMed and arXiv), which provide XML or LATEX sources. Those scientific documents present a limited variability in their layouts, because they are typeset in uniform templates provided by the publishers. Obviously, documents such as technical manuals, annual company reports, legal text, government tenders, etc. have very different and partially unique layouts. As a consequence, the layout predictions obtained from models trained on PubLayNet or DocBank is very reasonable when applied on scientific documents. However, for more artistic or free-style layouts, we see sub-par prediction quality from these models, which we demonstrate in Section 5. -In this paper , we present the DocLayNet dataset. It provides pageby-page layout annotation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique document pages, of which a fraction carry double- or triple-annotations. DocLayNet is similar in spirit to PubLayNet and DocBank and will likewise be made available to the public1 in order to stimulate the document-layout analysis community. It distinguishes itself in the following aspects: +In this paper, we present the DocLayNet dataset. It provides pageby-page layout annotation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique document pages, of which a fraction carry double- or triple-annotations. DocLayNet is similar in spirit to PubLayNet and DocBank and will likewise be made available to the public1 in order to stimulate the document-layout analysis community. It distinguishes itself in the following aspects: - (1) Human Annotation: In contrast to PubLayNet and DocBank, we relied on human annotation instead of automation approaches to generate the data set. - (2) Large Layout Variability: We include diverse and complex layouts from a large variety of public sources. @@ -77,7 +77,7 @@ Lately, new types of ML models for document-layout analysis have emerged in the ## 3 THE DOCLAYNET DATASET -DocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of human annotations, and 1591 carry three. This amounts to 91104 total annotation instances. The annotations provide layout information in the shape of labeled, rectangular boundingboxes. We define 11 distinct labels for layout features, namely Caption, Footnote, Formula, List-item, Page-footer , Page-header , Picture, Section-header , Table, Text , and Title. Our reasoning for picking this particular label set is detailed in Section 4. +DocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of human annotations, and 1591 carry three. This amounts to 91104 total annotation instances. The annotations provide layout information in the shape of labeled, rectangular boundingboxes. We define 11 distinct labels for layout features, namely Caption, Footnote, Formula, List-item, Page-footer, Page-header, Picture, Section-header, Table, Text, and Title. Our reasoning for picking this particular label set is detailed in Section 4. In addition to open intellectual property constraints for the source documents, we required that the documents in DocLayNet adhere to a few conditions. Firstly, we kept scanned documents @@ -89,7 +89,7 @@ to a minimum, since they introduce difficulties in annotation (see Section 4). A The pages in DocLayNet can be grouped into six distinct categories, namely Financial Reports, Manuals, Scientific Articles, Laws & Regulations, Patents and Government Tenders. Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports2 which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories (Financial Reports and Manuals) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes. -We did not control the document selection with regard to language. The vast majority of documents contained in DocLayNet (close to 95%) are published in English language. However , DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%). While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features. +We did not control the document selection with regard to language. The vast majority of documents contained in DocLayNet (close to 95%) are published in English language. However, DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%). While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features. To ensure that future benchmarks in the document-layout analysis community can be easily compared, we have split up DocLayNet into pre-defined train-, test- and validation-sets. In this way, we can avoid spurious variations in the evaluation scores due to random splitting in train-, test- and validation-sets. We also ensured that less frequent labels are represented in train and test sets in equal proportions. @@ -129,9 +129,9 @@ company websites as well as data directory services for financial reports and pa Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains. -Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption, Footnote, Formula, List-item, Pagefooter , Page-header , Picture, Section-header , Table, Text , and Title. Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation, as seen in DocBank, are often only distinguishable by discriminating on +Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption, Footnote, Formula, List-item, Pagefooter, Page-header, Picture, Section-header, Table, Text, and Title. Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation, as seen in DocBank, are often only distinguishable by discriminating on -we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four , a group of 40 dedicated annotators were assembled and supervised. +we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised. Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went @@ -139,7 +139,7 @@ Phase 1: Data selection and preparation. Our inclusion criteria for documents we the textual content of an element, which goes beyond visual layout recognition, in particular outside the Scientific Articles category. -At first sight, the task of visual document-layout interpretation appears intuitive enough to obtain plausible annotations in most cases. However , during early trial-runs in the core team, we observed many cases in which annotators use different annotation styles, especially for documents with challenging layouts. For example, if a figure is presented with subfigures, one annotator might draw a single figure bounding-box, while another might annotate each subfigure separately. The same applies for lists, where one might annotate all list items in one block or each list item separately. In essence, we observed that challenging layouts would be annotated in different but plausible ways. To illustrate this, we show in Figure 4 multiple examples of plausible but inconsistent annotations on the same pages. +At first sight, the task of visual document-layout interpretation appears intuitive enough to obtain plausible annotations in most cases. However, during early trial-runs in the core team, we observed many cases in which annotators use different annotation styles, especially for documents with challenging layouts. For example, if a figure is presented with subfigures, one annotator might draw a single figure bounding-box, while another might annotate each subfigure separately. The same applies for lists, where one might annotate all list items in one block or each list item separately. In essence, we observed that challenging layouts would be annotated in different but plausible ways. To illustrate this, we show in Figure 4 multiple examples of plausible but inconsistent annotations on the same pages. Obviously, this inconsistency in annotations is not desirable for datasets which are intended to be used for model training. To minimise these inconsistencies, we created a detailed annotation guideline. While perfect consistency across 40 annotation staff members is clearly not possible to achieve, we saw a huge improvement in annotation consistency after the introduction of our annotation guideline. A few selected, non-trivial highlights of the guideline are: @@ -148,7 +148,7 @@ Obviously, this inconsistency in annotations is not desirable for datasets which - (3) For every Caption, there must be exactly one corresponding Picture or Table. - (4) Connected sub-pictures are grouped together in one Picture object. - (5) Formula numbers are included in a Formula object. -- (6) Emphasised text (e.g. in italic or bold) at the beginning of a paragraph is not considered a Section-header , unless it appears exclusively on its own line. +- (6) Emphasised text (e.g. in italic or bold) at the beginning of a paragraph is not considered a Section-header, unless it appears exclusively on its own line. The complete annotation guideline is over 100 pages long and a detailed description is obviously out of scope for this paper. Nevertheless, it will be made publicly available alongside with DocLayNet for future reference. @@ -180,7 +180,7 @@ Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on D | Title | 60-72 | 76.7 | 80.4 | 79.9 | 82.7 | | All | 82-83 | 72.4 | 73.5 | 73.4 | 76.8 | -to avoid this at any cost in order to have clear , unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture. For the latter , we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity. +to avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture. For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity. ## 5 EXPERIMENTS @@ -196,7 +196,7 @@ In this section, we will present several aspects related to the performance of o ## Baselines for Object Detection -In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training and evaluation were performed on RGB images with dimensions of 1025 × 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low , but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text , Table and Picture. This is not entirely surprising, as Text , Table and Picture are abundant and the most visually distinctive in a document. +In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training and evaluation were performed on RGB images with dimensions of 1025 × 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text, Table and Picture. This is not entirely surprising, as Text, Table and Picture are abundant and the most visually distinctive in a document. Table 3: Performance of a Mask R-CNN R50 network in mAP@0.5-0.95 scores trained on DocLayNet with different class label sets. The reduced label sets were obtained by either down-mapping or dropping labels. @@ -217,7 +217,7 @@ Table 3: Performance of a Mask R-CNN R50 network in mAP@0.5-0.95 scores trained ## Learning Curve -One of the fundamental questions related to any dataset is if it is "large enough". To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar , depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather , it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles. +One of the fundamental questions related to any dataset is if it is "large enough". To answer this question for DocLayNet, we performed a data ablation study in which we evaluated a Mask R-CNN model trained on increasing fractions of the DocLayNet dataset. As can be seen in Figure 5, the mAP score rises sharply in the beginning and eventually levels out. To estimate the error-bar on the metrics, we ran the training five times on the entire data-set. This resulted in a 1% error-bar, depicted by the shaded area in Figure 5. In the inset of Figure 5, we show the exact same data-points, but with a logarithmic scale on the x-axis. As is expected, the mAP score increases linearly as a function of the data-size in the inset. The curve ultimately flattens out between the 80% and 100% mark, with the 80% mark falling within the error-bars of the 100% mark. This provides a good indication that the model would not improve significantly by yet increasing the data size. Rather, it would probably benefit more from improved data consistency (as discussed in Section 3), data augmentation methods [23], or the addition of more document categories and styles. ## Impact of Class Labels @@ -249,7 +249,7 @@ Many documents in DocLayNet have a unique styling. In order to avoid overfitting ## Dataset Comparison -Throughout this paper , we claim that DocLayNet's wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture, +Throughout this paper, we claim that DocLayNet's wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture, KDD'22, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar @@ -272,7 +272,7 @@ Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network acros | | Text | 77 | - | 84 | | | total | 59 | 47 | 78 | -Section-header , Table and Text. Before training, we either mapped or excluded DocLayNet's other labels as specified in table 3, and also PubLayNet's List to Text. Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text. +Section-header, Table and Text. Before training, we either mapped or excluded DocLayNet's other labels as specified in table 3, and also PubLayNet's List to Text. Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text. For comparison of DocBank with DocLayNet, we trained only on Picture and Table clusters of each dataset. We had to exclude Text because successive paragraphs are often grouped together into a single object in DocBank. This paragraph grouping is incompatible with the individual paragraphs of DocLayNet. As can be seen in Table 5, DocLayNet trained models yield better performance compared to the previous datasets. It is noteworthy that the models trained on PubLayNet and DocBank perform very well on their own test set, but have a much lower performance on the foreign datasets. While this also applies to DocLayNet, the difference is far less pronounced. Thus we conclude that DocLayNet trained models are overall more robust and will produce better results for challenging, unseen layouts. @@ -280,7 +280,7 @@ For comparison of DocBank with DocLayNet, we trained only on Picture and Table c To conclude this section, we illustrate the quality of layout predictions one can expect from DocLayNet-trained models by providing a selection of examples without any further post-processing applied. Figure 6 shows selected layout predictions on pages from the test-set of DocLayNet. Results look decent in general across document categories, however one can also observe mistakes such as overlapping clusters of different classes, or entirely missing boxes -In this paper , we presented the DocLayNet dataset. It provides the document conversion and layout analysis research community a new and challenging dataset to improve and fine-tune novel ML methods on. In contrast to many other datasets, DocLayNet was created by human annotation in order to obtain reliable layout ground-truth on a wide variety of publication- and typesettingstyles. Including a large proportion of documents outside the scientific publishing domain adds significant value in this respect. +In this paper, we presented the DocLayNet dataset. It provides the document conversion and layout analysis research community a new and challenging dataset to improve and fine-tune novel ML methods on. In contrast to many other datasets, DocLayNet was created by human annotation in order to obtain reliable layout ground-truth on a wide variety of publication- and typesettingstyles. Including a large proportion of documents outside the scientific publishing domain adds significant value in this respect. From the dataset, we have derived on the one hand reference metrics for human performance on document-layout annotation (through double and triple annotations) and on the other hand evaluated the baseline performance of commonly used object detection methods. We also illustrated the impact of various dataset-related aspects on model performance through data-ablation experiments, both from a size and class-label perspective. Last but not least, we compared the accuracy of models trained on other public datasets and showed that DocLayNet trained models are more robust. @@ -289,18 +289,18 @@ To date, there is still a significant gap between human and ML accuracy on the l ## REFERENCES - [1] Max Göbel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. Icdar 2013 table competition. In 201312th International Conference on Document Analysis and Recognition, pages 1449-1453, 2013. -- [2] Christian Clausner , Apostolos Antonacopoulos, and Stefan Pletschacher. Icdar2017 competition on recognition of documents with complex layouts - rdcl2017. In 201714th IAPR International Conference on Document Analysis and Recognition (ICDAR), volume 01, pages 1404-1410, 2017. -- [3] Hervé Déjean, Jean-Luc Meunier , Liangcai Gao, Yilun Huang, Yu Fang, Florian Kleber , and Eva-Maria Lang. 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IEEE Computer Society, Oct 2017. +- [13] Glenn Jocher, Alex Stoken, Ayush Chaurasia, Jirka Borovec, NanoCode012, TaoXie, Yonghye Kwon, Kalen Michael, Liu Changyu, Jiacong Fang, Abhiram V, Laughing, tkianai, yxNONG, Piotr Skalski, Adam Hogan, Jebastin Nadar, imyhxy, Lorenzo Mammana, Alex Wang, Cristi Fati, Diego Montes, Jan Hajek, Laurentiu Figure 6: Example layout predictions on selected pages from the DocLayNet test-set. (A, D) exhibit favourable results on coloured backgrounds. (B, C) show accurate list-item and paragraph differentiation despite densely-spaced lines. (E) demonstrates good table and figure distinction. (F) shows predictions on a Chinese patent with multiple overlaps, label confusion and missing boxes. @@ -310,13 +310,13 @@ Text Caption List-Item Formula Table Picture Section-Header Page-Header Page-Foo Diaconu, Mai Thanh Minh, Marc, albinxavi, fatih, oleg, and wanghao yang. ultralytics/yolov5: v6.0 - yolov5n nano models, roboflow integration, tensorflow export, opencv dnn support, October 2021. -- [14] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier , Alexander Kirillov , and Sergey Zagoruyko. End-to-end object detection with transformers. CoRR, abs/2005.12872, 2020. +- [14] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier , Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. CoRR, abs/2005.12872, 2020. - [15] Mingxing Tan, Ruoming Pang, and Quoc V. Le. Efficientdet: Scalable and efficient object detection. CoRR, abs/1911.09070, 2019. -- [16] Tsung-Yi Lin, Michael Maire, Serge J. Belongie, Lubomir D. Bourdev , Ross B. Girshick, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár , and C. Lawrence Zitnick. Microsoft COCO: common objects in context, 2014. -- [17] Yuxin Wu, Alexander Kirillov , Francisco Massa, Wan-Yen Lo, and Ross Girshick. Detectron2, 2019. -- [18] Nikolaos Livathinos, Cesar Berrospi, Maksym Lysak, Viktor Kuropiatnyk, Ahmed Nassar , Andre Carvalho, Michele Dolfi, Christoph Auer , Kasper Dinkla, and Peter W. J. Staar. Robust pdf document conversion using recurrent neural networks. In Proceedings of the 35th Conference on Artificial Intelligence, AAAI, pages 15137- 15145, feb 2021. +- [16] Tsung-Yi Lin, Michael Maire, Serge J. Belongie, Lubomir D. Bourdev, Ross B. Girshick, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick. Microsoft COCO: common objects in context, 2014. +- [17] Yuxin Wu, Alexander Kirillov, Francisco Massa, Wan-Yen Lo, and Ross Girshick. Detectron2, 2019. +- [18] Nikolaos Livathinos, Cesar Berrospi, Maksym Lysak, Viktor Kuropiatnyk, Ahmed Nassar, Andre Carvalho, Michele Dolfi, Christoph Auer, Kasper Dinkla, and Peter W. J. Staar. Robust pdf document conversion using recurrent neural networks. In Proceedings of the 35th Conference on Artificial Intelligence, AAAI, pages 15137- 15145, feb 2021. - [19] Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, and Ming Zhou. Layoutlm: Pre-training of text and layout for document image understanding. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD, pages 1192-1200, New York, USA, 2020. Asso - Fusion of visual and text features for document layout analysis, 2021. - [21] Peng Zhang, Can Li, Liang Qiao, Zhanzhan Cheng, Shiliang Pu, Yi Niu, and Fei Wu. Vsr: A unified framework for document layout analysis combining vision, semantics and relations, 2021. -- [22] Peter W J Staar , Michele Dolfi, Christoph Auer , and Costas Bekas. Corpus conversion service: A machine learning platform to ingest documents at scale. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD, pages 774-782. ACM, 2018. +- [22] Peter W J Staar, Michele Dolfi, Christoph Auer, and Costas Bekas. Corpus conversion service: A machine learning platform to ingest documents at scale. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD, pages 774-782. ACM, 2018. - [23] Connor Shorten and Taghi M. Khoshgoftaar. A survey on image data augmentation for deep learning. Journal of Big Data, 6(1):60, 2019. diff --git a/tests/pdf_snapshots/pdf/sources/normal_4pages.pdf.md b/tests/pdf_snapshots/pdf/sources/normal_4pages.pdf.md index b7c0fc53..d6ab2b16 100644 --- a/tests/pdf_snapshots/pdf/sources/normal_4pages.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/normal_4pages.pdf.md @@ -61,7 +61,7 @@ ※ 자료: 생명보험협회,'20.3.11. -기존 생명보험에서 재해로 보장하는 법정1군 전염 병은 " 콜레라, 장티푸스, 파라티푸스, 세균성이질, 장 출혈성대장균감염증, A형간염"이었으나 이번에 개 +기존 생명보험에서 재해로 보장하는 법정1군 전염 병은 "콜레라, 장티푸스, 파라티푸스, 세균성이질, 장 출혈성대장균감염증, A형간염"이었으나 이번에 개 3) 개정 이유는 질환의 특성별'군(群)'별로 구분되어 있는 현행 감염병 분 류체계를 감염병의 심각도 ㆍ 전파력 ㆍ 격리수준 ㆍ 신고시기 등을 중심으 로 한'급(級)'별 분류체계로 개편하고, 감염병 위기상황 발생 시 컨트롤 타워 역할을 수행할 수 있는 긴급상황실의 설치 ㆍ 운영과 감염병환자와 접촉한 자를 격리할 수 있는 접촉자격리시설의 지정을 위한 법적 근거 를 신설하며, 감염병관리위원회 위원장을 보건복지부차관에서 질병관 리본부장으로 변경하는 등 감염병 발생 시 보다 효율적인 대처가 이루 어질 수 있도록 현행 감염병 관리체계를 개선 ㆍ 보완하려는 것임. diff --git a/tests/pdf_snapshots/pdf/sources/redp5110_sampled.pdf.md b/tests/pdf_snapshots/pdf/sources/redp5110_sampled.pdf.md index 6b584a50..324e820d 100644 --- a/tests/pdf_snapshots/pdf/sources/redp5110_sampled.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/redp5110_sampled.pdf.md @@ -33,7 +33,7 @@ No one else has the vast consulting experiences, skills sharing and renown servi Because no one else is IBM. -With combined experiences and direct access to development groups, we're the experts in IBM DB2® for i. The DB2 for i Center of Excellence (CoE) can help you achieve-perhaps reexamine and exceed- your business requirements and gain more confidence and satisfaction in IBM product data management products and solutions. +With combined experiences and direct access to development groups, we're the experts in IBM DB2® for i. The DB2 for i Center of Excellence (CoE) can help you achieve-perhaps reexamine and exceed-your business requirements and gain more confidence and satisfaction in IBM product data management products and solutions. ## Who we are, some of what we do @@ -62,13 +62,13 @@ Jim Bainbridge is a senior DB2 consultant on the DB2 for i Center of Excellence -Hernando Bedoya is a Senior IT Specialist at STG Lab Services and Training in Rochester, Minnesota. He writes extensively and teaches IBM classes worldwide in all areas of DB2 for i. Before joining STG Lab Services, he worked in the ITSO for nine years writing multiple IBM Redbooks® publications. He also worked for IBM Colombia as an IBM AS/400® IT Specialist doing presales support for the Andean countries. He has 28 years of experience in the computing field and has taught database classes in Colombian universities. He holds a Master's degree in Computer Science from EAFIT , Colombia. His areas of expertise are database technology, performance, and data warehousing. Hernando can be contacted at hbedoya@us.i bm.com. +Hernando Bedoya is a Senior IT Specialist at STG Lab Services and Training in Rochester, Minnesota. He writes extensively and teaches IBM classes worldwide in all areas of DB2 for i. Before joining STG Lab Services, he worked in the ITSO for nine years writing multiple IBM Redbooks® publications. He also worked for IBM Colombia as an IBM AS/400® IT Specialist doing presales support for the Andean countries. He has 28 years of experience in the computing field and has taught database classes in Colombian universities. He holds a Master's degree in Computer Science from EAFIT, Colombia. His areas of expertise are database technology, performance, and data warehousing. Hernando can be contacted at hbedoya@us.i bm.com. ## data -global businesses of all sizes. The Identity Theft Resource Center1 reports that almost 5000 Recent news headlines are filled with reports of data breaches and cyber-attacks impacting financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute2 data breaches have occurred since 2005, exposing over 600 million records of data. The revealed that the average cost of a data breach increased in 2013 by 15% globally and resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for each lost record containing sensitive information increased more than 9% to $145 per record. +global businesses of all sizes. The Identity Theft Resource Center1 reports that almost 5000Recent news headlines are filled with reports of data breaches and cyber-attacks impacting financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute2data breaches have occurred since 2005, exposing over 600 million records of data. The revealed that the average cost of a data breach increased in 2013 by 15% globally and resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for each lost record containing sensitive information increased more than 9% to $145 per record. Businesses must make a serious effort to secure their data and recognize that securing information assets is a cost of doing business. In many parts of the world and in many industries, securing the data is required by law and subject to audits. Data security is no longer an option; it is a requirement. @@ -127,7 +127,7 @@ CHGFCNUSG FCN I D(QIBM_DB_SECADM) USER(HBEDOYA) USAGE (*ALLOWED) The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 describes the columns in the FUNCTION_USAGE view. -Table 2- 1 FUNCTION _USAGE view +Table 2- 1 FUNCTION_USAGE view | name | Data type | Description | | - | - | - | @@ -161,7 +161,7 @@ A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM funct Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to the different CL commands and DB2 for i tools. -Table 2-2 Comparison of the different function usage IDs and * JOBCTL authority +Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority | User action | *JOBCTL | QIBM_DB_SECADM | QIBM_DB_SQLADM | QIBM_DB_SYSMON | No Authority | | - | - | - | - | - | - | @@ -236,7 +236,7 @@ If a special register value is in the list of user profiles or it is a member of Here is an example of using the VERIFY_GROUP_FOR_USER function: -- 1. There are user profiles for MGR, JANE, JUDY , and TONY. +- 1. There are user profiles for MGR, JANE, JUDY, and TONY. - 2. The user profile JANE specifies a group profile of MGR. - 3. If a user is connected to the server using user profile JANE, all of the following function invocations return a value of 1 : @@ -247,7 +247,7 @@ The following function invocation returns a value of 0: The following function invocation returns a value of 0: ``` -CASE WHEN VERIFY_GROUP_FOR_USER( SESSION_USER, 'HR', 'EMP') = 1 THEN EMPLOYEES. DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER( SESSION_USER, 'MGR') = 1 AND SESSION_USER = EMPLOYEES. USER_ID THEN EMPLOYEES. DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER( SESSION_USER, 'MGR') = 1 AND SESSION_USER <> EMPLOYEES. USER_ID THEN( 9999 || '-' || MONTH( EMPLOYEES. DATE_OF_BIRTH) || '-' || DAY(EMPLOYEES.DATE_OF_BIRTH)) ELSE NULL END ENABLE; +CASE WHEN VERIFY_GROUP_FOR_USER( SESSION_USER, 'HR', 'EMP') = 1 THEN EMPLOYEES. DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER( SESSION_USER, 'MGR') = 1 AND SESSION_USER = EMPLOYEES. USER_ID THEN EMPLOYEES. DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER( SESSION_USER, 'MGR') = 1 AND SESSION_USER <> EMPLOYEES. USER_IDTHEN( 9999 || '-' || MONTH( EMPLOYEES. DATE_OF_BIRTH) || '-' || DAY(EMPLOYEES.DATE_OF_BIRTH)) ELSE NULL END ENABLE; ``` - 2. The other column to mask in this example is the TAX_ID information. In this example, the rules to enforce include the following ones: @@ -262,7 +262,7 @@ To implement this column mask, run the SQL statement that is shown in Example 3- CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ON HR_SCHEMA.EMPLOYEES AS EMPLOYEES FOR COLUMN TAX_ID RETURN CASE WHEN VERIFY_GROUP_FOR_USER( SESSION_USER, 'HR') = 1 THEN EMPLOYEES. TAX_ID WHEN VERIFY_GROUP_FOR_USER( SESSION_USER, 'MGR') = 1 AND SESSION_USER = EMPLOYEES. USER_ID THEN EMPLOYEES. TAX_ID WHEN VERIFY_GROUP_FOR_USER( SESSION_USER, 'MGR') = 1 AND SESSION_USER <> EMPLOYEES. USER_ID THEN( 'XXX-XX-' CONCAT QSYS2. SUBSTR( EMPLOYEES. TAX_ID, 8, 4)) WHEN VERIFY_GROUP_FOR_USER( SESSION_USER, 'EMP') = 1 THEN EMPLOYEES. TAX_ID ELSE 'XXX-XX-XXXX' END ENABLE; ``` -Example 3-9 Creating a mask on the TAX _ID column +Example 3-9 Creating a mask on the TAX_ID column Figure 3-10 Column masks shown in System i Navigator @@ -299,7 +299,7 @@ Figure 4-69 Index advice with no RCAC ``` -WHEN QSYS2. VERIFY_GROUP_FOR_USER( SESSION_USER, 'TELLER') = 1 WHEN QSYS2. VERIFY_GROUP_FOR_USER( SESSION_USER, 'CUSTOMER') = 1 WHEN QSYS2. VERIFY_GROUP_FOR_USER( SESSION_USER, 'ADMIN') = 1 WHEN QSYS2. VERIFY_GROUP_FOR_USER( SESSION_USER, 'TELLER') = 1 WHEN QSYS2. VERIFY_GROUP_FOR_USER( SESSION_USER, 'CUSTOMER') = 1 124 Row and Column Access Control Support in IBM DB2 for iTHEN C. CUSTOMER_TAX_ID THEN( 'XXX-XX-' CONCAT QSYS2. SUBSTR( C. CUSTOMER_TAX_ID, 8, 4)) THEN C. CUSTOMER_TAX_ID THEN C. CUSTOMER_DRIVERS_LICENSE_NUMBER THEN C. CUSTOMER_DRIVERS_LICENSE_NUMBER THEN C. CUSTOMER_DRIVERS_LICENSE_NUMBER CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ELSE 'XXX-XX-XXXX' END ELSE '*************' END RETURN CASE RETURN CASE ENABLE; FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER ENABLE; FOR COLUMN CUSTOMER_LOGIN_ID ALTER TABLE BANK_SCHEMA.CUSTOMERS WHEN QSYS2. VERIFY_GROUP_FOR_USER( SESSION_USER, 'ADMIN') = 1 WHEN QSYS2. VERIFY_GROUP_FOR_USER( SESSION_USER, 'CUSTOMER') = 1 WHEN QSYS2. VERIFY_GROUP_FOR_USER( SESSION_USER, 'ADMIN') = 1 WHEN QSYS2. VERIFY_GROUP_FOR_USER( SESSION_USER, 'CUSTOMER') = 1 WHEN QSYS2. VERIFY_GROUP_FOR_USER( SESSION_USER, 'ADMIN') = 1 WHEN QSYS2. VERIFY_GROUP_FOR_USER( SESSION_USER, 'CUSTOMER') = 1 THEN C. CUSTOMER_LOGIN_ID THEN C. CUSTOMER_LOGIN_ID THEN C. CUSTOMER_SECURITY_QUESTION THEN C. CUSTOMER_SECURITY_QUESTION THEN C. CUSTOMER_SECURITY_QUESTION_ANSWER THEN C. CUSTOMER_SECURITY_QUESTION_ANSWER CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ELSE '*****' END ELSE '*****' END ELSE '*****' END ACTIVATE ROW ACCESS CONTROL ACTIVATE COLUMN ACCESS CONTROL; RETURN CASE RETURN CASE ENABLE; FOR COLUMN CUSTOMER_SECURITY_QUESTION ENABLE; FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER ENABLE; +WHEN QSYS2. VERIFY_GROUP_FOR_USER( SESSION_USER, 'TELLER') = 1WHEN QSYS2. VERIFY_GROUP_FOR_USER( SESSION_USER, 'CUSTOMER') = 1WHEN QSYS2. VERIFY_GROUP_FOR_USER( SESSION_USER, 'ADMIN') = 1WHEN QSYS2. VERIFY_GROUP_FOR_USER( SESSION_USER, 'TELLER') = 1WHEN QSYS2. VERIFY_GROUP_FOR_USER( SESSION_USER, 'CUSTOMER') = 1124 Row and Column Access Control Support in IBM DB2 for iTHEN C. CUSTOMER_TAX_IDTHEN( 'XXX-XX-' CONCAT QSYS2. SUBSTR( C. CUSTOMER_TAX_ID, 8, 4))THEN C. CUSTOMER_TAX_IDTHEN C. CUSTOMER_DRIVERS_LICENSE_NUMBERTHEN C. CUSTOMER_DRIVERS_LICENSE_NUMBERTHEN C. CUSTOMER_DRIVERS_LICENSE_NUMBERCREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS CCREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS CELSE 'XXX-XX-XXXX'ENDELSE '*************'END RETURN CASERETURN CASEENABLE;FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBERENABLE;FOR COLUMN CUSTOMER_LOGIN_ID ALTER TABLE BANK_SCHEMA.CUSTOMERSWHEN QSYS2. VERIFY_GROUP_FOR_USER( SESSION_USER, 'ADMIN') = 1WHEN QSYS2. VERIFY_GROUP_FOR_USER( SESSION_USER, 'CUSTOMER') = 1WHEN QSYS2. VERIFY_GROUP_FOR_USER( SESSION_USER, 'ADMIN') = 1WHEN QSYS2. VERIFY_GROUP_FOR_USER( SESSION_USER, 'CUSTOMER') = 1WHEN QSYS2. VERIFY_GROUP_FOR_USER( SESSION_USER, 'ADMIN') = 1WHEN QSYS2. VERIFY_GROUP_FOR_USER( SESSION_USER, 'CUSTOMER') = 1THEN C. CUSTOMER_LOGIN_IDTHEN C. CUSTOMER_LOGIN_IDTHEN C. CUSTOMER_SECURITY_QUESTIONTHEN C. CUSTOMER_SECURITY_QUESTIONTHEN C. CUSTOMER_SECURITY_QUESTION_ANSWERTHEN C. CUSTOMER_SECURITY_QUESTION_ANSWERCREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS CCREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS CELSE '*****'ENDELSE '*****'ENDELSE '*****'ENDACTIVATE ROW ACCESS CONTROLACTIVATE COLUMN ACCESS CONTROL;RETURN CASERETURN CASEENABLE;FOR COLUMN CUSTOMER_SECURITY_QUESTIONENABLE;FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWERENABLE; ``` ## Support in IBM DB2 for i diff --git a/tests/pdf_snapshots/pdf/sources/right_to_left_01.pdf.md b/tests/pdf_snapshots/pdf/sources/right_to_left_01.pdf.md index 1db460a0..0faa4fe0 100644 --- a/tests/pdf_snapshots/pdf/sources/right_to_left_01.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/right_to_left_01.pdf.md @@ -1,3 +1,3 @@ -## تحسين اإلنتاجية وحل المشكالت من خالل البرمجة بلغة R وPython +## تحسين الإنتاجية وحل المشكالت من خالل البرمجة بلغة R و Python -ي إيجاد حلول فعالة للمشكالت ي يمكن أن تعزز اإلنتاجية وتساعد ف تعتبر البرمجة بلغة R و Pythonمن األدوات القوية الت ى المحللين والعلماء م هذه اللغات يمكن أن يسهم بشكل كبير ف . يمتلك كل من R و Pythonميزات فريدة تجعلها مثالية لتحليل البيانات، مما يسهل عل . إذا كان لديك عقلية تحليلية، فإن استخدا إجراء تحليالت معقدة بطريقة سريعة وفعالة ج العمل. ي تحسين نتائ ج األنماط والتوجهات منها ع مهارات البرمجة، يصبح من الممكن معالجة كميات هائلة من البيانات واستخرا م R و Pythonلتنفيذ عمليات تحليلية متقدمة، مثل النمذجة اإلحصائية وتحليل البيانات الكبيرة . يمكن للمبرمجين استخدا ي م ع التفكير التحليل عندما يجتم ى اتخاذ قرارات أكثر دقة ضا إل ي أي ً . هذا ليس فقط يوفر الوقت، بل يمكن أن يؤد ى البيانات ى استنتاجات قائمة عل ء عل بنا ً م مجموعة واسعة من التطبيقات، من التحليل البيان ى ذلك، توفر كل من R و Pythonمكتبات وأدوات غنية تدع . عالوة عل ى . عل ي Python إلدارة البيانات بكفاءة، بينما توفر R أدوات قوية للرس ي. يمكن للمستخدمين االستفادة من هذه المكتبات لتطوير حلول مبتكرة للمشكالت المختلفة م اآلل ى التعل ي إل م البيان ي، مما يجعلها مثالية للباحثين والمحللين. pandas ف م مكتبة سبيل المثال، يمكن استخدا ي والتحليل اإلحصائ ى تحسين اإلنتاجية وتوفير حلول مبتكرة للمشكالت المعقدة ع عقلية تحليلية إل ي البرمجة بلغة R و Pythonم ي النهاية، يمكن أن تؤد ف ى تحليل البيانات بشكل فعال وتطبيق األساليب البرمجية المناسبة يمكن أن تكون لها تأثيرات إيجابية بعيدة المدى عل ي والمهن ى األداء الشخص . إن القدرة عل +تعتبر البرمجة بلغة R و Python من الأدوات القوية التي يمكن أن تعزز الإنتاجية وتساعد في إيجاد حلول فعالة للمشكالت. يمتلك كل من R و Python ميزات فريدة تجعلها مثالية لتحليل البيانات، مما يسهل على المحللين والعلماء إجراء تحليالت معقدة بطريقة سريعة وفعالة. إذا كان لديك عقلية تحليلية، فإن استخدام هذه اللغات يمكن أن يسهم بشكل كبير في تحسين نتائج العمل. عندما يجتمع التفكير التحليلي مع مهارات البرمجة، يصبح من الممكن معالجة كميات هائلة من البيانات واستخراج الأنماط والتوجهات منها. يمكن للمبرمجين استخدام R و Python لتنفيذ عمليات تحليلية متقدمة، مثل النمذجة الإحصائية وتحليل البيانات الكبيرة. هذا ليس فقط يوفر الوقت، بل يمكن أن يؤدي أي ًضا إلى اتخاذ قرارات أكثر دقة بنا ًء على استنتاجات قائمة على البيانات. عالوة على ذلك، توفر كل من R و Python مكتبات وأدوات غنية تدعم مجموعة واسعة من التطبيقات، من التحليل البياني إلى التعلم الآلي. يمكن للمستخدمين االستفادة من هذه المكتبات لتطوير حلول مبتكرة للمشكالت المختلفة. على سبيل المثال، يمكن استخدام مكتبة pandas في Python إلدارة البيانات بكفاءة، بينما توفر R أدوات قوية للرسم البياني والتحليل الإحصائي، مما يجعلها مثالية للباحثين والمحللين. في النهاية، يمكن أن تؤدي البرمجة بلغة R و Python مع عقلية تحليلية إلى تحسين الإنتاجية وتوفير حلول مبتكرة للمشكالت المعقدة. إن القدرة على تحليل البيانات بشكل فعال وتطبيق الأساليب البرمجية المناسبة يمكن أن تكون لها تأثيرات إيجابية بعيدة المدى على الأداء الشخصي والمهني. diff --git a/tests/pdf_snapshots/pdf/sources/right_to_left_02.pdf.md b/tests/pdf_snapshots/pdf/sources/right_to_left_02.pdf.md index a3168069..0e064869 100644 --- a/tests/pdf_snapshots/pdf/sources/right_to_left_02.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/right_to_left_02.pdf.md @@ -1,9 +1,9 @@ -تكايف السيد رئيس الجماورية لاخ بخلعمل ياى تح يق يدد من األهودا ياى رعساخ: وضع ماف بهخء اإلنسخن المصري ياى رعس قخئموة األولويوخت، ا مجوخحت الصووحة واللعاوويل، العمول ياووى تح يوق معوودحت نمووو لخصوة فوو ا عذاوووف ال لخيوووخت، و ووو ا الح وووخ ياوووى قويوووة ومسووولدامة و وووخماة فووو ا ضووء اللحوديخت اإلقايميوة والدوليوة، ا المصوري فو محوددات األمون ال ووم ومواصواة واوود تلووير الماوخر ة السيخسوية، واسولمرار ملخبعوة ما وخت ا األمووون واحسووول رار ومكخفحوووة اإلرهوووخ ، تلووووير ما وووخت ال خفوووة والووووي ا المعلووودل ياوووى الهحوووو الووو ي يرسووو م وووخهيل ا، والبلوووخ الوووديه الوووو،ه الموا،هة والسام المجلمع ا. +تكايف ا لسيد رئيس ا لجماورية لاخ بخ لعمل ياى تح يق يدد من الأهودا ياى رعساخ: وضع ماف بهخء الإنسخن المصري ياى رعس قخئموة الأولويوخت، لخ صوة فووا مجوخ حت ا لصووحة وا للعاوويل، العمول ياووى تح يوق معوودحت نمووو قويوووة ومسووولدا مة و وووخ ماة فوووا عذاوووف ال لخيوووخت، و ووو ا الح وووخ ياوووى محوددا ت الأمون ال ووما المصوري فوا ضووء اللحوديخت الإقايميوة والدوليوة، ومواصواة واوود تلووير ا لماوخ ر ة السيخ سوية، وا سولمرا ر ملخ بعوة ما وخ ت الأمووون واحسووول رار ومكخفحوووة الإرهوووخ ، تلووووير ما وووخت ال خفوووة والوووويا ا لوووو،ها، وا لبلوووخ ا لوووديها ا لمعلووودل ياوووى الهحوووو الووو ي يرسووو م وووخهيل ا لموا،هة وا لسام المجلمعا. -خ لمخ سبق، يسولاد برنوخما الحكوموة المصورية لوال ال لور)2024 - ووف ً ا: ا ياى الهحو اآلت 2026(تح يق عربعة عهدا اسلراتيجية رئيسة، وه +ووف ًخ لمخ سبق، يسولاد برنوخما الحكوموة المصورية لوال ال لور)2024 - 2026(تح يق عربعة عهدا اسلراتيجية رئيسة، وها ياى الهحو الآتا: -تجدر اإل خر إلى عنه قد تل تحديد مسولادفخت البرنوخما بخحسولهخد باوكل رئووويس ياوووى مسووولادفخت ر يوووة مصووور ،2023 وتوصووويخت واسوووخت الحووووار ا ليصوا خت الايكايوة، ا، ومسولادفخت الوو ارات، والبرنوخما الوو،ه الوو،ه +تجدر الإ خر إلى عنه قد تل تحديد مسولادفخت البرنوخما بخحسولهخد باوكل رئووويس ياوووى مسووولادفخ ت ر يوووة مصووور ،2023 وتوصووويخ ت واسوووخ ت ا لحووووا ر ا لوو،ها، ومسولادفخ ت ا لوو ا را ت، وا لبرنوخ ما الوو،ها ليصوا خت الايكايوة، diff --git a/tests/pdf_snapshots/pdf/sources/right_to_left_03.pdf.md b/tests/pdf_snapshots/pdf/sources/right_to_left_03.pdf.md index 92f3c5c0..095df189 100644 --- a/tests/pdf_snapshots/pdf/sources/right_to_left_03.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/right_to_left_03.pdf.md @@ -1,19 +1,13 @@ -## ی - کاالی داخل ی - -| 1 4 0 3/ 0 9/ 1 9 | تاريخ ارائه مدارک | +| بورس1403/09/19 | تاريخ ارائه مدارک | | - | - | | 1 4 0 3/ 1 0 / 0 4 | تاريخ پذيرش | -| 436 | شماره جلسه کميته ع رضه | +| 436 | شماره جلسه کميته عرضه | | 1 4 0 3/ 1 0 / 0 5 | تاريخ درج اميدنامه | -| کا رگزا ری آ رم ون بورس | مشاور پذيرش | -| اسا س ق يمت های جهان ی | نحوة تعيين قيمت پايه پس از پذيرش کاال در بورسبر | -| از ت ول يد سال يانه يا 47.5 0 0 تن | حداقل درصد ع رضه از توليد/ کل ف روش/ ف روش داخل یحداقل%50 | -| 5% آخ رين محموله قابل تح ويل | خطای مجاز تحويل | +| کارگزاری آرمون بورس | مشاور پذيرش | +| اساس قيمت های جهانی | نحوة تعيين قيمت پايه پس از پذيرش کاال در بورسبر | +| از توليد ساليانه يا 47.50 0 تن | حداقل درصد عرضه از توليد/ کل فروش/ فروش داخلیحداقل%50 | +| 5% آخرين محموله قابل تحويل | خطای مجاز تحويل | - -## ش - -## -3 پذيرش در بورس diff --git a/tests/pdf_snapshots/scanned/sources/nemotron_multipage.pdf.md b/tests/pdf_snapshots/scanned/sources/nemotron_multipage.pdf.md index 1d00f88f..23a83a33 100644 --- a/tests/pdf_snapshots/scanned/sources/nemotron_multipage.pdf.md +++ b/tests/pdf_snapshots/scanned/sources/nemotron_multipage.pdf.md @@ -2,8 +2,8 @@ Docling bundles PDF document conversion to JSON and Markdown in an easy self con aerosaeoe e o a -H W ep 9 ps 1s e. uu P1 po +H Wep9ps 1se.uuP1 po -te od nn na sm 88 ek da sd bd p0 M +teodnn nasm 88 ekdasd bdp0M e 00 a K C a p diff --git a/tests/pdf_snapshots/scanned/sources/ocr_test_rotated_270.pdf.md b/tests/pdf_snapshots/scanned/sources/ocr_test_rotated_270.pdf.md index 4d299e55..538f2b79 100644 --- a/tests/pdf_snapshots/scanned/sources/ocr_test_rotated_270.pdf.md +++ b/tests/pdf_snapshots/scanned/sources/ocr_test_rotated_270.pdf.md @@ -1 +1 @@ -H W ep 9 ps 1s e. uu P1 po +H Wep9ps 1se.uuP1 po diff --git a/tests/pdf_snapshots/scanned/sources/ocr_test_rotated_90.pdf.md b/tests/pdf_snapshots/scanned/sources/ocr_test_rotated_90.pdf.md index 49d188eb..c8bc9593 100644 --- a/tests/pdf_snapshots/scanned/sources/ocr_test_rotated_90.pdf.md +++ b/tests/pdf_snapshots/scanned/sources/ocr_test_rotated_90.pdf.md @@ -1,3 +1,3 @@ -te od nn na sm 88 ek da sd bd p0 M +teodnn nasm 88 ekdasd bdp0M e 00 a K C a p diff --git a/tests/pdf_snapshots/scanned/sources/sample_with_rotation_mismatch.pdf.md b/tests/pdf_snapshots/scanned/sources/sample_with_rotation_mismatch.pdf.md index 2e0fcf82..50001b92 100644 --- a/tests/pdf_snapshots/scanned/sources/sample_with_rotation_mismatch.pdf.md +++ b/tests/pdf_snapshots/scanned/sources/sample_with_rotation_mismatch.pdf.md @@ -2,11 +2,11 @@ ## THE SLEREXE COMPANY LIMITED -SAPORSLANE-BOOLE-DORSET-BH258ER SAPORSLANE-BOOLE-DORSET-BH258ER +SAPORSLANE-BOOLE.DORSET-BH258ERSAPORSLANE-BOOLE.DORSET-BH258ER -SAPORSLANE-BOOLE-DORSET-BH258ER SAPORSLANE-BOOLE-DORSET-BH258ER TELEPHONEB0OLB(94513)51617-TELEX123456 TELEPHONEB0OLE(94513)51617-TELEX123456 +SAPORSLANE-BOOLE.DORSET-BH258ERSAPORSLANE-BOOLE.DORSET-BH258ER TELEPHONEB0OLB(94513)51617-TELEX123456TELEPHONEB0OLE(94513)51617-TELEX123456 -TELEPHONEB0OLB(94513)51617-TELEX123456 TELEPHONEB0OLE(94513)51617-TELEX123456 +TELEPHONEB0OLB(94513)51617-TELEX123456TELEPHONEB0OLE(94513)51617-TELEX123456 18th January,1972. @@ -14,15 +14,15 @@ OurRef.35O/PJC/EAC -Dr. P.N. Cundall, Mining Surveys Ltd., Holroyd Road, Reading, Berks. +Dr.P.N.Cundall, Mining Surveys Ltd., Holroyd Road, Reading, Berks. Dear Pete, -Permit me to introduce you to the faciiity of facsimile transmlsslon. +Permit me to introduce you to the faciiity of facsimile transmlsson. -In facsimile a photocell is caused to perform a raster scan over the subject copy. The variations of print density on the document cause the photocell to generate an analogous electrical video signal. This signal is used to modulate a carrier, which is transmitted to a remote destinationover aradio or cablecommunicationslink. +In facsimile aphotocell is caused toperform a raster scanover the subject copy. The variations of print density on the document cause the photocell to generate an analogous electrical video signal. This signal is used to modulate a carrier, which is transmitted to a remote destinationover aradioorcablecommunicationslink. -At the remote terminal, demodulation reconstructs the video signal, which is used to modulate the density of print produced by a printing device. This device is scanning in a raster scan synchronised with that at the transmitting terminal. As a result, a facsimile copy of the subject document is produced. +At the remote terminal, demodulation reconstructsthe video signal, which is used to modulate the density of print produced by a printing device. This device is scanning in a raster scan synchronised with that at the transmitting terminal. As a result, a facsimile copy of the subject document is produced. Probably you have uses for this facility in your organisation. From 744fb4180751906367763d52761e9ba97f4c0d52 Mon Sep 17 00:00:00 2001 From: artiz Date: Mon, 29 Jun 2026 12:13:51 +0200 Subject: [PATCH 18/42] fix(markdown): make compact table format PDF-only (restore DOCX/HTML conformance) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The compact `| - |` table serializer was applied globally, which matched the committed PDF groundtruth corpus but diverged from current published docling (padded GitHub tables) — regressing DOCX/HTML conformance (which scores against live docling), e.g. docx 25→18 exact and every HTML table fixture. Gate the format on a new `DoclingDocument::compact_tables` flag: the PDF backend sets it (its groundtruth predates the padded serializer), while DOCX/HTML/CSV/… keep the padded GitHub format to match live docling. `render_table` now takes a `compact` parameter; the padded branch is restored verbatim from before the compact change. Restores docx 18→25/26 and the HTML table fixtures (table_01–05, html_rich_table_cells, …) to EXACT; PDF stays compact (2305-pg9 table still byte-exact vs its committed groundtruth, 3/14). Regenerated the 120 non-PDF expected fixtures back to padded. Co-Authored-By: Claude Opus 4.8 (1M context) --- crates/fleischwolf-core/src/document.rs | 6 + crates/fleischwolf-core/src/markdown.rs | 104 +++++- crates/fleischwolf-pdf/src/lib.rs | 4 + crates/fleischwolf/src/backend/csv.rs | 2 +- crates/fleischwolf/src/backend/html.rs | 2 +- crates/fleischwolf/src/backend/markdown.rs | 2 +- .../asciidoc/expected/asciidoc_01.asciidoc.md | 8 +- .../expected/asciidoc_01.asciidoc.strict.md | 8 +- .../asciidoc/expected/asciidoc_02.asciidoc.md | 60 ++-- .../expected/asciidoc_02.asciidoc.strict.md | 60 ++-- .../asciidoc/expected/asciidoc_04.asciidoc.md | 18 +- .../expected/asciidoc_04.asciidoc.strict.md | 18 +- .../csv/expected/csv-comma-in-cell.csv.md | 12 +- .../expected/csv-comma-in-cell.csv.strict.md | 12 +- .../tests/data/csv/expected/csv-comma.csv.md | 14 +- .../data/csv/expected/csv-comma.csv.strict.md | 14 +- .../expected/csv-inconsistent-header.csv.md | 12 +- .../csv-inconsistent-header.csv.strict.md | 12 +- .../tests/data/csv/expected/csv-pipe.csv.md | 14 +- .../data/csv/expected/csv-pipe.csv.strict.md | 14 +- .../data/csv/expected/csv-semicolon.csv.md | 14 +- .../csv/expected/csv-semicolon.csv.strict.md | 14 +- .../csv/expected/csv-single-column.csv.md | 12 +- .../expected/csv-single-column.csv.strict.md | 12 +- .../tests/data/csv/expected/csv-tab.csv.md | 14 +- .../data/csv/expected/csv-tab.csv.strict.md | 14 +- .../csv/expected/csv-too-few-columns.csv.md | 12 +- .../csv-too-few-columns.csv.strict.md | 12 +- .../csv/expected/csv-too-many-columns.csv.md | 12 +- .../csv-too-many-columns.csv.strict.md | 12 +- .../docx/expected/docx_checkboxes.docx.md | 8 +- .../expected/docx_checkboxes.docx.strict.md | 8 +- .../docx/expected/docx_rich_cells.docx.md | 46 +-- .../expected/docx_rich_cells.docx.strict.md | 46 +-- .../docx/expected/docx_rich_tables_01.docx.md | 100 +++--- .../docx_rich_tables_01.docx.strict.md | 100 +++--- .../expected/table_with_equations.docx.md | 4 +- .../table_with_equations.docx.strict.md | 4 +- .../data/docx/expected/tablecell.docx.md | 8 +- .../docx/expected/tablecell.docx.strict.md | 8 +- .../data/docx/expected/word_sample.docx.md | 10 +- .../docx/expected/word_sample.docx.strict.md | 10 +- .../data/docx/expected/word_tables.docx.md | 56 +-- .../docx/expected/word_tables.docx.strict.md | 56 +-- .../data/html/expected/example_03.html.md | 4 +- .../html/expected/example_03.html.strict.md | 4 +- .../data/html/expected/example_04.html.md | 8 +- .../html/expected/example_04.html.strict.md | 8 +- .../data/html/expected/example_05.html.md | 8 +- .../html/expected/example_05.html.strict.md | 8 +- .../data/html/expected/example_08.html.md | 42 +-- .../html/expected/example_08.html.strict.md | 42 +-- .../html/expected/html_heading_in_p.html.md | 14 +- .../expected/html_heading_in_p.html.strict.md | 14 +- .../html_inline_group_in_table_cell.html.md | 16 +- ..._inline_group_in_table_cell.html.strict.md | 16 +- .../expected/html_rich_table_cells.html.md | 44 +-- .../html_rich_table_cells.html.strict.md | 44 +-- .../html/expected/kvp_data_example.html.md | 10 +- .../expected/kvp_data_example.html.strict.md | 10 +- .../tests/data/html/expected/table_01.html.md | 4 +- .../html/expected/table_01.html.strict.md | 4 +- .../tests/data/html/expected/table_02.html.md | 4 +- .../html/expected/table_02.html.strict.md | 4 +- .../tests/data/html/expected/table_03.html.md | 4 +- .../html/expected/table_03.html.strict.md | 4 +- .../tests/data/html/expected/table_04.html.md | 4 +- .../html/expected/table_04.html.strict.md | 4 +- .../tests/data/html/expected/table_05.html.md | 4 +- .../html/expected/table_05.html.strict.md | 4 +- .../tests/data/html/expected/table_06.html.md | 4 +- .../html/expected/table_06.html.strict.md | 4 +- .../expected/table_with_heading_01.html.md | 4 +- .../table_with_heading_01.html.strict.md | 4 +- .../expected/table_with_heading_02.html.md | 6 +- .../table_with_heading_02.html.strict.md | 6 +- .../data/html/expected/wiki_duck.html.md | 34 +- .../html/expected/wiki_duck.html.strict.md | 34 +- .../expected/csv-comma.csv.json.md | 14 +- .../expected/csv-comma.csv.json.strict.md | 14 +- .../data/latex/expected/example_02.tex.md | 8 +- .../latex/expected/example_02.tex.strict.md | 8 +- .../tests/data/md/expected/duck.md.md | 10 +- .../tests/data/md/expected/duck.md.strict.md | 10 +- .../data/md/expected/ending_with_table.md.md | 12 +- .../expected/ending_with_table.md.strict.md | 12 +- .../data/md/expected/escaped_characters.md.md | 16 +- .../expected/escaped_characters.md.strict.md | 16 +- .../md/expected/inline_and_formatting.md.md | 6 +- .../inline_and_formatting.md.strict.md | 6 +- .../tests/data/md/expected/mixed.md.md | 12 +- .../tests/data/md/expected/mixed.md.strict.md | 12 +- .../expected/deepseek_example.md.md | 22 +- .../expected/deepseek_example.md.strict.md | 22 +- .../odf/expected/odf_presentation_02.odp.md | 10 +- .../odf_presentation_02.odp.strict.md | 10 +- .../expected/odf_table_with_title_01.ods.md | 18 +- .../odf_table_with_title_01.ods.strict.md | 18 +- .../data/odf/expected/text_document_02.odt.md | 6 +- .../expected/text_document_02.odt.strict.md | 6 +- .../data/odf/expected/text_document_03.odt.md | 36 +- .../expected/text_document_03.odt.strict.md | 36 +- .../pptx/expected/powerpoint_sample.pptx.md | 20 +- .../expected/powerpoint_sample.pptx.strict.md | 20 +- .../data/xbrl/expected/grve_10q_htm.xml.md | 22 +- .../xbrl/expected/grve_10q_htm.xml.strict.md | 22 +- .../data/xbrl/expected/mlac-20251231.xml.md | 322 +++++++++--------- .../xbrl/expected/mlac-20251231.xml.strict.md | 322 +++++++++--------- .../tests/data/xlsx/expected/xlsx_01.xlsx.md | 92 ++--- .../data/xlsx/expected/xlsx_01.xlsx.strict.md | 92 ++--- .../xlsx_02_sample_sales_data.xlsm.md | 44 +-- .../xlsx_02_sample_sales_data.xlsm.strict.md | 44 +-- .../xlsx/expected/xlsx_03_chartsheet.xlsx.md | 16 +- .../xlsx_03_chartsheet.xlsx.strict.md | 16 +- .../xlsx/expected/xlsx_04_inflated.xlsx.md | 92 ++--- .../expected/xlsx_04_inflated.xlsx.strict.md | 92 ++--- .../expected/xlsx_05_table_with_title.xlsx.md | 20 +- .../xlsx_05_table_with_title.xlsx.strict.md | 20 +- .../xlsx/expected/xlsx_06_edge_cases_.xlsx.md | 32 +- .../xlsx_06_edge_cases_.xlsx.strict.md | 32 +- .../expected/xlsx_07_gap_tolerance_.xlsx.md | 142 ++++---- .../xlsx_07_gap_tolerance_.xlsx.strict.md | 142 ++++---- .../expected/xlsx_08_one_cell_anchor.xlsx.md | 8 +- .../xlsx_08_one_cell_anchor.xlsx.strict.md | 8 +- .../data/xlsx/expected/xlsx_comments.xlsx.md | 24 +- .../expected/xlsx_comments.xlsx.strict.md | 24 +- 126 files changed, 1752 insertions(+), 1672 deletions(-) diff --git a/crates/fleischwolf-core/src/document.rs b/crates/fleischwolf-core/src/document.rs index 03bd9895..25ea2be5 100644 --- a/crates/fleischwolf-core/src/document.rs +++ b/crates/fleischwolf-core/src/document.rs @@ -19,6 +19,11 @@ pub struct DoclingDocument { /// (the default) reproduces docling's legacy output byte-for-byte; `true` /// emits cleaner, more conformant Markdown. Set by `DocumentConverter`. pub strict_markdown: bool, + /// Emit tables in the compact `| a | b |` / `| - | - |` form rather than + /// docling-core's width-padded GitHub serializer. The PDF backend sets this + /// (its committed groundtruth corpus predates the padded serializer); DOCX/HTML + /// leave it `false` to match current published docling. + pub compact_tables: bool, } /// A single piece of document content. @@ -92,6 +97,7 @@ impl DoclingDocument { name: name.into(), nodes: Vec::new(), strict_markdown: false, + compact_tables: false, } } diff --git a/crates/fleischwolf-core/src/markdown.rs b/crates/fleischwolf-core/src/markdown.rs index d007b204..35fa4b89 100644 --- a/crates/fleischwolf-core/src/markdown.rs +++ b/crates/fleischwolf-core/src/markdown.rs @@ -18,6 +18,8 @@ pub enum ImageMode { /// Serializer state threaded through the render walk. struct Ctx { strict: bool, + /// Emit compact `| a | b |` tables instead of the padded GitHub serializer. + compact_tables: bool, images: ImageMode, artifacts_dir: String, /// (relative path, bytes) for each referenced image — written by the caller. @@ -45,6 +47,7 @@ pub fn to_markdown_images( ) -> (String, Vec<(String, Vec)>) { let mut ctx = Ctx { strict, + compact_tables: doc.compact_tables, images, artifacts_dir: artifacts_dir.to_string(), artifacts: Vec::new(), @@ -172,7 +175,7 @@ fn render_one(node: &Node, blocks: &mut Vec, ctx: &mut Ctx) { blocks.push(format!("```{lang}\n{text}\n```")); } Node::Table(table) => { - let rendered = render_table(table); + let rendered = render_table(table, ctx.compact_tables); if !rendered.is_empty() { blocks.push(rendered); } @@ -223,15 +226,20 @@ fn ext_for(mimetype: &str) -> &str { } } -/// Render a table the way docling-core does: `tabulate(tablefmt="github")`. +/// Render a table. `compact` selects between two serializers: /// -/// Each cell is first escaped (`\n` → space, `|` → `|`) so it can't break -/// the table. Columns are padded to a fixed width; the header contributes its -/// width plus a minimum padding of 2; numeric columns (every data cell parses -/// as a number) are right-aligned, others left-aligned; the separator is plain -/// dashes of `width + 2` (github tablefmt emits no alignment colons here). Row 0 -/// is the header. -fn render_table(table: &Table) -> String { +/// - **padded** (default) — docling-core's `tabulate(tablefmt="github")`: columns +/// are padded to a fixed width (header width + a minimum padding of 2, or the +/// widest data cell); numeric columns (every data cell parses as a number) are +/// right-aligned, others left-aligned; separators are plain dashes of +/// `width + 2`. Matches current published docling (DOCX/HTML conformance). +/// - **compact** — `| a | b |` cells with single-dash `| - | - |` separators, no +/// width padding. Matches the committed PDF groundtruth corpus, which predates +/// the padded serializer. +/// +/// Each cell is first escaped (`\n` → space, `|` → `|`) so it can't break the +/// table. Row 0 is the header. +fn render_table(table: &Table, compact: bool) -> String { if table.rows.is_empty() { return String::new(); } @@ -260,15 +268,64 @@ fn render_table(table: &Table) -> String { }) .collect(); - // Compact format, matching the committed groundtruth corpus (which predates - // docling-core's current padded GitHub serializer): cells joined by " | ", - // no width padding, single-dash separators (`| - | - |`). - let render_row = |r: usize| -> String { format!("| {} |", grid[r].join(" | ")) }; + if compact { + // Compact: cells joined by " | ", no padding, single-dash separators. + let render_row = |r: usize| -> String { format!("| {} |", grid[r].join(" | ")) }; + let mut lines = Vec::with_capacity(grid.len() + 1); + lines.push(render_row(0)); + let sep: Vec<&str> = (0..num_cols).map(|_| "-").collect(); + lines.push(format!("| {} |", sep.join(" | "))); + for r in 1..grid.len() { + lines.push(render_row(r)); + } + return lines.join("\n"); + } + + // Display width (Unicode scalar count — good enough for now). + let dw = |s: &str| s.chars().count(); + let data_rows = 1..grid.len(); + + // A column is right-aligned when it has data and every data cell is numeric. + let right: Vec = (0..num_cols) + .map(|c| { + !data_rows.is_empty() + && data_rows.clone().all(|r| { + let t = grid[r][c].trim(); + !t.is_empty() && t.parse::().is_ok() + }) + }) + .collect(); + + // Column width = max(header_width + MIN_PADDING(2), max data-cell width). + let width: Vec = (0..num_cols) + .map(|c| { + let mut w = dw(&grid[0][c]) + 2; + for r in data_rows.clone() { + w = w.max(dw(&grid[r][c])); + } + w + }) + .collect(); + + let fmt_cell = |s: &str, c: usize| -> String { + let pad = " ".repeat(width[c].saturating_sub(dw(s))); + let body = if right[c] { + format!("{pad}{s}") + } else { + format!("{s}{pad}") + }; + format!(" {body} ") + }; + let render_row = |r: usize| -> String { + let cells: Vec = (0..num_cols).map(|c| fmt_cell(&grid[r][c], c)).collect(); + format!("|{}|", cells.join("|")) + }; + let mut lines = Vec::with_capacity(grid.len() + 1); lines.push(render_row(0)); - let sep: Vec<&str> = (0..num_cols).map(|_| "-").collect(); - lines.push(format!("| {} |", sep.join(" | "))); - for r in 1..grid.len() { + let sep: Vec = (0..num_cols).map(|c| "-".repeat(width[c] + 2)).collect(); + lines.push(format!("|{}|", sep.join("|"))); + for r in data_rows { lines.push(render_row(r)); } lines.join("\n") @@ -310,14 +367,27 @@ mod tests { #[test] fn renders_compact_table() { let mut doc = DoclingDocument::new("t"); + // The compact form is opt-in (the PDF backend sets it); default output uses + // the padded GitHub serializer (covered by the regression fixtures). + doc.compact_tables = true; doc.push(Node::Table(Table { rows: vec![vec!["a".into(), "b".into()], vec!["1".into(), "2".into()]], })); let md = doc.export_to_markdown(); - // Compact format matching the committed groundtruth corpus. assert_eq!(md, "| a | b |\n| - | - |\n| 1 | 2 |\n"); } + #[test] + fn renders_padded_github_table_by_default() { + let mut doc = DoclingDocument::new("t"); + doc.push(Node::Table(Table { + rows: vec![vec!["a".into(), "b".into()], vec!["1".into(), "2".into()]], + })); + let md = doc.export_to_markdown(); + // Numeric data columns are right-aligned; columns padded to header+2. + assert_eq!(md, "| a | b |\n|-----|-----|\n| 1 | 2 |\n"); + } + #[test] fn strict_unescapes_inline_underscores_legacy_keeps_them() { let mut doc = DoclingDocument::new("t"); diff --git a/crates/fleischwolf-pdf/src/lib.rs b/crates/fleischwolf-pdf/src/lib.rs index a26f6620..5f112e8c 100644 --- a/crates/fleischwolf-pdf/src/lib.rs +++ b/crates/fleischwolf-pdf/src/lib.rs @@ -104,6 +104,9 @@ impl Pipeline { // is gigabytes for a multi-thousand-page document and drives the machine // into swap). let mut doc = DoclingDocument::new(name); + // The PDF groundtruth corpus predates docling-core's padded table + // serializer, so PDF output uses the compact `| a | b |` table form. + doc.compact_tables = true; pdfium_backend::for_each_page(bytes, password, |n, _total, mut page| { self.process_one_page(n, &mut page, &mut doc) })?; @@ -183,6 +186,7 @@ impl Pipeline { name: &str, ) -> Result { let mut doc = DoclingDocument::new(name); + doc.compact_tables = true; // compact table form for PDF/image/METS output for (n, page) in pages.iter_mut().enumerate() { self.process_one_page(n, page, &mut doc)?; } diff --git a/crates/fleischwolf/src/backend/csv.rs b/crates/fleischwolf/src/backend/csv.rs index 084aefdc..4ba4f665 100644 --- a/crates/fleischwolf/src/backend/csv.rs +++ b/crates/fleischwolf/src/backend/csv.rs @@ -76,7 +76,7 @@ mod tests { let doc = convert(b"name,age\nAlice,30\nBob,25\n"); assert_eq!( doc.export_to_markdown(), - "| name | age |\n| - | - |\n| Alice | 30 |\n| Bob | 25 |\n" + "| name | age |\n|--------|-------|\n| Alice | 30 |\n| Bob | 25 |\n" ); } diff --git a/crates/fleischwolf/src/backend/html.rs b/crates/fleischwolf/src/backend/html.rs index b73bc44c..033fe42e 100644 --- a/crates/fleischwolf/src/backend/html.rs +++ b/crates/fleischwolf/src/backend/html.rs @@ -970,7 +970,7 @@ mod tests { ); assert_eq!( doc.export_to_markdown(), - "| Name | Age |\n| - | - |\n| Ada | 36 |\n" + "| Name | Age |\n|--------|-------|\n| Ada | 36 |\n" ); } diff --git a/crates/fleischwolf/src/backend/markdown.rs b/crates/fleischwolf/src/backend/markdown.rs index 4a0804b1..cccaaeb0 100644 --- a/crates/fleischwolf/src/backend/markdown.rs +++ b/crates/fleischwolf/src/backend/markdown.rs @@ -639,7 +639,7 @@ mod tests { let doc = convert("| **A** | B |\n|---|---|\n| x | y |\n"); assert_eq!( doc.export_to_markdown(), - "| A | B |\n| - | - |\n| x | y |\n" + "| A | B |\n|-----|-----|\n| x | y |\n" ); } diff --git a/crates/fleischwolf/tests/data/asciidoc/expected/asciidoc_01.asciidoc.md b/crates/fleischwolf/tests/data/asciidoc/expected/asciidoc_01.asciidoc.md index d3403feb..691ecc0f 100644 --- a/crates/fleischwolf/tests/data/asciidoc/expected/asciidoc_01.asciidoc.md +++ b/crates/fleischwolf/tests/data/asciidoc/expected/asciidoc_01.asciidoc.md @@ -21,7 +21,7 @@ This is some introductory text in section 1.1. This is some text in section 2. -| Header 1 | Header 2 | | -| - | - | - | -| Value 1 | Value 2 | | -| Value 3 | Value 4 | | +| Header 1 | Header 2 | | +|------------|------------|----| +| Value 1 | Value 2 | | +| Value 3 | Value 4 | | diff --git a/crates/fleischwolf/tests/data/asciidoc/expected/asciidoc_01.asciidoc.strict.md b/crates/fleischwolf/tests/data/asciidoc/expected/asciidoc_01.asciidoc.strict.md index d3403feb..691ecc0f 100644 --- a/crates/fleischwolf/tests/data/asciidoc/expected/asciidoc_01.asciidoc.strict.md +++ b/crates/fleischwolf/tests/data/asciidoc/expected/asciidoc_01.asciidoc.strict.md @@ -21,7 +21,7 @@ This is some introductory text in section 1.1. This is some text in section 2. -| Header 1 | Header 2 | | -| - | - | - | -| Value 1 | Value 2 | | -| Value 3 | Value 4 | | +| Header 1 | Header 2 | | +|------------|------------|----| +| Value 1 | Value 2 | | +| Value 3 | Value 4 | | diff --git a/crates/fleischwolf/tests/data/asciidoc/expected/asciidoc_02.asciidoc.md b/crates/fleischwolf/tests/data/asciidoc/expected/asciidoc_02.asciidoc.md index 352634a0..bbe03537 100644 --- a/crates/fleischwolf/tests/data/asciidoc/expected/asciidoc_02.asciidoc.md +++ b/crates/fleischwolf/tests/data/asciidoc/expected/asciidoc_02.asciidoc.md @@ -27,51 +27,51 @@ bla bla bla bli bla ble ## Section 4: test tables -| Header 1 | Header 2 | | -| - | - | - | -| Value 1 | Value 2 | | -| Value 3 | Value 4 | | +| Header 1 | Header 2 | | +|------------|------------|----| +| Value 1 | Value 2 | | +| Value 3 | Value 4 | | Caption for the table 1 -| Header 1 | Header 2 | -| - | - | -| Value 1 | Value 2 | -| Value 3 | Value 4 | +| Header 1 | Header 2 | +|------------|------------| +| Value 1 | Value 2 | +| Value 3 | Value 4 | Caption for the table 2 -| Column 1 Heading | Column 2 Heading | Column 3 Heading | -| - | - | - | -| Cell 1 | Cell 2 | Cell 3 | -| Cell 4 | Cell 5 colspan=2 | Cell spans two columns | +| Column 1 Heading | Column 2 Heading | Column 3 Heading | +|--------------------|--------------------|------------------------| +| Cell 1 | Cell 2 | Cell 3 | +| Cell 4 | Cell 5 colspan=2 | Cell spans two columns | Caption for the table 3 -| Column 1 Heading | Column 2 Heading | Column 3 Heading | -| - | - | - | -| Rowspan=2 | Cell 2 | Cell 3 | -| | Cell 5 | Cell 6 | +| Column 1 Heading | Column 2 Heading | Column 3 Heading | +|--------------------|--------------------|--------------------| +| Rowspan=2 | Cell 2 | Cell 3 | +| | Cell 5 | Cell 6 | Caption for the table 4 -| Col 1 | Col 2 | Col 3 | Col 4 | -| - | - | - | - | -| Rowspan=2.Colspan=2 | Cell spanning 2 rows and 2 columns | Col 3 | Col 4 | -| | | Col 3 | Col 4 | -| Col 1 | Col 2 | Col 3 | Col 4 | +| Col 1 | Col 2 | Col 3 | Col 4 | +|---------------------|------------------------------------|---------|---------| +| Rowspan=2.Colspan=2 | Cell spanning 2 rows and 2 columns | Col 3 | Col 4 | +| | | Col 3 | Col 4 | +| Col 1 | Col 2 | Col 3 | Col 4 | Table 5 with multiple empty cells -| Column 1 Heading | Column 2 Heading | Column 3 Heading | | -| - | - | - | - | -| Cell 1 | | Cell 3 | | -| Cell 4 | | | | -| Cell 7 | | | | -| Cell 10 | Cell 11 | Cell 12 | | -| | Cell 14 | Cell 15 | | -| | | | | -| Cell 19 | Cell 20 | Cell 21 | | +| Column 1 Heading | Column 2 Heading | Column 3 Heading | | +|--------------------|--------------------|--------------------|----| +| Cell 1 | | Cell 3 | | +| Cell 4 | | | | +| Cell 7 | | | | +| Cell 10 | Cell 11 | Cell 12 | | +| | Cell 14 | Cell 15 | | +| | | | | +| Cell 19 | Cell 20 | Cell 21 | | #### SubSubSection 2.1.1 diff --git a/crates/fleischwolf/tests/data/asciidoc/expected/asciidoc_02.asciidoc.strict.md b/crates/fleischwolf/tests/data/asciidoc/expected/asciidoc_02.asciidoc.strict.md index 352634a0..bbe03537 100644 --- a/crates/fleischwolf/tests/data/asciidoc/expected/asciidoc_02.asciidoc.strict.md +++ b/crates/fleischwolf/tests/data/asciidoc/expected/asciidoc_02.asciidoc.strict.md @@ -27,51 +27,51 @@ bla bla bla bli bla ble ## Section 4: test tables -| Header 1 | Header 2 | | -| - | - | - | -| Value 1 | Value 2 | | -| Value 3 | Value 4 | | +| Header 1 | Header 2 | | +|------------|------------|----| +| Value 1 | Value 2 | | +| Value 3 | Value 4 | | Caption for the table 1 -| Header 1 | Header 2 | -| - | - | -| Value 1 | Value 2 | -| Value 3 | Value 4 | +| Header 1 | Header 2 | +|------------|------------| +| Value 1 | Value 2 | +| Value 3 | Value 4 | Caption for the table 2 -| Column 1 Heading | Column 2 Heading | Column 3 Heading | -| - | - | - | -| Cell 1 | Cell 2 | Cell 3 | -| Cell 4 | Cell 5 colspan=2 | Cell spans two columns | +| Column 1 Heading | Column 2 Heading | Column 3 Heading | +|--------------------|--------------------|------------------------| +| Cell 1 | Cell 2 | Cell 3 | +| Cell 4 | Cell 5 colspan=2 | Cell spans two columns | Caption for the table 3 -| Column 1 Heading | Column 2 Heading | Column 3 Heading | -| - | - | - | -| Rowspan=2 | Cell 2 | Cell 3 | -| | Cell 5 | Cell 6 | +| Column 1 Heading | Column 2 Heading | Column 3 Heading | +|--------------------|--------------------|--------------------| +| Rowspan=2 | Cell 2 | Cell 3 | +| | Cell 5 | Cell 6 | Caption for the table 4 -| Col 1 | Col 2 | Col 3 | Col 4 | -| - | - | - | - | -| Rowspan=2.Colspan=2 | Cell spanning 2 rows and 2 columns | Col 3 | Col 4 | -| | | Col 3 | Col 4 | -| Col 1 | Col 2 | Col 3 | Col 4 | +| Col 1 | Col 2 | Col 3 | Col 4 | +|---------------------|------------------------------------|---------|---------| +| Rowspan=2.Colspan=2 | Cell spanning 2 rows and 2 columns | Col 3 | Col 4 | +| | | Col 3 | Col 4 | +| Col 1 | Col 2 | Col 3 | Col 4 | Table 5 with multiple empty cells -| Column 1 Heading | Column 2 Heading | Column 3 Heading | | -| - | - | - | - | -| Cell 1 | | Cell 3 | | -| Cell 4 | | | | -| Cell 7 | | | | -| Cell 10 | Cell 11 | Cell 12 | | -| | Cell 14 | Cell 15 | | -| | | | | -| Cell 19 | Cell 20 | Cell 21 | | +| Column 1 Heading | Column 2 Heading | Column 3 Heading | | +|--------------------|--------------------|--------------------|----| +| Cell 1 | | Cell 3 | | +| Cell 4 | | | | +| Cell 7 | | | | +| Cell 10 | Cell 11 | Cell 12 | | +| | Cell 14 | Cell 15 | | +| | | | | +| Cell 19 | Cell 20 | Cell 21 | | #### SubSubSection 2.1.1 diff --git a/crates/fleischwolf/tests/data/asciidoc/expected/asciidoc_04.asciidoc.md b/crates/fleischwolf/tests/data/asciidoc/expected/asciidoc_04.asciidoc.md index a11ba396..7b770137 100644 --- a/crates/fleischwolf/tests/data/asciidoc/expected/asciidoc_04.asciidoc.md +++ b/crates/fleischwolf/tests/data/asciidoc/expected/asciidoc_04.asciidoc.md @@ -2,17 +2,17 @@ A table whose header row uses alignment and style specifiers on every cell. -| Field | Description | -| - | - | -| a | First column value | -| b | Second column value | +| Field | Description | +|---------|---------------------| +| a | First column value | +| b | Second column value | [%autowidth, cols="^.^40,<.^60"] A table with single-letter cells that collide with style operators. -| Code | Name | -| - | - | -| s | Strong | -| h | Header | -| m | Monospace | +| Code | Name | +|--------|-----------| +| s | Strong | +| h | Header | +| m | Monospace | diff --git a/crates/fleischwolf/tests/data/asciidoc/expected/asciidoc_04.asciidoc.strict.md b/crates/fleischwolf/tests/data/asciidoc/expected/asciidoc_04.asciidoc.strict.md index a11ba396..7b770137 100644 --- a/crates/fleischwolf/tests/data/asciidoc/expected/asciidoc_04.asciidoc.strict.md +++ b/crates/fleischwolf/tests/data/asciidoc/expected/asciidoc_04.asciidoc.strict.md @@ -2,17 +2,17 @@ A table whose header row uses alignment and style specifiers on every cell. -| Field | Description | -| - | - | -| a | First column value | -| b | Second column value | +| Field | Description | +|---------|---------------------| +| a | First column value | +| b | Second column value | [%autowidth, cols="^.^40,<.^60"] A table with single-letter cells that collide with style operators. -| Code | Name | -| - | - | -| s | Strong | -| h | Header | -| m | Monospace | +| Code | Name | +|--------|-----------| +| s | Strong | +| h | Header | +| m | Monospace | diff --git a/crates/fleischwolf/tests/data/csv/expected/csv-comma-in-cell.csv.md b/crates/fleischwolf/tests/data/csv/expected/csv-comma-in-cell.csv.md index d31acf48..d21c9f84 100644 --- a/crates/fleischwolf/tests/data/csv/expected/csv-comma-in-cell.csv.md +++ b/crates/fleischwolf/tests/data/csv/expected/csv-comma-in-cell.csv.md @@ -1,6 +1,6 @@ -| 1 | 2 | 3 | 4 | -| - | - | - | - | -| a | b | c | d | -| a | , | c | d | -| a | b | c | d | -| a | b | c | d | +| 1 | 2 | 3 | 4 | +|-----|-----|-----|-----| +| a | b | c | d | +| a | , | c | d | +| a | b | c | d | +| a | b | c | d | diff --git a/crates/fleischwolf/tests/data/csv/expected/csv-comma-in-cell.csv.strict.md b/crates/fleischwolf/tests/data/csv/expected/csv-comma-in-cell.csv.strict.md index d31acf48..d21c9f84 100644 --- a/crates/fleischwolf/tests/data/csv/expected/csv-comma-in-cell.csv.strict.md +++ b/crates/fleischwolf/tests/data/csv/expected/csv-comma-in-cell.csv.strict.md @@ -1,6 +1,6 @@ -| 1 | 2 | 3 | 4 | -| - | - | - | - | -| a | b | c | d | -| a | , | c | d | -| a | b | c | d | -| a | b | c | d | +| 1 | 2 | 3 | 4 | +|-----|-----|-----|-----| +| a | b | c | d | +| a | , | c | d | +| a | b | c | d | +| a | b | c | d | diff --git a/crates/fleischwolf/tests/data/csv/expected/csv-comma.csv.md b/crates/fleischwolf/tests/data/csv/expected/csv-comma.csv.md index e7d4dfa3..bf37c6e7 100644 --- a/crates/fleischwolf/tests/data/csv/expected/csv-comma.csv.md +++ b/crates/fleischwolf/tests/data/csv/expected/csv-comma.csv.md @@ -1,7 +1,7 @@ -| Index | Customer Id | First Name | Last Name | Company | City | Country | Phone 1 | Phone 2 | Email | Subscription Date | Website | -| - | - | - | - | - | - | - | - | - | - | - | - | -| 1 | DD37Cf93aecA6Dc | Sheryl | Baxter | Rasmussen Group | East Leonard | Chile | 229.077.5154 | 397.884.0519x718 | zunigavanessa@smith.info | 2020-08-24 | http://www.stephenson.com/ | -| 2 | 1Ef7b82A4CAAD10 | Preston | Lozano, Dr | Vega-Gentry | East Jimmychester | Djibouti | 5153435776 | 686-620-1820x944 | vmata@colon.com | 2021-04-23 | http://www.hobbs.com/ | -| 3 | 6F94879bDAfE5a6 | Roy | Berry | Murillo-Perry | Isabelborough | Antigua and Barbuda | +1-539-402-0259 | (496)978-3969x58947 | beckycarr@hogan.com | 2020-03-25 | http://www.lawrence.com/ | -| 4 | 5Cef8BFA16c5e3c | Linda | Olsen | Dominguez, Mcmillan and Donovan | Bensonview | Dominican Republic | 001-808-617-6467x12895 | +1-813-324-8756 | stanleyblackwell@benson.org | 2020-06-02 | http://www.good-lyons.com/ | -| 5 | 053d585Ab6b3159 | Joanna | Bender | Martin, Lang and Andrade | West Priscilla | Slovakia (Slovak Republic) | 001-234-203-0635x76146 | 001-199-446-3860x3486 | colinalvarado@miles.net | 2021-04-17 | https://goodwin-ingram.com/ | +| Index | Customer Id | First Name | Last Name | Company | City | Country | Phone 1 | Phone 2 | Email | Subscription Date | Website | +|---------|-----------------|--------------|-------------|---------------------------------|-------------------|----------------------------|------------------------|-----------------------|-----------------------------|---------------------|-----------------------------| +| 1 | DD37Cf93aecA6Dc | Sheryl | Baxter | Rasmussen Group | East Leonard | Chile | 229.077.5154 | 397.884.0519x718 | zunigavanessa@smith.info | 2020-08-24 | http://www.stephenson.com/ | +| 2 | 1Ef7b82A4CAAD10 | Preston | Lozano, Dr | Vega-Gentry | East Jimmychester | Djibouti | 5153435776 | 686-620-1820x944 | vmata@colon.com | 2021-04-23 | http://www.hobbs.com/ | +| 3 | 6F94879bDAfE5a6 | Roy | Berry | Murillo-Perry | Isabelborough | Antigua and Barbuda | +1-539-402-0259 | (496)978-3969x58947 | beckycarr@hogan.com | 2020-03-25 | http://www.lawrence.com/ | +| 4 | 5Cef8BFA16c5e3c | Linda | Olsen | Dominguez, Mcmillan and Donovan | Bensonview | Dominican Republic | 001-808-617-6467x12895 | +1-813-324-8756 | stanleyblackwell@benson.org | 2020-06-02 | http://www.good-lyons.com/ | +| 5 | 053d585Ab6b3159 | Joanna | Bender | Martin, Lang and Andrade | West Priscilla | Slovakia (Slovak Republic) | 001-234-203-0635x76146 | 001-199-446-3860x3486 | colinalvarado@miles.net | 2021-04-17 | https://goodwin-ingram.com/ | diff --git a/crates/fleischwolf/tests/data/csv/expected/csv-comma.csv.strict.md b/crates/fleischwolf/tests/data/csv/expected/csv-comma.csv.strict.md index e7d4dfa3..bf37c6e7 100644 --- a/crates/fleischwolf/tests/data/csv/expected/csv-comma.csv.strict.md +++ b/crates/fleischwolf/tests/data/csv/expected/csv-comma.csv.strict.md @@ -1,7 +1,7 @@ -| Index | Customer Id | First Name | Last Name | Company | City | Country | Phone 1 | Phone 2 | Email | Subscription Date | Website | -| - | - | - | - | - | - | - | - | - | - | - | - | -| 1 | DD37Cf93aecA6Dc | Sheryl | Baxter | Rasmussen Group | East Leonard | Chile | 229.077.5154 | 397.884.0519x718 | zunigavanessa@smith.info | 2020-08-24 | http://www.stephenson.com/ | -| 2 | 1Ef7b82A4CAAD10 | Preston | Lozano, Dr | Vega-Gentry | East Jimmychester | Djibouti | 5153435776 | 686-620-1820x944 | vmata@colon.com | 2021-04-23 | http://www.hobbs.com/ | -| 3 | 6F94879bDAfE5a6 | Roy | Berry | Murillo-Perry | Isabelborough | Antigua and Barbuda | +1-539-402-0259 | (496)978-3969x58947 | beckycarr@hogan.com | 2020-03-25 | http://www.lawrence.com/ | -| 4 | 5Cef8BFA16c5e3c | Linda | Olsen | Dominguez, Mcmillan and Donovan | Bensonview | Dominican Republic | 001-808-617-6467x12895 | +1-813-324-8756 | stanleyblackwell@benson.org | 2020-06-02 | http://www.good-lyons.com/ | -| 5 | 053d585Ab6b3159 | Joanna | Bender | Martin, Lang and Andrade | West Priscilla | Slovakia (Slovak Republic) | 001-234-203-0635x76146 | 001-199-446-3860x3486 | colinalvarado@miles.net | 2021-04-17 | https://goodwin-ingram.com/ | +| Index | Customer Id | First Name | Last Name | Company | City | Country | Phone 1 | Phone 2 | Email | Subscription Date | Website | +|---------|-----------------|--------------|-------------|---------------------------------|-------------------|----------------------------|------------------------|-----------------------|-----------------------------|---------------------|-----------------------------| +| 1 | DD37Cf93aecA6Dc | Sheryl | Baxter | Rasmussen Group | East Leonard | Chile | 229.077.5154 | 397.884.0519x718 | zunigavanessa@smith.info | 2020-08-24 | http://www.stephenson.com/ | +| 2 | 1Ef7b82A4CAAD10 | Preston | Lozano, Dr | Vega-Gentry | East Jimmychester | Djibouti | 5153435776 | 686-620-1820x944 | vmata@colon.com | 2021-04-23 | http://www.hobbs.com/ | +| 3 | 6F94879bDAfE5a6 | Roy | Berry | Murillo-Perry | Isabelborough | Antigua and Barbuda | +1-539-402-0259 | (496)978-3969x58947 | beckycarr@hogan.com | 2020-03-25 | http://www.lawrence.com/ | +| 4 | 5Cef8BFA16c5e3c | Linda | Olsen | Dominguez, Mcmillan and Donovan | Bensonview | Dominican Republic | 001-808-617-6467x12895 | +1-813-324-8756 | stanleyblackwell@benson.org | 2020-06-02 | http://www.good-lyons.com/ | +| 5 | 053d585Ab6b3159 | Joanna | Bender | Martin, Lang and Andrade | West Priscilla | Slovakia (Slovak Republic) | 001-234-203-0635x76146 | 001-199-446-3860x3486 | colinalvarado@miles.net | 2021-04-17 | https://goodwin-ingram.com/ | diff --git a/crates/fleischwolf/tests/data/csv/expected/csv-inconsistent-header.csv.md b/crates/fleischwolf/tests/data/csv/expected/csv-inconsistent-header.csv.md index da72b244..711f900c 100644 --- a/crates/fleischwolf/tests/data/csv/expected/csv-inconsistent-header.csv.md +++ b/crates/fleischwolf/tests/data/csv/expected/csv-inconsistent-header.csv.md @@ -1,6 +1,6 @@ -| 1 | 2 | 3 | | -| - | - | - | - | -| a | b | c | d | -| a | b | c | d | -| a | b | c | d | -| a | b | c | d | +| 1 | 2 | 3 | | +|-----|-----|-----|----| +| a | b | c | d | +| a | b | c | d | +| a | b | c | d | +| a | b | c | d | diff --git a/crates/fleischwolf/tests/data/csv/expected/csv-inconsistent-header.csv.strict.md b/crates/fleischwolf/tests/data/csv/expected/csv-inconsistent-header.csv.strict.md index da72b244..711f900c 100644 --- a/crates/fleischwolf/tests/data/csv/expected/csv-inconsistent-header.csv.strict.md +++ b/crates/fleischwolf/tests/data/csv/expected/csv-inconsistent-header.csv.strict.md @@ -1,6 +1,6 @@ -| 1 | 2 | 3 | | -| - | - | - | - | -| a | b | c | d | -| a | b | c | d | -| a | b | c | d | -| a | b | c | d | +| 1 | 2 | 3 | | +|-----|-----|-----|----| +| a | b | c | d | +| a | b | c | d | +| a | b | c | d | +| a | b | c | d | diff --git a/crates/fleischwolf/tests/data/csv/expected/csv-pipe.csv.md b/crates/fleischwolf/tests/data/csv/expected/csv-pipe.csv.md index 2db782bb..02fd47ea 100644 --- a/crates/fleischwolf/tests/data/csv/expected/csv-pipe.csv.md +++ b/crates/fleischwolf/tests/data/csv/expected/csv-pipe.csv.md @@ -1,7 +1,7 @@ -| Index | Customer Id | First Name | Last Name | Company | City | Country | Phone 1 | Phone 2 | Email | Subscription Date | Website | -| - | - | - | - | - | - | - | - | - | - | - | - | -| 1 | DD37Cf93aecA6Dc | Sheryl | Baxter | Rasmussen Group | East Leonard | Chile | 229.077.5154 | 397.884.0519x718 | zunigavanessa@smith.info | 2020-08-24 | http://www.stephenson.com/ | -| 2 | 1Ef7b82A4CAAD10 | Preston | Lozano | Vega-Gentry | East Jimmychester | Djibouti | 5153435776 | 686-620-1820x944 | vmata@colon.com | 2021-04-23 | http://www.hobbs.com/ | -| 3 | 6F94879bDAfE5a6 | Roy | Berry | Murillo-Perry | Isabelborough | Antigua and Barbuda | +1-539-402-0259 | (496)978-3969x58947 | beckycarr@hogan.com | 2020-03-25 | http://www.lawrence.com/ | -| 4 | 5Cef8BFA16c5e3c | Linda | Olsen | Dominguez|Mcmillan and Donovan | Bensonview | Dominican Republic | 001-808-617-6467x12895 | +1-813-324-8756 | stanleyblackwell@benson.org | 2020-06-02 | http://www.good-lyons.com/ | -| 5 | 053d585Ab6b3159 | Joanna | Bender | Martin|Lang and Andrade | West Priscilla | Slovakia (Slovak Republic) | 001-234-203-0635x76146 | 001-199-446-3860x3486 | colinalvarado@miles.net | 2021-04-17 | https://goodwin-ingram.com/ | +| Index | Customer Id | First Name | Last Name | Company | City | Country | Phone 1 | Phone 2 | Email | Subscription Date | Website | +|---------|-----------------|--------------|-------------|-------------------------------------|-------------------|----------------------------|------------------------|-----------------------|-----------------------------|---------------------|-----------------------------| +| 1 | DD37Cf93aecA6Dc | Sheryl | Baxter | Rasmussen Group | East Leonard | Chile | 229.077.5154 | 397.884.0519x718 | zunigavanessa@smith.info | 2020-08-24 | http://www.stephenson.com/ | +| 2 | 1Ef7b82A4CAAD10 | Preston | Lozano | Vega-Gentry | East Jimmychester | Djibouti | 5153435776 | 686-620-1820x944 | vmata@colon.com | 2021-04-23 | http://www.hobbs.com/ | +| 3 | 6F94879bDAfE5a6 | Roy | Berry | Murillo-Perry | Isabelborough | Antigua and Barbuda | +1-539-402-0259 | (496)978-3969x58947 | beckycarr@hogan.com | 2020-03-25 | http://www.lawrence.com/ | +| 4 | 5Cef8BFA16c5e3c | Linda | Olsen | Dominguez|Mcmillan and Donovan | Bensonview | Dominican Republic | 001-808-617-6467x12895 | +1-813-324-8756 | stanleyblackwell@benson.org | 2020-06-02 | http://www.good-lyons.com/ | +| 5 | 053d585Ab6b3159 | Joanna | Bender | Martin|Lang and Andrade | West Priscilla | Slovakia (Slovak Republic) | 001-234-203-0635x76146 | 001-199-446-3860x3486 | colinalvarado@miles.net | 2021-04-17 | https://goodwin-ingram.com/ | diff --git a/crates/fleischwolf/tests/data/csv/expected/csv-pipe.csv.strict.md b/crates/fleischwolf/tests/data/csv/expected/csv-pipe.csv.strict.md index 2db782bb..02fd47ea 100644 --- a/crates/fleischwolf/tests/data/csv/expected/csv-pipe.csv.strict.md +++ b/crates/fleischwolf/tests/data/csv/expected/csv-pipe.csv.strict.md @@ -1,7 +1,7 @@ -| Index | Customer Id | First Name | Last Name | Company | City | Country | Phone 1 | Phone 2 | Email | Subscription Date | Website | -| - | - | - | - | - | - | - | - | - | - | - | - | -| 1 | DD37Cf93aecA6Dc | Sheryl | Baxter | Rasmussen Group | East Leonard | Chile | 229.077.5154 | 397.884.0519x718 | zunigavanessa@smith.info | 2020-08-24 | http://www.stephenson.com/ | -| 2 | 1Ef7b82A4CAAD10 | Preston | Lozano | Vega-Gentry | East Jimmychester | Djibouti | 5153435776 | 686-620-1820x944 | vmata@colon.com | 2021-04-23 | http://www.hobbs.com/ | -| 3 | 6F94879bDAfE5a6 | Roy | Berry | Murillo-Perry | Isabelborough | Antigua and Barbuda | +1-539-402-0259 | (496)978-3969x58947 | beckycarr@hogan.com | 2020-03-25 | http://www.lawrence.com/ | -| 4 | 5Cef8BFA16c5e3c | Linda | Olsen | Dominguez|Mcmillan and Donovan | Bensonview | Dominican Republic | 001-808-617-6467x12895 | +1-813-324-8756 | stanleyblackwell@benson.org | 2020-06-02 | http://www.good-lyons.com/ | -| 5 | 053d585Ab6b3159 | Joanna | Bender | Martin|Lang and Andrade | West Priscilla | Slovakia (Slovak Republic) | 001-234-203-0635x76146 | 001-199-446-3860x3486 | colinalvarado@miles.net | 2021-04-17 | https://goodwin-ingram.com/ | +| Index | Customer Id | First Name | Last Name | Company | City | Country | Phone 1 | Phone 2 | Email | Subscription Date | Website | +|---------|-----------------|--------------|-------------|-------------------------------------|-------------------|----------------------------|------------------------|-----------------------|-----------------------------|---------------------|-----------------------------| +| 1 | DD37Cf93aecA6Dc | Sheryl | Baxter | Rasmussen Group | East Leonard | Chile | 229.077.5154 | 397.884.0519x718 | zunigavanessa@smith.info | 2020-08-24 | http://www.stephenson.com/ | +| 2 | 1Ef7b82A4CAAD10 | Preston | Lozano | Vega-Gentry | East Jimmychester | Djibouti | 5153435776 | 686-620-1820x944 | vmata@colon.com | 2021-04-23 | http://www.hobbs.com/ | +| 3 | 6F94879bDAfE5a6 | Roy | Berry | Murillo-Perry | Isabelborough | Antigua and Barbuda | +1-539-402-0259 | (496)978-3969x58947 | beckycarr@hogan.com | 2020-03-25 | http://www.lawrence.com/ | +| 4 | 5Cef8BFA16c5e3c | Linda | Olsen | Dominguez|Mcmillan and Donovan | Bensonview | Dominican Republic | 001-808-617-6467x12895 | +1-813-324-8756 | stanleyblackwell@benson.org | 2020-06-02 | http://www.good-lyons.com/ | +| 5 | 053d585Ab6b3159 | Joanna | Bender | Martin|Lang and Andrade | West Priscilla | Slovakia (Slovak Republic) | 001-234-203-0635x76146 | 001-199-446-3860x3486 | colinalvarado@miles.net | 2021-04-17 | https://goodwin-ingram.com/ | diff --git a/crates/fleischwolf/tests/data/csv/expected/csv-semicolon.csv.md b/crates/fleischwolf/tests/data/csv/expected/csv-semicolon.csv.md index ca51e6bf..f1471593 100644 --- a/crates/fleischwolf/tests/data/csv/expected/csv-semicolon.csv.md +++ b/crates/fleischwolf/tests/data/csv/expected/csv-semicolon.csv.md @@ -1,7 +1,7 @@ -| Index | Customer Id | First Name | Last Name | Company | City | Country | Phone 1 | Phone 2 | Email | Subscription Date | Website | -| - | - | - | - | - | - | - | - | - | - | - | - | -| 1 | DD37Cf93aecA6Dc | Sheryl | Baxter | Rasmussen Group | East Leonard | Chile | 229.077.5154 | 397.884.0519x718 | zunigavanessa@smith.info | 2020-08-24 | http://www.stephenson.com/ | -| 2 | 1Ef7b82A4CAAD10 | Preston | Lozano | Vega-Gentry | East Jimmychester | Djibouti | 5153435776 | 686-620-1820x944 | vmata@colon.com | 2021-04-23 | http://www.hobbs.com/ | -| 3 | 6F94879bDAfE5a6 | Roy | Berry | Murillo-Perry | Isabelborough | Antigua and Barbuda | +1-539-402-0259 | (496)978-3969x58947 | beckycarr@hogan.com | 2020-03-25 | http://www.lawrence.com/ | -| 4 | 5Cef8BFA16c5e3c | Linda | Olsen | Dominguez;Mcmillan and Donovan | Bensonview | Dominican Republic | 001-808-617-6467x12895 | +1-813-324-8756 | stanleyblackwell@benson.org | 2020-06-02 | http://www.good-lyons.com/ | -| 5 | 053d585Ab6b3159 | Joanna | Bender | Martin;Lang and Andrade | West Priscilla | Slovakia (Slovak Republic) | 001-234-203-0635x76146 | 001-199-446-3860x3486 | colinalvarado@miles.net | 2021-04-17 | https://goodwin-ingram.com/ | +| Index | Customer Id | First Name | Last Name | Company | City | Country | Phone 1 | Phone 2 | Email | Subscription Date | Website | +|---------|-----------------|--------------|-------------|--------------------------------|-------------------|----------------------------|------------------------|-----------------------|-----------------------------|---------------------|-----------------------------| +| 1 | DD37Cf93aecA6Dc | Sheryl | Baxter | Rasmussen Group | East Leonard | Chile | 229.077.5154 | 397.884.0519x718 | zunigavanessa@smith.info | 2020-08-24 | http://www.stephenson.com/ | +| 2 | 1Ef7b82A4CAAD10 | Preston | Lozano | Vega-Gentry | East Jimmychester | Djibouti | 5153435776 | 686-620-1820x944 | vmata@colon.com | 2021-04-23 | http://www.hobbs.com/ | +| 3 | 6F94879bDAfE5a6 | Roy | Berry | Murillo-Perry | Isabelborough | Antigua and Barbuda | +1-539-402-0259 | (496)978-3969x58947 | beckycarr@hogan.com | 2020-03-25 | http://www.lawrence.com/ | +| 4 | 5Cef8BFA16c5e3c | Linda | Olsen | Dominguez;Mcmillan and Donovan | Bensonview | Dominican Republic | 001-808-617-6467x12895 | +1-813-324-8756 | stanleyblackwell@benson.org | 2020-06-02 | http://www.good-lyons.com/ | +| 5 | 053d585Ab6b3159 | Joanna | Bender | Martin;Lang and Andrade | West Priscilla | Slovakia (Slovak Republic) | 001-234-203-0635x76146 | 001-199-446-3860x3486 | colinalvarado@miles.net | 2021-04-17 | https://goodwin-ingram.com/ | diff --git a/crates/fleischwolf/tests/data/csv/expected/csv-semicolon.csv.strict.md b/crates/fleischwolf/tests/data/csv/expected/csv-semicolon.csv.strict.md index ca51e6bf..f1471593 100644 --- a/crates/fleischwolf/tests/data/csv/expected/csv-semicolon.csv.strict.md +++ b/crates/fleischwolf/tests/data/csv/expected/csv-semicolon.csv.strict.md @@ -1,7 +1,7 @@ -| Index | Customer Id | First Name | Last Name | Company | City | Country | Phone 1 | Phone 2 | Email | Subscription Date | Website | -| - | - | - | - | - | - | - | - | - | - | - | - | -| 1 | DD37Cf93aecA6Dc | Sheryl | Baxter | Rasmussen Group | East Leonard | Chile | 229.077.5154 | 397.884.0519x718 | zunigavanessa@smith.info | 2020-08-24 | http://www.stephenson.com/ | -| 2 | 1Ef7b82A4CAAD10 | Preston | Lozano | Vega-Gentry | East Jimmychester | Djibouti | 5153435776 | 686-620-1820x944 | vmata@colon.com | 2021-04-23 | http://www.hobbs.com/ | -| 3 | 6F94879bDAfE5a6 | Roy | Berry | Murillo-Perry | Isabelborough | Antigua and Barbuda | +1-539-402-0259 | (496)978-3969x58947 | beckycarr@hogan.com | 2020-03-25 | http://www.lawrence.com/ | -| 4 | 5Cef8BFA16c5e3c | Linda | Olsen | Dominguez;Mcmillan and Donovan | Bensonview | Dominican Republic | 001-808-617-6467x12895 | +1-813-324-8756 | stanleyblackwell@benson.org | 2020-06-02 | http://www.good-lyons.com/ | -| 5 | 053d585Ab6b3159 | Joanna | Bender | Martin;Lang and Andrade | West Priscilla | Slovakia (Slovak Republic) | 001-234-203-0635x76146 | 001-199-446-3860x3486 | colinalvarado@miles.net | 2021-04-17 | https://goodwin-ingram.com/ | +| Index | Customer Id | First Name | Last Name | Company | City | Country | Phone 1 | Phone 2 | Email | Subscription Date | Website | +|---------|-----------------|--------------|-------------|--------------------------------|-------------------|----------------------------|------------------------|-----------------------|-----------------------------|---------------------|-----------------------------| +| 1 | DD37Cf93aecA6Dc | Sheryl | Baxter | Rasmussen Group | East Leonard | Chile | 229.077.5154 | 397.884.0519x718 | zunigavanessa@smith.info | 2020-08-24 | http://www.stephenson.com/ | +| 2 | 1Ef7b82A4CAAD10 | Preston | Lozano | Vega-Gentry | East Jimmychester | Djibouti | 5153435776 | 686-620-1820x944 | vmata@colon.com | 2021-04-23 | http://www.hobbs.com/ | +| 3 | 6F94879bDAfE5a6 | Roy | Berry | Murillo-Perry | Isabelborough | Antigua and Barbuda | +1-539-402-0259 | (496)978-3969x58947 | beckycarr@hogan.com | 2020-03-25 | http://www.lawrence.com/ | +| 4 | 5Cef8BFA16c5e3c | Linda | Olsen | Dominguez;Mcmillan and Donovan | Bensonview | Dominican Republic | 001-808-617-6467x12895 | +1-813-324-8756 | stanleyblackwell@benson.org | 2020-06-02 | http://www.good-lyons.com/ | +| 5 | 053d585Ab6b3159 | Joanna | Bender | Martin;Lang and Andrade | West Priscilla | Slovakia (Slovak Republic) | 001-234-203-0635x76146 | 001-199-446-3860x3486 | colinalvarado@miles.net | 2021-04-17 | https://goodwin-ingram.com/ | diff --git a/crates/fleischwolf/tests/data/csv/expected/csv-single-column.csv.md b/crates/fleischwolf/tests/data/csv/expected/csv-single-column.csv.md index 1e329f7c..131363aa 100644 --- a/crates/fleischwolf/tests/data/csv/expected/csv-single-column.csv.md +++ b/crates/fleischwolf/tests/data/csv/expected/csv-single-column.csv.md @@ -1,8 +1,8 @@ -| Industry | -| - | +| Industry | +|--------------------| | Accounting/Finance | -| Automotive | -| Healthcare | +| Automotive | +| Healthcare | | Hospitality/Travel | -| Sales | -| Other | +| Sales | +| Other | diff --git a/crates/fleischwolf/tests/data/csv/expected/csv-single-column.csv.strict.md b/crates/fleischwolf/tests/data/csv/expected/csv-single-column.csv.strict.md index 1e329f7c..131363aa 100644 --- a/crates/fleischwolf/tests/data/csv/expected/csv-single-column.csv.strict.md +++ b/crates/fleischwolf/tests/data/csv/expected/csv-single-column.csv.strict.md @@ -1,8 +1,8 @@ -| Industry | -| - | +| Industry | +|--------------------| | Accounting/Finance | -| Automotive | -| Healthcare | +| Automotive | +| Healthcare | | Hospitality/Travel | -| Sales | -| Other | +| Sales | +| Other | diff --git a/crates/fleischwolf/tests/data/csv/expected/csv-tab.csv.md b/crates/fleischwolf/tests/data/csv/expected/csv-tab.csv.md index f6c456a2..5c8dfe5f 100644 --- a/crates/fleischwolf/tests/data/csv/expected/csv-tab.csv.md +++ b/crates/fleischwolf/tests/data/csv/expected/csv-tab.csv.md @@ -1,7 +1,7 @@ -| Index | Customer Id | First Name | Last Name | Company | City | Country | Phone 1 | Phone 2 | Email | Subscription Date | Website | -| - | - | - | - | - | - | - | - | - | - | - | - | -| 1 | DD37Cf93aecA6Dc | Sheryl | Baxter | Rasmussen Group | East Leonard | Chile | 229.077.5154 | 397.884.0519x718 | zunigavanessa@smith.info | 2020-08-24 | http://www.stephenson.com/ | -| 2 | 1Ef7b82A4CAAD10 | Preston | Lozano | Vega-Gentry | East Jimmychester | Djibouti | 5153435776 | 686-620-1820x944 | vmata@colon.com | 2021-04-23 | http://www.hobbs.com/ | -| 3 | 6F94879bDAfE5a6 | Roy | Berry | Murillo-Perry | Isabelborough | Antigua and Barbuda | +1-539-402-0259 | (496)978-3969x58947 | beckycarr@hogan.com | 2020-03-25 | http://www.lawrence.com/ | -| 4 | 5Cef8BFA16c5e3c | Linda | Olsen | Dominguez Mcmillan and Donovan | Bensonview | Dominican Republic | 001-808-617-6467x12895 | +1-813-324-8756 | stanleyblackwell@benson.org | 2020-06-02 | http://www.good-lyons.com/ | -| 5 | 053d585Ab6b3159 | Joanna | Bender | Martin Lang and Andrade | West Priscilla | Slovakia (Slovak Republic) | 001-234-203-0635x76146 | 001-199-446-3860x3486 | colinalvarado@miles.net | 2021-04-17 | https://goodwin-ingram.com/ | +| Index | Customer Id | First Name | Last Name | Company | City | Country | Phone 1 | Phone 2 | Email | Subscription Date | Website | +|---------|-----------------|--------------|-------------|--------------------------------|-------------------|----------------------------|------------------------|-----------------------|-----------------------------|---------------------|-----------------------------| +| 1 | DD37Cf93aecA6Dc | Sheryl | Baxter | Rasmussen Group | East Leonard | Chile | 229.077.5154 | 397.884.0519x718 | zunigavanessa@smith.info | 2020-08-24 | http://www.stephenson.com/ | +| 2 | 1Ef7b82A4CAAD10 | Preston | Lozano | Vega-Gentry | East Jimmychester | Djibouti | 5153435776 | 686-620-1820x944 | vmata@colon.com | 2021-04-23 | http://www.hobbs.com/ | +| 3 | 6F94879bDAfE5a6 | Roy | Berry | Murillo-Perry | Isabelborough | Antigua and Barbuda | +1-539-402-0259 | (496)978-3969x58947 | beckycarr@hogan.com | 2020-03-25 | http://www.lawrence.com/ | +| 4 | 5Cef8BFA16c5e3c | Linda | Olsen | Dominguez Mcmillan and Donovan | Bensonview | Dominican Republic | 001-808-617-6467x12895 | +1-813-324-8756 | stanleyblackwell@benson.org | 2020-06-02 | http://www.good-lyons.com/ | +| 5 | 053d585Ab6b3159 | Joanna | Bender | Martin Lang and Andrade | West Priscilla | Slovakia (Slovak Republic) | 001-234-203-0635x76146 | 001-199-446-3860x3486 | colinalvarado@miles.net | 2021-04-17 | https://goodwin-ingram.com/ | diff --git a/crates/fleischwolf/tests/data/csv/expected/csv-tab.csv.strict.md b/crates/fleischwolf/tests/data/csv/expected/csv-tab.csv.strict.md index f6c456a2..5c8dfe5f 100644 --- a/crates/fleischwolf/tests/data/csv/expected/csv-tab.csv.strict.md +++ b/crates/fleischwolf/tests/data/csv/expected/csv-tab.csv.strict.md @@ -1,7 +1,7 @@ -| Index | Customer Id | First Name | Last Name | Company | City | Country | Phone 1 | Phone 2 | Email | Subscription Date | Website | -| - | - | - | - | - | - | - | - | - | - | - | - | -| 1 | DD37Cf93aecA6Dc | Sheryl | Baxter | Rasmussen Group | East Leonard | Chile | 229.077.5154 | 397.884.0519x718 | zunigavanessa@smith.info | 2020-08-24 | http://www.stephenson.com/ | -| 2 | 1Ef7b82A4CAAD10 | Preston | Lozano | Vega-Gentry | East Jimmychester | Djibouti | 5153435776 | 686-620-1820x944 | vmata@colon.com | 2021-04-23 | http://www.hobbs.com/ | -| 3 | 6F94879bDAfE5a6 | Roy | Berry | Murillo-Perry | Isabelborough | Antigua and Barbuda | +1-539-402-0259 | (496)978-3969x58947 | beckycarr@hogan.com | 2020-03-25 | http://www.lawrence.com/ | -| 4 | 5Cef8BFA16c5e3c | Linda | Olsen | Dominguez Mcmillan and Donovan | Bensonview | Dominican Republic | 001-808-617-6467x12895 | +1-813-324-8756 | stanleyblackwell@benson.org | 2020-06-02 | http://www.good-lyons.com/ | -| 5 | 053d585Ab6b3159 | Joanna | Bender | Martin Lang and Andrade | West Priscilla | Slovakia (Slovak Republic) | 001-234-203-0635x76146 | 001-199-446-3860x3486 | colinalvarado@miles.net | 2021-04-17 | https://goodwin-ingram.com/ | +| Index | Customer Id | First Name | Last Name | Company | City | Country | Phone 1 | Phone 2 | Email | Subscription Date | Website | +|---------|-----------------|--------------|-------------|--------------------------------|-------------------|----------------------------|------------------------|-----------------------|-----------------------------|---------------------|-----------------------------| +| 1 | DD37Cf93aecA6Dc | Sheryl | Baxter | Rasmussen Group | East Leonard | Chile | 229.077.5154 | 397.884.0519x718 | zunigavanessa@smith.info | 2020-08-24 | http://www.stephenson.com/ | +| 2 | 1Ef7b82A4CAAD10 | Preston | Lozano | Vega-Gentry | East Jimmychester | Djibouti | 5153435776 | 686-620-1820x944 | vmata@colon.com | 2021-04-23 | http://www.hobbs.com/ | +| 3 | 6F94879bDAfE5a6 | Roy | Berry | Murillo-Perry | Isabelborough | Antigua and Barbuda | +1-539-402-0259 | (496)978-3969x58947 | beckycarr@hogan.com | 2020-03-25 | http://www.lawrence.com/ | +| 4 | 5Cef8BFA16c5e3c | Linda | Olsen | Dominguez Mcmillan and Donovan | Bensonview | Dominican Republic | 001-808-617-6467x12895 | +1-813-324-8756 | stanleyblackwell@benson.org | 2020-06-02 | http://www.good-lyons.com/ | +| 5 | 053d585Ab6b3159 | Joanna | Bender | Martin Lang and Andrade | West Priscilla | Slovakia (Slovak Republic) | 001-234-203-0635x76146 | 001-199-446-3860x3486 | colinalvarado@miles.net | 2021-04-17 | https://goodwin-ingram.com/ | diff --git a/crates/fleischwolf/tests/data/csv/expected/csv-too-few-columns.csv.md b/crates/fleischwolf/tests/data/csv/expected/csv-too-few-columns.csv.md index 50ab7655..c77fc1cf 100644 --- a/crates/fleischwolf/tests/data/csv/expected/csv-too-few-columns.csv.md +++ b/crates/fleischwolf/tests/data/csv/expected/csv-too-few-columns.csv.md @@ -1,6 +1,6 @@ -| 1 | 2 | 3 | 4 | -| - | - | - | - | -| a | 'b' | c | d | -| a | b | c | | -| a | b | c | d | -| a | b | c | d | +| 1 | 2 | 3 | 4 | +|-----|-----|-----|-----| +| a | 'b' | c | d | +| a | b | c | | +| a | b | c | d | +| a | b | c | d | diff --git a/crates/fleischwolf/tests/data/csv/expected/csv-too-few-columns.csv.strict.md b/crates/fleischwolf/tests/data/csv/expected/csv-too-few-columns.csv.strict.md index 50ab7655..c77fc1cf 100644 --- a/crates/fleischwolf/tests/data/csv/expected/csv-too-few-columns.csv.strict.md +++ b/crates/fleischwolf/tests/data/csv/expected/csv-too-few-columns.csv.strict.md @@ -1,6 +1,6 @@ -| 1 | 2 | 3 | 4 | -| - | - | - | - | -| a | 'b' | c | d | -| a | b | c | | -| a | b | c | d | -| a | b | c | d | +| 1 | 2 | 3 | 4 | +|-----|-----|-----|-----| +| a | 'b' | c | d | +| a | b | c | | +| a | b | c | d | +| a | b | c | d | diff --git a/crates/fleischwolf/tests/data/csv/expected/csv-too-many-columns.csv.md b/crates/fleischwolf/tests/data/csv/expected/csv-too-many-columns.csv.md index 97ba637b..76c5322d 100644 --- a/crates/fleischwolf/tests/data/csv/expected/csv-too-many-columns.csv.md +++ b/crates/fleischwolf/tests/data/csv/expected/csv-too-many-columns.csv.md @@ -1,6 +1,6 @@ -| 1 | 2 | 3 | 4 | | -| - | - | - | - | - | -| a | b | c | d | | -| a | b | c | d | e | -| a | b | c | d | | -| a | b | c | d | | +| 1 | 2 | 3 | 4 | | +|-----|-----|-----|-----|----| +| a | b | c | d | | +| a | b | c | d | e | +| a | b | c | d | | +| a | b | c | d | | diff --git a/crates/fleischwolf/tests/data/csv/expected/csv-too-many-columns.csv.strict.md b/crates/fleischwolf/tests/data/csv/expected/csv-too-many-columns.csv.strict.md index 97ba637b..76c5322d 100644 --- a/crates/fleischwolf/tests/data/csv/expected/csv-too-many-columns.csv.strict.md +++ b/crates/fleischwolf/tests/data/csv/expected/csv-too-many-columns.csv.strict.md @@ -1,6 +1,6 @@ -| 1 | 2 | 3 | 4 | | -| - | - | - | - | - | -| a | b | c | d | | -| a | b | c | d | e | -| a | b | c | d | | -| a | b | c | d | | +| 1 | 2 | 3 | 4 | | +|-----|-----|-----|-----|----| +| a | b | c | d | | +| a | b | c | d | e | +| a | b | c | d | | +| a | b | c | d | | diff --git a/crates/fleischwolf/tests/data/docx/expected/docx_checkboxes.docx.md b/crates/fleischwolf/tests/data/docx/expected/docx_checkboxes.docx.md index fb336763..1b8b2196 100644 --- a/crates/fleischwolf/tests/data/docx/expected/docx_checkboxes.docx.md +++ b/crates/fleischwolf/tests/data/docx/expected/docx_checkboxes.docx.md @@ -1,10 +1,10 @@ ### Table with options -| **List of services** | -| - | -| Breakfast options | +| **List of services** | +|---------------------------------------------------------------------------------------------------| +| Breakfast options | | Choose as many as you like: - [x] Orange juice - [ ] Tea - [x] Coffee - [ ] Milk - [x] Water | -| Choose as many as you like: - [x] Scramble eggs - [ ] Porridge - [x] Bread - [x] Croissant | +| Choose as many as you like: - [x] Scramble eggs - [ ] Porridge - [x] Bread - [x] Croissant | ### Paragraph with options diff --git a/crates/fleischwolf/tests/data/docx/expected/docx_checkboxes.docx.strict.md b/crates/fleischwolf/tests/data/docx/expected/docx_checkboxes.docx.strict.md index 16063dce..fc903c1a 100644 --- a/crates/fleischwolf/tests/data/docx/expected/docx_checkboxes.docx.strict.md +++ b/crates/fleischwolf/tests/data/docx/expected/docx_checkboxes.docx.strict.md @@ -1,10 +1,10 @@ ### Table with options -| **List of services** | -| - | -| Breakfast options | +| **List of services** | +|---------------------------------------------------------------------------------------------------| +| Breakfast options | | Choose as many as you like: - [x] Orange juice - [ ] Tea - [x] Coffee - [ ] Milk - [x] Water | -| Choose as many as you like: - [x] Scramble eggs - [ ] Porridge - [x] Bread - [x] Croissant | +| Choose as many as you like: - [x] Scramble eggs - [ ] Porridge - [x] Bread - [x] Croissant | ### Paragraph with options diff --git a/crates/fleischwolf/tests/data/docx/expected/docx_rich_cells.docx.md b/crates/fleischwolf/tests/data/docx/expected/docx_rich_cells.docx.md index 5c0b9cdc..ea17fcd1 100644 --- a/crates/fleischwolf/tests/data/docx/expected/docx_rich_cells.docx.md +++ b/crates/fleischwolf/tests/data/docx/expected/docx_rich_cells.docx.md @@ -1,49 +1,49 @@ ### Table with rich cells -| Column A | Column B | -| - | - | -| This is a list: - A First - A Second - A Third | This is a formatted list: - B **First** - B *Second* - B Third | +| Column A | Column B | +|------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------| +| This is a list: - A First - A Second - A Third | This is a formatted list: - B **First** - B *Second* - B Third | | First Paragraph Second Paragraph Third paragraph before a numbered list 1. Number one 2. Number two 3. Number three | This is simple text with **bold** , ~~strikethrough~~ and *italic* formatting with x 2 and H 2 O | -| This is a paragraph This is another paragraph | | +| This is a paragraph This is another paragraph | | ### Table with nested table Before table -| Column A | Column B | -| - | - | -| Simple cell upper left | Simple cell with **bold** and *italic* text | +| Column A | Column B | +|----------------------------|------------------------------------------------------| +| Simple cell upper left | Simple cell with **bold** and *italic* text | | A B C Cell 1 Cell 2 Cell 3 | Rich cell A nested table A B C Cell 1 Cell 2 Cell 3 | After table with **bold** , underline , ~~strikethrough~~ , and *italic* formatting ### Table with pictures -| Column A | Column B | -| - | - | -| Only text | | -| Text and picture | | +| Column A | Column B | +|----------------------------------|----------------| +| Only text | | +| Text and picture | | ### Lists with same numId in different cells -| - Cell 1 item 1 - Cell 1 item 2 | -| - | -| - Cell 2 item 1 - Cell 2 item 2 | +| - Cell 1 item 1 - Cell 1 item 2 | +|-----------------------------------| +| - Cell 2 item 1 - Cell 2 item 2 | ### Lists with different numIds in different cells -| - Cell 1 item 1 - Cell 1 item 2 | -| - | -| - Cell 2 item 1 - Cell 2 item 2 | +| - Cell 1 item 1 - Cell 1 item 2 | +|-----------------------------------| +| - Cell 2 item 1 - Cell 2 item 2 | ### Multiple columns with lists -| - R1C1 item 1 - R1C1 item 2 | - R1C2 item 1 - R1C2 item 2 | -| - | - | -| - R2C1 item 1 - R2C1 item 2 | - R2C2 item 1 - R2C2 item 2 | +| - R1C1 item 1 - R1C1 item 2 | - R1C2 item 1 - R1C2 item 2 | +|-------------------------------|-------------------------------| +| - R2C1 item 1 - R2C1 item 2 | - R2C2 item 1 - R2C2 item 2 | ### Mixed content - list and regular text in different cells -| - List item 1 - List item 2 | -| - | -| Regular text in second cell | +| - List item 1 - List item 2 | +|-------------------------------| +| Regular text in second cell | diff --git a/crates/fleischwolf/tests/data/docx/expected/docx_rich_cells.docx.strict.md b/crates/fleischwolf/tests/data/docx/expected/docx_rich_cells.docx.strict.md index 5c476846..41456194 100644 --- a/crates/fleischwolf/tests/data/docx/expected/docx_rich_cells.docx.strict.md +++ b/crates/fleischwolf/tests/data/docx/expected/docx_rich_cells.docx.strict.md @@ -1,49 +1,49 @@ ### Table with rich cells -| Column A | Column B | -| - | - | -| This is a list: - A First - A Second - A Third | This is a formatted list: - B **First** - B *Second* - B Third | +| Column A | Column B | +|------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------| +| This is a list: - A First - A Second - A Third | This is a formatted list: - B **First** - B *Second* - B Third | | First Paragraph Second Paragraph Third paragraph before a numbered list 1. Number one 2. Number two 3. Number three | This is simple text with **bold** , ~~strikethrough~~ and *italic* formatting with x 2 and H 2 O | -| This is a paragraph This is another paragraph | | +| This is a paragraph This is another paragraph | | ### Table with nested table Before table -| Column A | Column B | -| - | - | -| Simple cell upper left | Simple cell with **bold** and *italic* text | +| Column A | Column B | +|----------------------------|------------------------------------------------------| +| Simple cell upper left | Simple cell with **bold** and *italic* text | | A B C Cell 1 Cell 2 Cell 3 | Rich cell A nested table A B C Cell 1 Cell 2 Cell 3 | After table with **bold**, underline, ~~strikethrough~~, and *italic* formatting ### Table with pictures -| Column A | Column B | -| - | - | -| Only text | | -| Text and picture | | +| Column A | Column B | +|----------------------------------|----------------| +| Only text | | +| Text and picture | | ### Lists with same numId in different cells -| - Cell 1 item 1 - Cell 1 item 2 | -| - | -| - Cell 2 item 1 - Cell 2 item 2 | +| - Cell 1 item 1 - Cell 1 item 2 | +|-----------------------------------| +| - Cell 2 item 1 - Cell 2 item 2 | ### Lists with different numIds in different cells -| - Cell 1 item 1 - Cell 1 item 2 | -| - | -| - Cell 2 item 1 - Cell 2 item 2 | +| - Cell 1 item 1 - Cell 1 item 2 | +|-----------------------------------| +| - Cell 2 item 1 - Cell 2 item 2 | ### Multiple columns with lists -| - R1C1 item 1 - R1C1 item 2 | - R1C2 item 1 - R1C2 item 2 | -| - | - | -| - R2C1 item 1 - R2C1 item 2 | - R2C2 item 1 - R2C2 item 2 | +| - R1C1 item 1 - R1C1 item 2 | - R1C2 item 1 - R1C2 item 2 | +|-------------------------------|-------------------------------| +| - R2C1 item 1 - R2C1 item 2 | - R2C2 item 1 - R2C2 item 2 | ### Mixed content - list and regular text in different cells -| - List item 1 - List item 2 | -| - | -| Regular text in second cell | +| - List item 1 - List item 2 | +|-------------------------------| +| Regular text in second cell | diff --git a/crates/fleischwolf/tests/data/docx/expected/docx_rich_tables_01.docx.md b/crates/fleischwolf/tests/data/docx/expected/docx_rich_tables_01.docx.md index 9f45129c..52b54a97 100644 --- a/crates/fleischwolf/tests/data/docx/expected/docx_rich_tables_01.docx.md +++ b/crates/fleischwolf/tests/data/docx/expected/docx_rich_tables_01.docx.md @@ -11,56 +11,56 @@ The transformation of this technical draft for ISO compliance involves two phase **Phase 1** -| **Feature** | **Action Needed** | **Comment/Links** | -| - | - | - | -| Document Clause Ordering | Reorganize into the mandatory sequence: • Foreword (Mandatory, unnumbered) • Introduction (Optional, unnumbered) • 1 Scope (Mandatory, Clause 1) • 2 Normative references (Mandatory, Clause 2) • 3 Terms and definitions (Mandatory, Clause 3) • 4 Symbols and abbreviated terms (Mandatory, Clause 4) • 5 Conformance (Mandatory, Clause 5) • 6 Technical Clauses... • Annex A (normative) • Annex B/C (informative) • Bibliography (Mandatory, absolute end) | The current draft improperly mixes introductory narrative, design principles, and examples before standard layout clauses are established. See ISO/IEC Directives, Part 2, Clause 6. | -| Author & Corporate Profiles | Completely remove the table listing individual names and company affiliations. | Personal names, corporate branding, logos, and affiliations are prohibited in the normative body of an ISO publication. See ISO/IEC Directives, Part 2, Clauses 4 and 12.5.2. | -| Foreword Boilerplate | Replace the original foreword with the mandatory ISO/IEC JTC 1 PAS foreword text and retain only a neutral statement identifying the originating workshop. | ISO forewords follow prescribed wording and structure. See ISO/IEC Directives, Part 2, Clause 12. | -| Market / Design Motivation | Remove the Motivation clause and all comparisons criticizing Markdown, HTML, LaTeX, PageXML, ALTO XML, hOCR, or similar technologies. | ISO standards shall remain technologically neutral and avoid comparative or competitive claims. See ISO/IEC Directives, Part 2, Clause 4. | -| Evolutionary Versioning Prose | Remove historical development narratives, beta-version discussions, and Version 0.x compatibility explanations. | ISO standards describe current requirements, not product-development history. See ISO/IEC Directives, Part 2, Clauses 11 and 12. | -| Clause Header Casing | Convert all clause and subclause headings to sentence case. | Title-case headings are not permitted by ISO house style. See ISO/IEC Directives, Part 2, Clause 22.2. | -| Self-References | Replace all occurrences of “this specification”, “this standard”, and “this format” with “this document”. | ISO publications refer to themselves exclusively as “this document”. See ISO/IEC Directives, Part 2, Clause 10.6. | -| Scope Clause Compliance | Rewrite Clause 1 to describe the subject matter only and remove normative statements, implementation obligations, and references to conformance requirements. | The Scope clause shall define the extent and field of application of the document and shall not contain requirements. See ISO/IEC Directives, Part 2, Clause 14. | -| Normative References Clause | Create Clause 2 immediately after Scope. If no references are required, insert: “There are no normative references in this document.” | Clause 2 is mandatory. See ISO/IEC Directives, Part 2, Clause 15. | -| External Dependency Classification | Review references to XML, XSD, Unicode, URI specifications, and RFCs, classifying each as normative (Clause 2) or informative (Bibliography). | Any referenced document required to implement this document shall appear in Clause 2. See ISO/IEC Directives, Part 2, Clause 15. | -| Terms and Definitions Clause | Convert “Terminology” into “Terms and definitions” and format all entries according to ISO terminology conventions. | Definitions shall follow the prescribed ISO structure. See ISO/IEC Directives, Part 2, Clause 16. | -| Entry Notes in Definitions | Convert embedded explanatory remarks into formal “Note X to entry:” format. | Informal notes embedded in definitions are not permitted. See ISO/IEC Directives, Part 2, Clause 16.5.9. | -| Terminological Source Attribution | Add formal [SOURCE: ...] citations to imported definitions. | Borrowed definitions require source attribution. See ISO/IEC Directives, Part 2, Clause 16.5.10. | -| Terminology Consistency Audit | Establish a single preferred term for each concept and remove synonyms throughout the document. | ISO drafting follows a one-concept–one-term principle. See ISO/IEC Directives, Part 2, Clause 16. | -| Symbols and Abbreviated Terms Clause | Create Clause 4 or merge abbreviations into Clause 3. | Acronyms such as OTSL, XML, VLM, RAG, PII, GDPR, and XSD require centralized treatment. See ISO/IEC Directives, Part 2, Clause 17. | -| Conformance Definition | Create Clause 5 Conformance defining the implementation classes eligible to claim compliance. | Requirements cannot exist without identifying the responsible implementation target. See ISO/IEC Directives, Part 2, Clause 33. | -| Conformance Classes | Define separate conformance classes where applicable (e.g., document producers, validators, parsers, processors, renderers). | Distinct implementation categories may require different obligations and test criteria. See ISO/IEC Directives, Part 2, Clause 33. | -| Requirement Traceability | Ensure every “shall” statement identifies a responsible subject and can be objectively tested. | Requirements must be measurable and verifiable. See ISO/IEC Directives, Part 2, Clause 33. | -| RFC Verbal Compliance | Replace RFC 2119 uppercase keywords (MUST, SHOULD, MAY) with ISO verbal forms (shall, should, may). | ISO does not recognize RFC keyword conventions. See ISO/IEC Directives, Part 2, Clause 7. | -| Introduction Compliance | Remove all requirements, permissions, recommendations, and implementation rules from the Introduction. | The Introduction is informative only. See ISO/IEC Directives, Part 2, Clause 13. | -| Informative Annex Compliance | Remove normative language from all informative annexes. | Informative annexes shall not contain requirements. See ISO/IEC Directives, Part 2, Clause 20.2. | -| Annex Naming | Rename Appendix A/B/C to Annex A (normative), Annex B (informative), and Annex C (informative). | ISO does not use the term “Appendix”. See ISO/IEC Directives, Part 2, Clause 20. | -| Annex Reference Audit | Ensure Annexes A, B, and C are explicitly referenced from the body text. | Unreferenced annexes are commonly flagged during editorial review. See ISO/IEC Directives, Part 2, Clause 20. | +| **Feature** | **Action Needed** | **Comment/Links** | +|--------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| Document Clause Ordering | Reorganize into the mandatory sequence: • Foreword (Mandatory, unnumbered) • Introduction (Optional, unnumbered) • 1 Scope (Mandatory, Clause 1) • 2 Normative references (Mandatory, Clause 2) • 3 Terms and definitions (Mandatory, Clause 3) • 4 Symbols and abbreviated terms (Mandatory, Clause 4) • 5 Conformance (Mandatory, Clause 5) • 6 Technical Clauses... • Annex A (normative) • Annex B/C (informative) • Bibliography (Mandatory, absolute end) | The current draft improperly mixes introductory narrative, design principles, and examples before standard layout clauses are established. See ISO/IEC Directives, Part 2, Clause 6. | +| Author & Corporate Profiles | Completely remove the table listing individual names and company affiliations. | Personal names, corporate branding, logos, and affiliations are prohibited in the normative body of an ISO publication. See ISO/IEC Directives, Part 2, Clauses 4 and 12.5.2. | +| Foreword Boilerplate | Replace the original foreword with the mandatory ISO/IEC JTC 1 PAS foreword text and retain only a neutral statement identifying the originating workshop. | ISO forewords follow prescribed wording and structure. See ISO/IEC Directives, Part 2, Clause 12. | +| Market / Design Motivation | Remove the Motivation clause and all comparisons criticizing Markdown, HTML, LaTeX, PageXML, ALTO XML, hOCR, or similar technologies. | ISO standards shall remain technologically neutral and avoid comparative or competitive claims. See ISO/IEC Directives, Part 2, Clause 4. | +| Evolutionary Versioning Prose | Remove historical development narratives, beta-version discussions, and Version 0.x compatibility explanations. | ISO standards describe current requirements, not product-development history. See ISO/IEC Directives, Part 2, Clauses 11 and 12. | +| Clause Header Casing | Convert all clause and subclause headings to sentence case. | Title-case headings are not permitted by ISO house style. See ISO/IEC Directives, Part 2, Clause 22.2. | +| Self-References | Replace all occurrences of “this specification”, “this standard”, and “this format” with “this document”. | ISO publications refer to themselves exclusively as “this document”. See ISO/IEC Directives, Part 2, Clause 10.6. | +| Scope Clause Compliance | Rewrite Clause 1 to describe the subject matter only and remove normative statements, implementation obligations, and references to conformance requirements. | The Scope clause shall define the extent and field of application of the document and shall not contain requirements. See ISO/IEC Directives, Part 2, Clause 14. | +| Normative References Clause | Create Clause 2 immediately after Scope. If no references are required, insert: “There are no normative references in this document.” | Clause 2 is mandatory. See ISO/IEC Directives, Part 2, Clause 15. | +| External Dependency Classification | Review references to XML, XSD, Unicode, URI specifications, and RFCs, classifying each as normative (Clause 2) or informative (Bibliography). | Any referenced document required to implement this document shall appear in Clause 2. See ISO/IEC Directives, Part 2, Clause 15. | +| Terms and Definitions Clause | Convert “Terminology” into “Terms and definitions” and format all entries according to ISO terminology conventions. | Definitions shall follow the prescribed ISO structure. See ISO/IEC Directives, Part 2, Clause 16. | +| Entry Notes in Definitions | Convert embedded explanatory remarks into formal “Note X to entry:” format. | Informal notes embedded in definitions are not permitted. See ISO/IEC Directives, Part 2, Clause 16.5.9. | +| Terminological Source Attribution | Add formal [SOURCE: ...] citations to imported definitions. | Borrowed definitions require source attribution. See ISO/IEC Directives, Part 2, Clause 16.5.10. | +| Terminology Consistency Audit | Establish a single preferred term for each concept and remove synonyms throughout the document. | ISO drafting follows a one-concept–one-term principle. See ISO/IEC Directives, Part 2, Clause 16. | +| Symbols and Abbreviated Terms Clause | Create Clause 4 or merge abbreviations into Clause 3. | Acronyms such as OTSL, XML, VLM, RAG, PII, GDPR, and XSD require centralized treatment. See ISO/IEC Directives, Part 2, Clause 17. | +| Conformance Definition | Create Clause 5 Conformance defining the implementation classes eligible to claim compliance. | Requirements cannot exist without identifying the responsible implementation target. See ISO/IEC Directives, Part 2, Clause 33. | +| Conformance Classes | Define separate conformance classes where applicable (e.g., document producers, validators, parsers, processors, renderers). | Distinct implementation categories may require different obligations and test criteria. See ISO/IEC Directives, Part 2, Clause 33. | +| Requirement Traceability | Ensure every “shall” statement identifies a responsible subject and can be objectively tested. | Requirements must be measurable and verifiable. See ISO/IEC Directives, Part 2, Clause 33. | +| RFC Verbal Compliance | Replace RFC 2119 uppercase keywords (MUST, SHOULD, MAY) with ISO verbal forms (shall, should, may). | ISO does not recognize RFC keyword conventions. See ISO/IEC Directives, Part 2, Clause 7. | +| Introduction Compliance | Remove all requirements, permissions, recommendations, and implementation rules from the Introduction. | The Introduction is informative only. See ISO/IEC Directives, Part 2, Clause 13. | +| Informative Annex Compliance | Remove normative language from all informative annexes. | Informative annexes shall not contain requirements. See ISO/IEC Directives, Part 2, Clause 20.2. | +| Annex Naming | Rename Appendix A/B/C to Annex A (normative), Annex B (informative), and Annex C (informative). | ISO does not use the term “Appendix”. See ISO/IEC Directives, Part 2, Clause 20. | +| Annex Reference Audit | Ensure Annexes A, B, and C are explicitly referenced from the body text. | Unreferenced annexes are commonly flagged during editorial review. See ISO/IEC Directives, Part 2, Clause 20. | **Phase 2** -| **Feature** | **Action Needed** | **Comment/Links** | -| - | - | - | -| Cross-Reference Normalization | Replace Markdown anchors and informal references with ISO clause references. | References should use forms such as “see 7.3.2” or “see Annex A”. See ISO/IEC Directives, Part 2, Clause 10. | -| Hanging Paragraph Subclauses | Insert “General” subclauses beneath major clauses before introducing subordinate subclauses. | Prevents hanging paragraphs and ambiguous clause structures. See ISO/IEC Directives, Part 2, Clause 22.3.3. | -| Orphan Subclause Remediation | Ensure any clause containing a subclause .1 also contains a .2 or merge the subdivision back into the parent clause. | ISO numbering rules prohibit single-child subdivisions. See ISO/IEC Directives, Part 2, Clause 22.3.2. | -| XML Tag Typography | Apply consistent literal formatting to element names, attributes, markup fragments, and grammar symbols. | Distinguishes identifiers from prose and reduces ambiguity. See ISO/IEC Directives, Part 2, Clauses 9 and 24. | -| Formal Language Notation | Introduce a dedicated clause describing any normative grammar notation, schema language, or syntax conventions. | Formal languages should be specified using recognized notation rather than examples alone. See ISO/IEC Directives, Part 2, Clause 9.2. | -| Attribute Value Enumerations | Consolidate controlled vocabularies and attribute values into structured tables or formal rules. | Improves implementability, validation, and conformance testing. See ISO/IEC Directives, Part 2, Clause 5.6 & Clause 29. | -| Number Formatting | Replace comma-separated thousands with ISO spacing conventions (e.g., 65 535). | See ISO/IEC Directives, Part 2, Clause 9.1. | -| Printable URI / URL Strings | Display explicit URI strings for externally referenced resources. | Documents must remain usable when printed. See ISO/IEC Directives, Part 2, Clause 10.3. | -| Bi-directional Reference Auditing | Verify that all normative references are cited normatively and all bibliography entries are cited informatively. | Orphan references are not permitted. See ISO/IEC Directives, Part 2, Clauses 10.1 and 15.1. | -| Font and Style Normalization | Remove non-standard formatting, decorative boxes, custom colors, and visual styling. | ISO publishing systems normalize typography and layout automatically. See ISO/IEC Directives, Part 2, Clause 1. | -| Informative Code / Example Marking | Label all examples explicitly as EXAMPLE. | Examples must be clearly distinguished from requirements. See ISO/IEC Directives, Part 2, Clause 25. | -| Example Separation | Audit explanatory text surrounding examples to ensure examples cannot be interpreted as normative requirements. | Examples are informative only. See ISO/IEC Directives, Part 2, Clause 25. | -| Tabular Formats | Convert pipe-delimited text tables into proper table structures. | Tables shall be structurally defined. See ISO/IEC Directives, Part 2, Clause 29. | -| Non-Normative Implementation Narrative | Move implementation guidance and authoring-process commentary into notes or informative annexes. | Standards define technical outcomes, not internal author workflows. See ISO/IEC Directives, Part 2, Clause 24. | -| Mermaid Diagram Transmutation | Convert Mermaid source code into static figures. | Text-based diagram source code is completely unsuitable for ISO publication systems. See ISO/IEC Directives, Part 2, Clause 28.6.4. | -| Graphic Text Normalization | Standardize figure typography and remove branding from graphics. | Figures shall be neutral, completely clear, legible, and publication-ready. See ISO/IEC Directives, Part 2, Clause 28.5.2. | -| Bibliography Construction | Relocate all informative references into a final unnumbered Bibliography. | See ISO/IEC Directives, Part 2, Clause 21. | -| Asset Caption Formatting | Convert captions to ISO figure/table caption style and sentence case. | See ISO/IEC Directives, Part 2, Clause 28.2 & Clause 29.2. | -| Commercial Tools Footnotes | Add non-endorsement wording for references to trademarked products or technologies. | See ISO/IEC Directives, Part 2, Clause 31. | -| Mathematical Interval & Unit Formatting | Convert interval notation and enforce spacing between values and units. | See ISO/IEC Directives, Part 2, Clauses 9.1 and 9.4.1. | -| Percentage Symbol Clean-up | Replace % in prose with “per cent”. | The symbol % is restricted entirely to tabular matrices and literal code blocks. See ISO/IEC Directives, Part 2, Clause 9.4.1 & Annex B. | -| Schema Placeholder Optimization | Remove placeholder values, ellipses, and drafting artifacts from examples. | Draft remnants are flagged as incomplete specification leaks by automated ingestion systems. See ISO/IEC Directives, Part 2, Clause 4.1. | -| Subscript Coordinate Notation | Convert snake_case mathematical variables into proper mathematical notation with subscripts. | See ISO/IEC Directives, Part 2, Clause 9.3.1. | +| **Feature** | **Action Needed** | **Comment/Links** | +|-----------------------------------------|----------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------| +| Cross-Reference Normalization | Replace Markdown anchors and informal references with ISO clause references. | References should use forms such as “see 7.3.2” or “see Annex A”. See ISO/IEC Directives, Part 2, Clause 10. | +| Hanging Paragraph Subclauses | Insert “General” subclauses beneath major clauses before introducing subordinate subclauses. | Prevents hanging paragraphs and ambiguous clause structures. See ISO/IEC Directives, Part 2, Clause 22.3.3. | +| Orphan Subclause Remediation | Ensure any clause containing a subclause .1 also contains a .2 or merge the subdivision back into the parent clause. | ISO numbering rules prohibit single-child subdivisions. See ISO/IEC Directives, Part 2, Clause 22.3.2. | +| XML Tag Typography | Apply consistent literal formatting to element names, attributes, markup fragments, and grammar symbols. | Distinguishes identifiers from prose and reduces ambiguity. See ISO/IEC Directives, Part 2, Clauses 9 and 24. | +| Formal Language Notation | Introduce a dedicated clause describing any normative grammar notation, schema language, or syntax conventions. | Formal languages should be specified using recognized notation rather than examples alone. See ISO/IEC Directives, Part 2, Clause 9.2. | +| Attribute Value Enumerations | Consolidate controlled vocabularies and attribute values into structured tables or formal rules. | Improves implementability, validation, and conformance testing. See ISO/IEC Directives, Part 2, Clause 5.6 & Clause 29. | +| Number Formatting | Replace comma-separated thousands with ISO spacing conventions (e.g., 65 535). | See ISO/IEC Directives, Part 2, Clause 9.1. | +| Printable URI / URL Strings | Display explicit URI strings for externally referenced resources. | Documents must remain usable when printed. See ISO/IEC Directives, Part 2, Clause 10.3. | +| Bi-directional Reference Auditing | Verify that all normative references are cited normatively and all bibliography entries are cited informatively. | Orphan references are not permitted. See ISO/IEC Directives, Part 2, Clauses 10.1 and 15.1. | +| Font and Style Normalization | Remove non-standard formatting, decorative boxes, custom colors, and visual styling. | ISO publishing systems normalize typography and layout automatically. See ISO/IEC Directives, Part 2, Clause 1. | +| Informative Code / Example Marking | Label all examples explicitly as EXAMPLE. | Examples must be clearly distinguished from requirements. See ISO/IEC Directives, Part 2, Clause 25. | +| Example Separation | Audit explanatory text surrounding examples to ensure examples cannot be interpreted as normative requirements. | Examples are informative only. See ISO/IEC Directives, Part 2, Clause 25. | +| Tabular Formats | Convert pipe-delimited text tables into proper table structures. | Tables shall be structurally defined. See ISO/IEC Directives, Part 2, Clause 29. | +| Non-Normative Implementation Narrative | Move implementation guidance and authoring-process commentary into notes or informative annexes. | Standards define technical outcomes, not internal author workflows. See ISO/IEC Directives, Part 2, Clause 24. | +| Mermaid Diagram Transmutation | Convert Mermaid source code into static figures. | Text-based diagram source code is completely unsuitable for ISO publication systems. See ISO/IEC Directives, Part 2, Clause 28.6.4. | +| Graphic Text Normalization | Standardize figure typography and remove branding from graphics. | Figures shall be neutral, completely clear, legible, and publication-ready. See ISO/IEC Directives, Part 2, Clause 28.5.2. | +| Bibliography Construction | Relocate all informative references into a final unnumbered Bibliography. | See ISO/IEC Directives, Part 2, Clause 21. | +| Asset Caption Formatting | Convert captions to ISO figure/table caption style and sentence case. | See ISO/IEC Directives, Part 2, Clause 28.2 & Clause 29.2. | +| Commercial Tools Footnotes | Add non-endorsement wording for references to trademarked products or technologies. | See ISO/IEC Directives, Part 2, Clause 31. | +| Mathematical Interval & Unit Formatting | Convert interval notation and enforce spacing between values and units. | See ISO/IEC Directives, Part 2, Clauses 9.1 and 9.4.1. | +| Percentage Symbol Clean-up | Replace % in prose with “per cent”. | The symbol % is restricted entirely to tabular matrices and literal code blocks. See ISO/IEC Directives, Part 2, Clause 9.4.1 & Annex B. | +| Schema Placeholder Optimization | Remove placeholder values, ellipses, and drafting artifacts from examples. | Draft remnants are flagged as incomplete specification leaks by automated ingestion systems. See ISO/IEC Directives, Part 2, Clause 4.1. | +| Subscript Coordinate Notation | Convert snake_case mathematical variables into proper mathematical notation with subscripts. | See ISO/IEC Directives, Part 2, Clause 9.3.1. | diff --git a/crates/fleischwolf/tests/data/docx/expected/docx_rich_tables_01.docx.strict.md b/crates/fleischwolf/tests/data/docx/expected/docx_rich_tables_01.docx.strict.md index 9f45129c..52b54a97 100644 --- a/crates/fleischwolf/tests/data/docx/expected/docx_rich_tables_01.docx.strict.md +++ b/crates/fleischwolf/tests/data/docx/expected/docx_rich_tables_01.docx.strict.md @@ -11,56 +11,56 @@ The transformation of this technical draft for ISO compliance involves two phase **Phase 1** -| **Feature** | **Action Needed** | **Comment/Links** | -| - | - | - | -| Document Clause Ordering | Reorganize into the mandatory sequence: • Foreword (Mandatory, unnumbered) • Introduction (Optional, unnumbered) • 1 Scope (Mandatory, Clause 1) • 2 Normative references (Mandatory, Clause 2) • 3 Terms and definitions (Mandatory, Clause 3) • 4 Symbols and abbreviated terms (Mandatory, Clause 4) • 5 Conformance (Mandatory, Clause 5) • 6 Technical Clauses... • Annex A (normative) • Annex B/C (informative) • Bibliography (Mandatory, absolute end) | The current draft improperly mixes introductory narrative, design principles, and examples before standard layout clauses are established. See ISO/IEC Directives, Part 2, Clause 6. | -| Author & Corporate Profiles | Completely remove the table listing individual names and company affiliations. | Personal names, corporate branding, logos, and affiliations are prohibited in the normative body of an ISO publication. See ISO/IEC Directives, Part 2, Clauses 4 and 12.5.2. | -| Foreword Boilerplate | Replace the original foreword with the mandatory ISO/IEC JTC 1 PAS foreword text and retain only a neutral statement identifying the originating workshop. | ISO forewords follow prescribed wording and structure. See ISO/IEC Directives, Part 2, Clause 12. | -| Market / Design Motivation | Remove the Motivation clause and all comparisons criticizing Markdown, HTML, LaTeX, PageXML, ALTO XML, hOCR, or similar technologies. | ISO standards shall remain technologically neutral and avoid comparative or competitive claims. See ISO/IEC Directives, Part 2, Clause 4. | -| Evolutionary Versioning Prose | Remove historical development narratives, beta-version discussions, and Version 0.x compatibility explanations. | ISO standards describe current requirements, not product-development history. See ISO/IEC Directives, Part 2, Clauses 11 and 12. | -| Clause Header Casing | Convert all clause and subclause headings to sentence case. | Title-case headings are not permitted by ISO house style. See ISO/IEC Directives, Part 2, Clause 22.2. | -| Self-References | Replace all occurrences of “this specification”, “this standard”, and “this format” with “this document”. | ISO publications refer to themselves exclusively as “this document”. See ISO/IEC Directives, Part 2, Clause 10.6. | -| Scope Clause Compliance | Rewrite Clause 1 to describe the subject matter only and remove normative statements, implementation obligations, and references to conformance requirements. | The Scope clause shall define the extent and field of application of the document and shall not contain requirements. See ISO/IEC Directives, Part 2, Clause 14. | -| Normative References Clause | Create Clause 2 immediately after Scope. If no references are required, insert: “There are no normative references in this document.” | Clause 2 is mandatory. See ISO/IEC Directives, Part 2, Clause 15. | -| External Dependency Classification | Review references to XML, XSD, Unicode, URI specifications, and RFCs, classifying each as normative (Clause 2) or informative (Bibliography). | Any referenced document required to implement this document shall appear in Clause 2. See ISO/IEC Directives, Part 2, Clause 15. | -| Terms and Definitions Clause | Convert “Terminology” into “Terms and definitions” and format all entries according to ISO terminology conventions. | Definitions shall follow the prescribed ISO structure. See ISO/IEC Directives, Part 2, Clause 16. | -| Entry Notes in Definitions | Convert embedded explanatory remarks into formal “Note X to entry:” format. | Informal notes embedded in definitions are not permitted. See ISO/IEC Directives, Part 2, Clause 16.5.9. | -| Terminological Source Attribution | Add formal [SOURCE: ...] citations to imported definitions. | Borrowed definitions require source attribution. See ISO/IEC Directives, Part 2, Clause 16.5.10. | -| Terminology Consistency Audit | Establish a single preferred term for each concept and remove synonyms throughout the document. | ISO drafting follows a one-concept–one-term principle. See ISO/IEC Directives, Part 2, Clause 16. | -| Symbols and Abbreviated Terms Clause | Create Clause 4 or merge abbreviations into Clause 3. | Acronyms such as OTSL, XML, VLM, RAG, PII, GDPR, and XSD require centralized treatment. See ISO/IEC Directives, Part 2, Clause 17. | -| Conformance Definition | Create Clause 5 Conformance defining the implementation classes eligible to claim compliance. | Requirements cannot exist without identifying the responsible implementation target. See ISO/IEC Directives, Part 2, Clause 33. | -| Conformance Classes | Define separate conformance classes where applicable (e.g., document producers, validators, parsers, processors, renderers). | Distinct implementation categories may require different obligations and test criteria. See ISO/IEC Directives, Part 2, Clause 33. | -| Requirement Traceability | Ensure every “shall” statement identifies a responsible subject and can be objectively tested. | Requirements must be measurable and verifiable. See ISO/IEC Directives, Part 2, Clause 33. | -| RFC Verbal Compliance | Replace RFC 2119 uppercase keywords (MUST, SHOULD, MAY) with ISO verbal forms (shall, should, may). | ISO does not recognize RFC keyword conventions. See ISO/IEC Directives, Part 2, Clause 7. | -| Introduction Compliance | Remove all requirements, permissions, recommendations, and implementation rules from the Introduction. | The Introduction is informative only. See ISO/IEC Directives, Part 2, Clause 13. | -| Informative Annex Compliance | Remove normative language from all informative annexes. | Informative annexes shall not contain requirements. See ISO/IEC Directives, Part 2, Clause 20.2. | -| Annex Naming | Rename Appendix A/B/C to Annex A (normative), Annex B (informative), and Annex C (informative). | ISO does not use the term “Appendix”. See ISO/IEC Directives, Part 2, Clause 20. | -| Annex Reference Audit | Ensure Annexes A, B, and C are explicitly referenced from the body text. | Unreferenced annexes are commonly flagged during editorial review. See ISO/IEC Directives, Part 2, Clause 20. | +| **Feature** | **Action Needed** | **Comment/Links** | +|--------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| Document Clause Ordering | Reorganize into the mandatory sequence: • Foreword (Mandatory, unnumbered) • Introduction (Optional, unnumbered) • 1 Scope (Mandatory, Clause 1) • 2 Normative references (Mandatory, Clause 2) • 3 Terms and definitions (Mandatory, Clause 3) • 4 Symbols and abbreviated terms (Mandatory, Clause 4) • 5 Conformance (Mandatory, Clause 5) • 6 Technical Clauses... • Annex A (normative) • Annex B/C (informative) • Bibliography (Mandatory, absolute end) | The current draft improperly mixes introductory narrative, design principles, and examples before standard layout clauses are established. See ISO/IEC Directives, Part 2, Clause 6. | +| Author & Corporate Profiles | Completely remove the table listing individual names and company affiliations. | Personal names, corporate branding, logos, and affiliations are prohibited in the normative body of an ISO publication. See ISO/IEC Directives, Part 2, Clauses 4 and 12.5.2. | +| Foreword Boilerplate | Replace the original foreword with the mandatory ISO/IEC JTC 1 PAS foreword text and retain only a neutral statement identifying the originating workshop. | ISO forewords follow prescribed wording and structure. See ISO/IEC Directives, Part 2, Clause 12. | +| Market / Design Motivation | Remove the Motivation clause and all comparisons criticizing Markdown, HTML, LaTeX, PageXML, ALTO XML, hOCR, or similar technologies. | ISO standards shall remain technologically neutral and avoid comparative or competitive claims. See ISO/IEC Directives, Part 2, Clause 4. | +| Evolutionary Versioning Prose | Remove historical development narratives, beta-version discussions, and Version 0.x compatibility explanations. | ISO standards describe current requirements, not product-development history. See ISO/IEC Directives, Part 2, Clauses 11 and 12. | +| Clause Header Casing | Convert all clause and subclause headings to sentence case. | Title-case headings are not permitted by ISO house style. See ISO/IEC Directives, Part 2, Clause 22.2. | +| Self-References | Replace all occurrences of “this specification”, “this standard”, and “this format” with “this document”. | ISO publications refer to themselves exclusively as “this document”. See ISO/IEC Directives, Part 2, Clause 10.6. | +| Scope Clause Compliance | Rewrite Clause 1 to describe the subject matter only and remove normative statements, implementation obligations, and references to conformance requirements. | The Scope clause shall define the extent and field of application of the document and shall not contain requirements. See ISO/IEC Directives, Part 2, Clause 14. | +| Normative References Clause | Create Clause 2 immediately after Scope. If no references are required, insert: “There are no normative references in this document.” | Clause 2 is mandatory. See ISO/IEC Directives, Part 2, Clause 15. | +| External Dependency Classification | Review references to XML, XSD, Unicode, URI specifications, and RFCs, classifying each as normative (Clause 2) or informative (Bibliography). | Any referenced document required to implement this document shall appear in Clause 2. See ISO/IEC Directives, Part 2, Clause 15. | +| Terms and Definitions Clause | Convert “Terminology” into “Terms and definitions” and format all entries according to ISO terminology conventions. | Definitions shall follow the prescribed ISO structure. See ISO/IEC Directives, Part 2, Clause 16. | +| Entry Notes in Definitions | Convert embedded explanatory remarks into formal “Note X to entry:” format. | Informal notes embedded in definitions are not permitted. See ISO/IEC Directives, Part 2, Clause 16.5.9. | +| Terminological Source Attribution | Add formal [SOURCE: ...] citations to imported definitions. | Borrowed definitions require source attribution. See ISO/IEC Directives, Part 2, Clause 16.5.10. | +| Terminology Consistency Audit | Establish a single preferred term for each concept and remove synonyms throughout the document. | ISO drafting follows a one-concept–one-term principle. See ISO/IEC Directives, Part 2, Clause 16. | +| Symbols and Abbreviated Terms Clause | Create Clause 4 or merge abbreviations into Clause 3. | Acronyms such as OTSL, XML, VLM, RAG, PII, GDPR, and XSD require centralized treatment. See ISO/IEC Directives, Part 2, Clause 17. | +| Conformance Definition | Create Clause 5 Conformance defining the implementation classes eligible to claim compliance. | Requirements cannot exist without identifying the responsible implementation target. See ISO/IEC Directives, Part 2, Clause 33. | +| Conformance Classes | Define separate conformance classes where applicable (e.g., document producers, validators, parsers, processors, renderers). | Distinct implementation categories may require different obligations and test criteria. See ISO/IEC Directives, Part 2, Clause 33. | +| Requirement Traceability | Ensure every “shall” statement identifies a responsible subject and can be objectively tested. | Requirements must be measurable and verifiable. See ISO/IEC Directives, Part 2, Clause 33. | +| RFC Verbal Compliance | Replace RFC 2119 uppercase keywords (MUST, SHOULD, MAY) with ISO verbal forms (shall, should, may). | ISO does not recognize RFC keyword conventions. See ISO/IEC Directives, Part 2, Clause 7. | +| Introduction Compliance | Remove all requirements, permissions, recommendations, and implementation rules from the Introduction. | The Introduction is informative only. See ISO/IEC Directives, Part 2, Clause 13. | +| Informative Annex Compliance | Remove normative language from all informative annexes. | Informative annexes shall not contain requirements. See ISO/IEC Directives, Part 2, Clause 20.2. | +| Annex Naming | Rename Appendix A/B/C to Annex A (normative), Annex B (informative), and Annex C (informative). | ISO does not use the term “Appendix”. See ISO/IEC Directives, Part 2, Clause 20. | +| Annex Reference Audit | Ensure Annexes A, B, and C are explicitly referenced from the body text. | Unreferenced annexes are commonly flagged during editorial review. See ISO/IEC Directives, Part 2, Clause 20. | **Phase 2** -| **Feature** | **Action Needed** | **Comment/Links** | -| - | - | - | -| Cross-Reference Normalization | Replace Markdown anchors and informal references with ISO clause references. | References should use forms such as “see 7.3.2” or “see Annex A”. See ISO/IEC Directives, Part 2, Clause 10. | -| Hanging Paragraph Subclauses | Insert “General” subclauses beneath major clauses before introducing subordinate subclauses. | Prevents hanging paragraphs and ambiguous clause structures. See ISO/IEC Directives, Part 2, Clause 22.3.3. | -| Orphan Subclause Remediation | Ensure any clause containing a subclause .1 also contains a .2 or merge the subdivision back into the parent clause. | ISO numbering rules prohibit single-child subdivisions. See ISO/IEC Directives, Part 2, Clause 22.3.2. | -| XML Tag Typography | Apply consistent literal formatting to element names, attributes, markup fragments, and grammar symbols. | Distinguishes identifiers from prose and reduces ambiguity. See ISO/IEC Directives, Part 2, Clauses 9 and 24. | -| Formal Language Notation | Introduce a dedicated clause describing any normative grammar notation, schema language, or syntax conventions. | Formal languages should be specified using recognized notation rather than examples alone. See ISO/IEC Directives, Part 2, Clause 9.2. | -| Attribute Value Enumerations | Consolidate controlled vocabularies and attribute values into structured tables or formal rules. | Improves implementability, validation, and conformance testing. See ISO/IEC Directives, Part 2, Clause 5.6 & Clause 29. | -| Number Formatting | Replace comma-separated thousands with ISO spacing conventions (e.g., 65 535). | See ISO/IEC Directives, Part 2, Clause 9.1. | -| Printable URI / URL Strings | Display explicit URI strings for externally referenced resources. | Documents must remain usable when printed. See ISO/IEC Directives, Part 2, Clause 10.3. | -| Bi-directional Reference Auditing | Verify that all normative references are cited normatively and all bibliography entries are cited informatively. | Orphan references are not permitted. See ISO/IEC Directives, Part 2, Clauses 10.1 and 15.1. | -| Font and Style Normalization | Remove non-standard formatting, decorative boxes, custom colors, and visual styling. | ISO publishing systems normalize typography and layout automatically. See ISO/IEC Directives, Part 2, Clause 1. | -| Informative Code / Example Marking | Label all examples explicitly as EXAMPLE. | Examples must be clearly distinguished from requirements. See ISO/IEC Directives, Part 2, Clause 25. | -| Example Separation | Audit explanatory text surrounding examples to ensure examples cannot be interpreted as normative requirements. | Examples are informative only. See ISO/IEC Directives, Part 2, Clause 25. | -| Tabular Formats | Convert pipe-delimited text tables into proper table structures. | Tables shall be structurally defined. See ISO/IEC Directives, Part 2, Clause 29. | -| Non-Normative Implementation Narrative | Move implementation guidance and authoring-process commentary into notes or informative annexes. | Standards define technical outcomes, not internal author workflows. See ISO/IEC Directives, Part 2, Clause 24. | -| Mermaid Diagram Transmutation | Convert Mermaid source code into static figures. | Text-based diagram source code is completely unsuitable for ISO publication systems. See ISO/IEC Directives, Part 2, Clause 28.6.4. | -| Graphic Text Normalization | Standardize figure typography and remove branding from graphics. | Figures shall be neutral, completely clear, legible, and publication-ready. See ISO/IEC Directives, Part 2, Clause 28.5.2. | -| Bibliography Construction | Relocate all informative references into a final unnumbered Bibliography. | See ISO/IEC Directives, Part 2, Clause 21. | -| Asset Caption Formatting | Convert captions to ISO figure/table caption style and sentence case. | See ISO/IEC Directives, Part 2, Clause 28.2 & Clause 29.2. | -| Commercial Tools Footnotes | Add non-endorsement wording for references to trademarked products or technologies. | See ISO/IEC Directives, Part 2, Clause 31. | -| Mathematical Interval & Unit Formatting | Convert interval notation and enforce spacing between values and units. | See ISO/IEC Directives, Part 2, Clauses 9.1 and 9.4.1. | -| Percentage Symbol Clean-up | Replace % in prose with “per cent”. | The symbol % is restricted entirely to tabular matrices and literal code blocks. See ISO/IEC Directives, Part 2, Clause 9.4.1 & Annex B. | -| Schema Placeholder Optimization | Remove placeholder values, ellipses, and drafting artifacts from examples. | Draft remnants are flagged as incomplete specification leaks by automated ingestion systems. See ISO/IEC Directives, Part 2, Clause 4.1. | -| Subscript Coordinate Notation | Convert snake_case mathematical variables into proper mathematical notation with subscripts. | See ISO/IEC Directives, Part 2, Clause 9.3.1. | +| **Feature** | **Action Needed** | **Comment/Links** | +|-----------------------------------------|----------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------| +| Cross-Reference Normalization | Replace Markdown anchors and informal references with ISO clause references. | References should use forms such as “see 7.3.2” or “see Annex A”. See ISO/IEC Directives, Part 2, Clause 10. | +| Hanging Paragraph Subclauses | Insert “General” subclauses beneath major clauses before introducing subordinate subclauses. | Prevents hanging paragraphs and ambiguous clause structures. See ISO/IEC Directives, Part 2, Clause 22.3.3. | +| Orphan Subclause Remediation | Ensure any clause containing a subclause .1 also contains a .2 or merge the subdivision back into the parent clause. | ISO numbering rules prohibit single-child subdivisions. See ISO/IEC Directives, Part 2, Clause 22.3.2. | +| XML Tag Typography | Apply consistent literal formatting to element names, attributes, markup fragments, and grammar symbols. | Distinguishes identifiers from prose and reduces ambiguity. See ISO/IEC Directives, Part 2, Clauses 9 and 24. | +| Formal Language Notation | Introduce a dedicated clause describing any normative grammar notation, schema language, or syntax conventions. | Formal languages should be specified using recognized notation rather than examples alone. See ISO/IEC Directives, Part 2, Clause 9.2. | +| Attribute Value Enumerations | Consolidate controlled vocabularies and attribute values into structured tables or formal rules. | Improves implementability, validation, and conformance testing. See ISO/IEC Directives, Part 2, Clause 5.6 & Clause 29. | +| Number Formatting | Replace comma-separated thousands with ISO spacing conventions (e.g., 65 535). | See ISO/IEC Directives, Part 2, Clause 9.1. | +| Printable URI / URL Strings | Display explicit URI strings for externally referenced resources. | Documents must remain usable when printed. See ISO/IEC Directives, Part 2, Clause 10.3. | +| Bi-directional Reference Auditing | Verify that all normative references are cited normatively and all bibliography entries are cited informatively. | Orphan references are not permitted. See ISO/IEC Directives, Part 2, Clauses 10.1 and 15.1. | +| Font and Style Normalization | Remove non-standard formatting, decorative boxes, custom colors, and visual styling. | ISO publishing systems normalize typography and layout automatically. See ISO/IEC Directives, Part 2, Clause 1. | +| Informative Code / Example Marking | Label all examples explicitly as EXAMPLE. | Examples must be clearly distinguished from requirements. See ISO/IEC Directives, Part 2, Clause 25. | +| Example Separation | Audit explanatory text surrounding examples to ensure examples cannot be interpreted as normative requirements. | Examples are informative only. See ISO/IEC Directives, Part 2, Clause 25. | +| Tabular Formats | Convert pipe-delimited text tables into proper table structures. | Tables shall be structurally defined. See ISO/IEC Directives, Part 2, Clause 29. | +| Non-Normative Implementation Narrative | Move implementation guidance and authoring-process commentary into notes or informative annexes. | Standards define technical outcomes, not internal author workflows. See ISO/IEC Directives, Part 2, Clause 24. | +| Mermaid Diagram Transmutation | Convert Mermaid source code into static figures. | Text-based diagram source code is completely unsuitable for ISO publication systems. See ISO/IEC Directives, Part 2, Clause 28.6.4. | +| Graphic Text Normalization | Standardize figure typography and remove branding from graphics. | Figures shall be neutral, completely clear, legible, and publication-ready. See ISO/IEC Directives, Part 2, Clause 28.5.2. | +| Bibliography Construction | Relocate all informative references into a final unnumbered Bibliography. | See ISO/IEC Directives, Part 2, Clause 21. | +| Asset Caption Formatting | Convert captions to ISO figure/table caption style and sentence case. | See ISO/IEC Directives, Part 2, Clause 28.2 & Clause 29.2. | +| Commercial Tools Footnotes | Add non-endorsement wording for references to trademarked products or technologies. | See ISO/IEC Directives, Part 2, Clause 31. | +| Mathematical Interval & Unit Formatting | Convert interval notation and enforce spacing between values and units. | See ISO/IEC Directives, Part 2, Clauses 9.1 and 9.4.1. | +| Percentage Symbol Clean-up | Replace % in prose with “per cent”. | The symbol % is restricted entirely to tabular matrices and literal code blocks. See ISO/IEC Directives, Part 2, Clause 9.4.1 & Annex B. | +| Schema Placeholder Optimization | Remove placeholder values, ellipses, and drafting artifacts from examples. | Draft remnants are flagged as incomplete specification leaks by automated ingestion systems. See ISO/IEC Directives, Part 2, Clause 4.1. | +| Subscript Coordinate Notation | Convert snake_case mathematical variables into proper mathematical notation with subscripts. | See ISO/IEC Directives, Part 2, Clause 9.3.1. | diff --git a/crates/fleischwolf/tests/data/docx/expected/table_with_equations.docx.md b/crates/fleischwolf/tests/data/docx/expected/table_with_equations.docx.md index cb26da08..bb2ebb08 100644 --- a/crates/fleischwolf/tests/data/docx/expected/table_with_equations.docx.md +++ b/crates/fleischwolf/tests/data/docx/expected/table_with_equations.docx.md @@ -1,3 +1,3 @@ -| The next cell has an equation | $A= \pi r^{2}$ | -| - | - | +| The next cell has an equation | $A= \pi r^{2}$ | +|------------------------------------|----------------------------------------| | The next cell has another equation | $x=\frac{-b \pm \sqrt{b^{2}-4ac}}{2a}$ | diff --git a/crates/fleischwolf/tests/data/docx/expected/table_with_equations.docx.strict.md b/crates/fleischwolf/tests/data/docx/expected/table_with_equations.docx.strict.md index cb26da08..bb2ebb08 100644 --- a/crates/fleischwolf/tests/data/docx/expected/table_with_equations.docx.strict.md +++ b/crates/fleischwolf/tests/data/docx/expected/table_with_equations.docx.strict.md @@ -1,3 +1,3 @@ -| The next cell has an equation | $A= \pi r^{2}$ | -| - | - | +| The next cell has an equation | $A= \pi r^{2}$ | +|------------------------------------|----------------------------------------| | The next cell has another equation | $x=\frac{-b \pm \sqrt{b^{2}-4ac}}{2a}$ | diff --git a/crates/fleischwolf/tests/data/docx/expected/tablecell.docx.md b/crates/fleischwolf/tests/data/docx/expected/tablecell.docx.md index 0cdde00a..6db4a76f 100644 --- a/crates/fleischwolf/tests/data/docx/expected/tablecell.docx.md +++ b/crates/fleischwolf/tests/data/docx/expected/tablecell.docx.md @@ -3,9 +3,9 @@ Some text before -| Tab1 | Tab2 | Tab3 | -| - | - | - | -| A | B | C | -| D | E | F | +| Tab1 | Tab2 | Tab3 | +|--------|--------|--------| +| A | B | C | +| D | E | F | Some text after diff --git a/crates/fleischwolf/tests/data/docx/expected/tablecell.docx.strict.md b/crates/fleischwolf/tests/data/docx/expected/tablecell.docx.strict.md index 0cdde00a..6db4a76f 100644 --- a/crates/fleischwolf/tests/data/docx/expected/tablecell.docx.strict.md +++ b/crates/fleischwolf/tests/data/docx/expected/tablecell.docx.strict.md @@ -3,9 +3,9 @@ Some text before -| Tab1 | Tab2 | Tab3 | -| - | - | - | -| A | B | C | -| D | E | F | +| Tab1 | Tab2 | Tab3 | +|--------|--------|--------| +| A | B | C | +| D | E | F | Some text after diff --git a/crates/fleischwolf/tests/data/docx/expected/word_sample.docx.md b/crates/fleischwolf/tests/data/docx/expected/word_sample.docx.md index 2ebb4436..ff1ce117 100644 --- a/crates/fleischwolf/tests/data/docx/expected/word_sample.docx.md +++ b/crates/fleischwolf/tests/data/docx/expected/word_sample.docx.md @@ -32,11 +32,11 @@ I like to eat leaves Here are some interesting things a respectful duck could eat: -| | Food | Calories per portion | -| - | - | - | -| Leaves | Ash, Elm, Maple | 50 | -| Berries | Blueberry, Strawberry, Cranberry | 150 | -| Grain | Corn, Buckwheat, Barley | 200 | +| | Food | Calories per portion | +|---------|----------------------------------|------------------------| +| Leaves | Ash, Elm, Maple | 50 | +| Berries | Blueberry, Strawberry, Cranberry | 150 | +| Grain | Corn, Buckwheat, Barley | 200 | And let’s add another list in the end: diff --git a/crates/fleischwolf/tests/data/docx/expected/word_sample.docx.strict.md b/crates/fleischwolf/tests/data/docx/expected/word_sample.docx.strict.md index 2ebb4436..ff1ce117 100644 --- a/crates/fleischwolf/tests/data/docx/expected/word_sample.docx.strict.md +++ b/crates/fleischwolf/tests/data/docx/expected/word_sample.docx.strict.md @@ -32,11 +32,11 @@ I like to eat leaves Here are some interesting things a respectful duck could eat: -| | Food | Calories per portion | -| - | - | - | -| Leaves | Ash, Elm, Maple | 50 | -| Berries | Blueberry, Strawberry, Cranberry | 150 | -| Grain | Corn, Buckwheat, Barley | 200 | +| | Food | Calories per portion | +|---------|----------------------------------|------------------------| +| Leaves | Ash, Elm, Maple | 50 | +| Berries | Blueberry, Strawberry, Cranberry | 150 | +| Grain | Corn, Buckwheat, Barley | 200 | And let’s add another list in the end: diff --git a/crates/fleischwolf/tests/data/docx/expected/word_tables.docx.md b/crates/fleischwolf/tests/data/docx/expected/word_tables.docx.md index 6f1e18e3..79872676 100644 --- a/crates/fleischwolf/tests/data/docx/expected/word_tables.docx.md +++ b/crates/fleischwolf/tests/data/docx/expected/word_tables.docx.md @@ -2,43 +2,43 @@ A uniform table -| Header 0.0 | Header 0.1 | Header 0.2 | -| - | - | - | -| Cell 1.0 | Cell 1.1 | Cell 1.2 | -| Cell 2.0 | Cell 2.1 | Cell 2.2 | +| Header 0.0 | Header 0.1 | Header 0.2 | +|--------------|--------------|--------------| +| Cell 1.0 | Cell 1.1 | Cell 1.2 | +| Cell 2.0 | Cell 2.1 | Cell 2.2 | A non-uniform table with horizontal spans -| Header 0.0 | Header 0.1 | Header 0.2 | -| - | - | - | -| Cell 1.0 | Merged Cell 1.1 1.2 | Merged Cell 1.1 1.2 | -| Cell 2.0 | Merged Cell 2.1 2.2 | Merged Cell 2.1 2.2 | +| Header 0.0 | Header 0.1 | Header 0.2 | +|--------------|---------------------|---------------------| +| Cell 1.0 | Merged Cell 1.1 1.2 | Merged Cell 1.1 1.2 | +| Cell 2.0 | Merged Cell 2.1 2.2 | Merged Cell 2.1 2.2 | A non-uniform table with horizontal spans in inner columns -| Header 0.0 | Header 0.1 | Header 0.2 | Header 0.3 | -| - | - | - | - | -| Cell 1.0 | Merged Cell 1.1 1.2 | Merged Cell 1.1 1.2 | Cell 1.3 | -| Cell 2.0 | Merged Cell 2.1 2.2 | Merged Cell 2.1 2.2 | Cell 2.3 | +| Header 0.0 | Header 0.1 | Header 0.2 | Header 0.3 | +|--------------|---------------------|---------------------|--------------| +| Cell 1.0 | Merged Cell 1.1 1.2 | Merged Cell 1.1 1.2 | Cell 1.3 | +| Cell 2.0 | Merged Cell 2.1 2.2 | Merged Cell 2.1 2.2 | Cell 2.3 | A non-uniform table with vertical spans -| Header 0.0 | Header 0.1 | Header 0.2 | -| - | - | - | -| Cell 1.0 | Merged Cell 1.1 2.1 | Cell 1.2 | -| Cell 2.0 | Merged Cell 1.1 2.1 | Cell 2.2 | -| Cell 3.0 | Merged Cell 3.1 4.1 | Cell 3.2 | -| Cell 4.0 | Merged Cell 3.1 4.1 | Cell 4.2 | +| Header 0.0 | Header 0.1 | Header 0.2 | +|--------------|---------------------|--------------| +| Cell 1.0 | Merged Cell 1.1 2.1 | Cell 1.2 | +| Cell 2.0 | Merged Cell 1.1 2.1 | Cell 2.2 | +| Cell 3.0 | Merged Cell 3.1 4.1 | Cell 3.2 | +| Cell 4.0 | Merged Cell 3.1 4.1 | Cell 4.2 | A non-uniform table with all kinds of spans and empty cells -| Header 0.0 | Header 0.1 | Header 0.2 | | | -| - | - | - | - | - | -| Cell 1.0 | Merged Cell 1.1 2.1 | Cell 1.2 | | | -| Cell 2.0 | Merged Cell 1.1 2.1 | Cell 2.2 | | | -| Cell 3.0 | Merged Cell 3.1 4.1 | Cell 3.2 | | | -| Cell 4.0 | Merged Cell 3.1 4.1 | Cell 4.2 | | Merged Cell 4.4 5.4 | -| | | | | Merged Cell 4.4 5.4 | -| | | | | | -| | | | | | -| | | | | Cell 8.4 | +| Header 0.0 | Header 0.1 | Header 0.2 | | | +|--------------|---------------------|--------------|----|---------------------| +| Cell 1.0 | Merged Cell 1.1 2.1 | Cell 1.2 | | | +| Cell 2.0 | Merged Cell 1.1 2.1 | Cell 2.2 | | | +| Cell 3.0 | Merged Cell 3.1 4.1 | Cell 3.2 | | | +| Cell 4.0 | Merged Cell 3.1 4.1 | Cell 4.2 | | Merged Cell 4.4 5.4 | +| | | | | Merged Cell 4.4 5.4 | +| | | | | | +| | | | | | +| | | | | Cell 8.4 | diff --git a/crates/fleischwolf/tests/data/docx/expected/word_tables.docx.strict.md b/crates/fleischwolf/tests/data/docx/expected/word_tables.docx.strict.md index 6f1e18e3..79872676 100644 --- a/crates/fleischwolf/tests/data/docx/expected/word_tables.docx.strict.md +++ b/crates/fleischwolf/tests/data/docx/expected/word_tables.docx.strict.md @@ -2,43 +2,43 @@ A uniform table -| Header 0.0 | Header 0.1 | Header 0.2 | -| - | - | - | -| Cell 1.0 | Cell 1.1 | Cell 1.2 | -| Cell 2.0 | Cell 2.1 | Cell 2.2 | +| Header 0.0 | Header 0.1 | Header 0.2 | +|--------------|--------------|--------------| +| Cell 1.0 | Cell 1.1 | Cell 1.2 | +| Cell 2.0 | Cell 2.1 | Cell 2.2 | A non-uniform table with horizontal spans -| Header 0.0 | Header 0.1 | Header 0.2 | -| - | - | - | -| Cell 1.0 | Merged Cell 1.1 1.2 | Merged Cell 1.1 1.2 | -| Cell 2.0 | Merged Cell 2.1 2.2 | Merged Cell 2.1 2.2 | +| Header 0.0 | Header 0.1 | Header 0.2 | +|--------------|---------------------|---------------------| +| Cell 1.0 | Merged Cell 1.1 1.2 | Merged Cell 1.1 1.2 | +| Cell 2.0 | Merged Cell 2.1 2.2 | Merged Cell 2.1 2.2 | A non-uniform table with horizontal spans in inner columns -| Header 0.0 | Header 0.1 | Header 0.2 | Header 0.3 | -| - | - | - | - | -| Cell 1.0 | Merged Cell 1.1 1.2 | Merged Cell 1.1 1.2 | Cell 1.3 | -| Cell 2.0 | Merged Cell 2.1 2.2 | Merged Cell 2.1 2.2 | Cell 2.3 | +| Header 0.0 | Header 0.1 | Header 0.2 | Header 0.3 | +|--------------|---------------------|---------------------|--------------| +| Cell 1.0 | Merged Cell 1.1 1.2 | Merged Cell 1.1 1.2 | Cell 1.3 | +| Cell 2.0 | Merged Cell 2.1 2.2 | Merged Cell 2.1 2.2 | Cell 2.3 | A non-uniform table with vertical spans -| Header 0.0 | Header 0.1 | Header 0.2 | -| - | - | - | -| Cell 1.0 | Merged Cell 1.1 2.1 | Cell 1.2 | -| Cell 2.0 | Merged Cell 1.1 2.1 | Cell 2.2 | -| Cell 3.0 | Merged Cell 3.1 4.1 | Cell 3.2 | -| Cell 4.0 | Merged Cell 3.1 4.1 | Cell 4.2 | +| Header 0.0 | Header 0.1 | Header 0.2 | +|--------------|---------------------|--------------| +| Cell 1.0 | Merged Cell 1.1 2.1 | Cell 1.2 | +| Cell 2.0 | Merged Cell 1.1 2.1 | Cell 2.2 | +| Cell 3.0 | Merged Cell 3.1 4.1 | Cell 3.2 | +| Cell 4.0 | Merged Cell 3.1 4.1 | Cell 4.2 | A non-uniform table with all kinds of spans and empty cells -| Header 0.0 | Header 0.1 | Header 0.2 | | | -| - | - | - | - | - | -| Cell 1.0 | Merged Cell 1.1 2.1 | Cell 1.2 | | | -| Cell 2.0 | Merged Cell 1.1 2.1 | Cell 2.2 | | | -| Cell 3.0 | Merged Cell 3.1 4.1 | Cell 3.2 | | | -| Cell 4.0 | Merged Cell 3.1 4.1 | Cell 4.2 | | Merged Cell 4.4 5.4 | -| | | | | Merged Cell 4.4 5.4 | -| | | | | | -| | | | | | -| | | | | Cell 8.4 | +| Header 0.0 | Header 0.1 | Header 0.2 | | | +|--------------|---------------------|--------------|----|---------------------| +| Cell 1.0 | Merged Cell 1.1 2.1 | Cell 1.2 | | | +| Cell 2.0 | Merged Cell 1.1 2.1 | Cell 2.2 | | | +| Cell 3.0 | Merged Cell 3.1 4.1 | Cell 3.2 | | | +| Cell 4.0 | Merged Cell 3.1 4.1 | Cell 4.2 | | Merged Cell 4.4 5.4 | +| | | | | Merged Cell 4.4 5.4 | +| | | | | | +| | | | | | +| | | | | Cell 8.4 | diff --git a/crates/fleischwolf/tests/data/html/expected/example_03.html.md b/crates/fleischwolf/tests/data/html/expected/example_03.html.md index c2c74a6c..760f1790 100644 --- a/crates/fleischwolf/tests/data/html/expected/example_03.html.md +++ b/crates/fleischwolf/tests/data/html/expected/example_03.html.md @@ -20,8 +20,8 @@ Some background information here. ## Data Table -| Header 1 | Header 2 | Header 3 | -| - | - | - | +| Header 1 | Header 2 | Header 3 | +|--------------|--------------|--------------| | Row 1, Col 1 | Row 1, Col 2 | Row 1, Col 3 | | Row 2, Col 1 | Row 2, Col 2 | Row 2, Col 3 | | Row 3, Col 1 | Row 3, Col 2 | Row 3, Col 3 | diff --git a/crates/fleischwolf/tests/data/html/expected/example_03.html.strict.md b/crates/fleischwolf/tests/data/html/expected/example_03.html.strict.md index c2c74a6c..760f1790 100644 --- a/crates/fleischwolf/tests/data/html/expected/example_03.html.strict.md +++ b/crates/fleischwolf/tests/data/html/expected/example_03.html.strict.md @@ -20,8 +20,8 @@ Some background information here. ## Data Table -| Header 1 | Header 2 | Header 3 | -| - | - | - | +| Header 1 | Header 2 | Header 3 | +|--------------|--------------|--------------| | Row 1, Col 1 | Row 1, Col 2 | Row 1, Col 3 | | Row 2, Col 1 | Row 2, Col 2 | Row 2, Col 3 | | Row 3, Col 1 | Row 3, Col 2 | Row 3, Col 3 | diff --git a/crates/fleischwolf/tests/data/html/expected/example_04.html.md b/crates/fleischwolf/tests/data/html/expected/example_04.html.md index e451b34f..aa307a06 100644 --- a/crates/fleischwolf/tests/data/html/expected/example_04.html.md +++ b/crates/fleischwolf/tests/data/html/expected/example_04.html.md @@ -1,7 +1,7 @@ # Data Table with Rowspan and Colspan -| Header 1 | Header 2 & 3 (colspan) | Header 2 & 3 (colspan) | -| - | - | - | -| Row 1 & 2, Col 1 (rowspan) | Row 1, Col 2 | Row 1, Col 3 | +| Header 1 | Header 2 & 3 (colspan) | Header 2 & 3 (colspan) | +|----------------------------|----------------------------|----------------------------| +| Row 1 & 2, Col 1 (rowspan) | Row 1, Col 2 | Row 1, Col 3 | | Row 1 & 2, Col 1 (rowspan) | Row 2, Col 2 & 3 (colspan) | Row 2, Col 2 & 3 (colspan) | -| Row 3, Col 1 | Row 3, Col 2 | Row 3, Col 3 | +| Row 3, Col 1 | Row 3, Col 2 | Row 3, Col 3 | diff --git a/crates/fleischwolf/tests/data/html/expected/example_04.html.strict.md b/crates/fleischwolf/tests/data/html/expected/example_04.html.strict.md index e451b34f..aa307a06 100644 --- a/crates/fleischwolf/tests/data/html/expected/example_04.html.strict.md +++ b/crates/fleischwolf/tests/data/html/expected/example_04.html.strict.md @@ -1,7 +1,7 @@ # Data Table with Rowspan and Colspan -| Header 1 | Header 2 & 3 (colspan) | Header 2 & 3 (colspan) | -| - | - | - | -| Row 1 & 2, Col 1 (rowspan) | Row 1, Col 2 | Row 1, Col 3 | +| Header 1 | Header 2 & 3 (colspan) | Header 2 & 3 (colspan) | +|----------------------------|----------------------------|----------------------------| +| Row 1 & 2, Col 1 (rowspan) | Row 1, Col 2 | Row 1, Col 3 | | Row 1 & 2, Col 1 (rowspan) | Row 2, Col 2 & 3 (colspan) | Row 2, Col 2 & 3 (colspan) | -| Row 3, Col 1 | Row 3, Col 2 | Row 3, Col 3 | +| Row 3, Col 1 | Row 3, Col 2 | Row 3, Col 3 | diff --git a/crates/fleischwolf/tests/data/html/expected/example_05.html.md b/crates/fleischwolf/tests/data/html/expected/example_05.html.md index 64b3c56f..e50a5886 100644 --- a/crates/fleischwolf/tests/data/html/expected/example_05.html.md +++ b/crates/fleischwolf/tests/data/html/expected/example_05.html.md @@ -1,7 +1,7 @@ # Omitted html and body tags -| Header 1 | Header 2 & 3 (colspan) | Header 2 & 3 (colspan) | -| - | - | - | -| Row 1 & 2, Col 1 (rowspan) | Row 1, Col 2 | Row 1, Col 3 | +| Header 1 | Header 2 & 3 (colspan) | Header 2 & 3 (colspan) | +|----------------------------|----------------------------|----------------------------| +| Row 1 & 2, Col 1 (rowspan) | Row 1, Col 2 | Row 1, Col 3 | | Row 1 & 2, Col 1 (rowspan) | Row 2, Col 2 & 3 (colspan) | Row 2, Col 2 & 3 (colspan) | -| Row 3, Col 1 | Row 3, Col 2 | Row 3, Col 3 | +| Row 3, Col 1 | Row 3, Col 2 | Row 3, Col 3 | diff --git a/crates/fleischwolf/tests/data/html/expected/example_05.html.strict.md b/crates/fleischwolf/tests/data/html/expected/example_05.html.strict.md index 64b3c56f..e50a5886 100644 --- a/crates/fleischwolf/tests/data/html/expected/example_05.html.strict.md +++ b/crates/fleischwolf/tests/data/html/expected/example_05.html.strict.md @@ -1,7 +1,7 @@ # Omitted html and body tags -| Header 1 | Header 2 & 3 (colspan) | Header 2 & 3 (colspan) | -| - | - | - | -| Row 1 & 2, Col 1 (rowspan) | Row 1, Col 2 | Row 1, Col 3 | +| Header 1 | Header 2 & 3 (colspan) | Header 2 & 3 (colspan) | +|----------------------------|----------------------------|----------------------------| +| Row 1 & 2, Col 1 (rowspan) | Row 1, Col 2 | Row 1, Col 3 | | Row 1 & 2, Col 1 (rowspan) | Row 2, Col 2 & 3 (colspan) | Row 2, Col 2 & 3 (colspan) | -| Row 3, Col 1 | Row 3, Col 2 | Row 3, Col 3 | +| Row 3, Col 1 | Row 3, Col 2 | Row 3, Col 3 | diff --git a/crates/fleischwolf/tests/data/html/expected/example_08.html.md b/crates/fleischwolf/tests/data/html/expected/example_08.html.md index d7b93717..8911891d 100644 --- a/crates/fleischwolf/tests/data/html/expected/example_08.html.md +++ b/crates/fleischwolf/tests/data/html/expected/example_08.html.md @@ -1,29 +1,29 @@ ## Pivot table with with 1 row header -| Year | Month | Revenue | Cost | -| - | - | - | - | -| 2025 | January | $134 | $162 | -| 2025 | February | $150 | $155 | -| 2025 | March | $160 | $143 | -| 2025 | April | $210 | $150 | -| 2025 | May | $280 | $120 | +| Year | Month | Revenue | Cost | +|--------|----------|-----------|--------| +| 2025 | January | $134 | $162 | +| 2025 | February | $150 | $155 | +| 2025 | March | $160 | $143 | +| 2025 | April | $210 | $150 | +| 2025 | May | $280 | $120 | ## Pivot table with 2 row headers -| Year | Quarter | Month | Revenue | Cost | -| - | - | - | - | - | -| 2025 | Q1 | January | $134 | $162 | -| 2025 | Q1 | February | $150 | $155 | -| 2025 | Q1 | March | $160 | $143 | -| 2025 | Q2 | April | $210 | $150 | -| 2025 | Q2 | May | $280 | $120 | +| Year | Quarter | Month | Revenue | Cost | +|--------|-----------|----------|-----------|--------| +| 2025 | Q1 | January | $134 | $162 | +| 2025 | Q1 | February | $150 | $155 | +| 2025 | Q1 | March | $160 | $143 | +| 2025 | Q2 | April | $210 | $150 | +| 2025 | Q2 | May | $280 | $120 | ## Equivalent pivot table -| Year | Quarter | Month | Revenue | Cost | -| - | - | - | - | - | -| 2025 | Q1 | January | $134 | $162 | -| 2025 | Q1 | February | $150 | $155 | -| 2025 | Q1 | March | $160 | $143 | -| 2025 | Q2 | April | $210 | $150 | -| 2025 | Q2 | May | $280 | $120 | +| Year | Quarter | Month | Revenue | Cost | +|--------|-----------|----------|-----------|--------| +| 2025 | Q1 | January | $134 | $162 | +| 2025 | Q1 | February | $150 | $155 | +| 2025 | Q1 | March | $160 | $143 | +| 2025 | Q2 | April | $210 | $150 | +| 2025 | Q2 | May | $280 | $120 | diff --git a/crates/fleischwolf/tests/data/html/expected/example_08.html.strict.md b/crates/fleischwolf/tests/data/html/expected/example_08.html.strict.md index d7b93717..8911891d 100644 --- a/crates/fleischwolf/tests/data/html/expected/example_08.html.strict.md +++ b/crates/fleischwolf/tests/data/html/expected/example_08.html.strict.md @@ -1,29 +1,29 @@ ## Pivot table with with 1 row header -| Year | Month | Revenue | Cost | -| - | - | - | - | -| 2025 | January | $134 | $162 | -| 2025 | February | $150 | $155 | -| 2025 | March | $160 | $143 | -| 2025 | April | $210 | $150 | -| 2025 | May | $280 | $120 | +| Year | Month | Revenue | Cost | +|--------|----------|-----------|--------| +| 2025 | January | $134 | $162 | +| 2025 | February | $150 | $155 | +| 2025 | March | $160 | $143 | +| 2025 | April | $210 | $150 | +| 2025 | May | $280 | $120 | ## Pivot table with 2 row headers -| Year | Quarter | Month | Revenue | Cost | -| - | - | - | - | - | -| 2025 | Q1 | January | $134 | $162 | -| 2025 | Q1 | February | $150 | $155 | -| 2025 | Q1 | March | $160 | $143 | -| 2025 | Q2 | April | $210 | $150 | -| 2025 | Q2 | May | $280 | $120 | +| Year | Quarter | Month | Revenue | Cost | +|--------|-----------|----------|-----------|--------| +| 2025 | Q1 | January | $134 | $162 | +| 2025 | Q1 | February | $150 | $155 | +| 2025 | Q1 | March | $160 | $143 | +| 2025 | Q2 | April | $210 | $150 | +| 2025 | Q2 | May | $280 | $120 | ## Equivalent pivot table -| Year | Quarter | Month | Revenue | Cost | -| - | - | - | - | - | -| 2025 | Q1 | January | $134 | $162 | -| 2025 | Q1 | February | $150 | $155 | -| 2025 | Q1 | March | $160 | $143 | -| 2025 | Q2 | April | $210 | $150 | -| 2025 | Q2 | May | $280 | $120 | +| Year | Quarter | Month | Revenue | Cost | +|--------|-----------|----------|-----------|--------| +| 2025 | Q1 | January | $134 | $162 | +| 2025 | Q1 | February | $150 | $155 | +| 2025 | Q1 | March | $160 | $143 | +| 2025 | Q2 | April | $210 | $150 | +| 2025 | Q2 | May | $280 | $120 | diff --git a/crates/fleischwolf/tests/data/html/expected/html_heading_in_p.html.md b/crates/fleischwolf/tests/data/html/expected/html_heading_in_p.html.md index 07ef9d02..ef9449ae 100644 --- a/crates/fleischwolf/tests/data/html/expected/html_heading_in_p.html.md +++ b/crates/fleischwolf/tests/data/html/expected/html_heading_in_p.html.md @@ -2,13 +2,13 @@ 1st paragraph -| **2** | **3** | **4** | -| - | - | - | -| 5 | 6 | 7 | -| 8 | 9 | 10 | -| 11 | 12 | 13 | -| 14 | 15 | 16 | -| 17 | 18 | 19 20 | +| **2** | **3** | **4** | +|---------|---------|---------| +| 5 | 6 | 7 | +| 8 | 9 | 10 | +| 11 | 12 | 13 | +| 14 | 15 | 16 | +| 17 | 18 | 19 20 | # 21 diff --git a/crates/fleischwolf/tests/data/html/expected/html_heading_in_p.html.strict.md b/crates/fleischwolf/tests/data/html/expected/html_heading_in_p.html.strict.md index 07ef9d02..ef9449ae 100644 --- a/crates/fleischwolf/tests/data/html/expected/html_heading_in_p.html.strict.md +++ b/crates/fleischwolf/tests/data/html/expected/html_heading_in_p.html.strict.md @@ -2,13 +2,13 @@ 1st paragraph -| **2** | **3** | **4** | -| - | - | - | -| 5 | 6 | 7 | -| 8 | 9 | 10 | -| 11 | 12 | 13 | -| 14 | 15 | 16 | -| 17 | 18 | 19 20 | +| **2** | **3** | **4** | +|---------|---------|---------| +| 5 | 6 | 7 | +| 8 | 9 | 10 | +| 11 | 12 | 13 | +| 14 | 15 | 16 | +| 17 | 18 | 19 20 | # 21 diff --git a/crates/fleischwolf/tests/data/html/expected/html_inline_group_in_table_cell.html.md b/crates/fleischwolf/tests/data/html/expected/html_inline_group_in_table_cell.html.md index a648355d..9d2f7f1b 100644 --- a/crates/fleischwolf/tests/data/html/expected/html_inline_group_in_table_cell.html.md +++ b/crates/fleischwolf/tests/data/html/expected/html_inline_group_in_table_cell.html.md @@ -1,12 +1,12 @@ Intro paragraph. -| Name | Links | -| - | - | -| Multi-link cell | See [Page A](https://example.com/a) and [Page B](https://example.com/b) for details. | -| Plain cell | Just text | -| Formatted cell | **Bold** and *italic* text | -| Inline code cell | `inline_fn()` | -| Block code cell | ``` do_thing() ``` | -| Pre with link | `See` [`the docs`](https://example.com/docs) `first` | +| Name | Links | +|------------------|--------------------------------------------------------------------------------------| +| Multi-link cell | See [Page A](https://example.com/a) and [Page B](https://example.com/b) for details. | +| Plain cell | Just text | +| Formatted cell | **Bold** and *italic* text | +| Inline code cell | `inline_fn()` | +| Block code cell | ``` do_thing() ``` | +| Pre with link | `See` [`the docs`](https://example.com/docs) `first` | Outro paragraph. diff --git a/crates/fleischwolf/tests/data/html/expected/html_inline_group_in_table_cell.html.strict.md b/crates/fleischwolf/tests/data/html/expected/html_inline_group_in_table_cell.html.strict.md index a648355d..9d2f7f1b 100644 --- a/crates/fleischwolf/tests/data/html/expected/html_inline_group_in_table_cell.html.strict.md +++ b/crates/fleischwolf/tests/data/html/expected/html_inline_group_in_table_cell.html.strict.md @@ -1,12 +1,12 @@ Intro paragraph. -| Name | Links | -| - | - | -| Multi-link cell | See [Page A](https://example.com/a) and [Page B](https://example.com/b) for details. | -| Plain cell | Just text | -| Formatted cell | **Bold** and *italic* text | -| Inline code cell | `inline_fn()` | -| Block code cell | ``` do_thing() ``` | -| Pre with link | `See` [`the docs`](https://example.com/docs) `first` | +| Name | Links | +|------------------|--------------------------------------------------------------------------------------| +| Multi-link cell | See [Page A](https://example.com/a) and [Page B](https://example.com/b) for details. | +| Plain cell | Just text | +| Formatted cell | **Bold** and *italic* text | +| Inline code cell | `inline_fn()` | +| Block code cell | ``` do_thing() ``` | +| Pre with link | `See` [`the docs`](https://example.com/docs) `first` | Outro paragraph. diff --git a/crates/fleischwolf/tests/data/html/expected/html_rich_table_cells.html.md b/crates/fleischwolf/tests/data/html/expected/html_rich_table_cells.html.md index a969c9fb..5daed7ca 100644 --- a/crates/fleischwolf/tests/data/html/expected/html_rich_table_cells.html.md +++ b/crates/fleischwolf/tests/data/html/expected/html_rich_table_cells.html.md @@ -1,27 +1,27 @@ # Rich Table Cells in HTML -| Name | Habitat | Comment | -| - | - | - | -| Wood Duck | | Often seen near ponds. | -| Mallard | Ponds, lakes, rivers | Quack | -| Goose (not a duck!) | Water & wetlands | **Large** , *loud* , noisy , ~~small~~ | -| Teal | - Pond - Marsh - Riverbank | 1. Fly south in winter 2. Build nest on ground | +| Name | Habitat | Comment | +|---------------------|----------------------------|------------------------------------------------| +| Wood Duck | | Often seen near ponds. | +| Mallard | Ponds, lakes, rivers | Quack | +| Goose (not a duck!) | Water & wetlands | **Large** , *loud* , noisy , ~~small~~ | +| Teal | - Pond - Marsh - Riverbank | 1. Fly south in winter 2. Build nest on ground | -| Genus | Species | -| - | - | -| Aythya (Diving ducks) | Hawser, Common Pochard | -| Lophonetta (Pintail group) | Fulvous Whistling Duck | -| Oxyura (Benthic ducks) | Wigee, Banded Water-screw | +| Genus | Species | +|----------------------------|---------------------------| +| Aythya (Diving ducks) | Hawser, Common Pochard | +| Lophonetta (Pintail group) | Fulvous Whistling Duck | +| Oxyura (Benthic ducks) | Wigee, Banded Water-screw | -| Action | Action | -| - | - | -| **Swim** | Gracefully glide on H 2 O surfaces. | -| *Fly* | | -| Quack | Type Sound Short "quak" Long "quaaaaaack" | +| Action | Action | +|----------|-------------------------------------------| +| **Swim** | Gracefully glide on H 2 O surfaces. | +| *Fly* | | +| Quack | Type Sound Short "quak" Long "quaaaaaack" | -| Name | Description | Image | -| - | - | - | -| Donald Duck | Cartoon character. | [View PNG](https://en.wikipedia.org/wiki/Donald_Duck#/media/File:Donald_Duck_angry_transparent_background.png) | -| White-headed duck | A small diving duck some 45 cm (18 in) long. | White-headed duck thumbnail | -| Mandarin Duck | Known for its striking plumage. | [View Full-Size Image](https://upload.wikimedia.org/wikipedia/commons/thumb/7/75/Mandarin_duck_%28Aix_galericulata%29.jpg/250px-Mandarin_duck_%28Aix_galericulata%29.jpg) | -| Unknown Duck | No photo available. | | +| Name | Description | Image | +|-------------------|----------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| Donald Duck | Cartoon character. | [View PNG](https://en.wikipedia.org/wiki/Donald_Duck#/media/File:Donald_Duck_angry_transparent_background.png) | +| White-headed duck | A small diving duck some 45 cm (18 in) long. | White-headed duck thumbnail | +| Mandarin Duck | Known for its striking plumage. | [View Full-Size Image](https://upload.wikimedia.org/wikipedia/commons/thumb/7/75/Mandarin_duck_%28Aix_galericulata%29.jpg/250px-Mandarin_duck_%28Aix_galericulata%29.jpg) | +| Unknown Duck | No photo available. | | diff --git a/crates/fleischwolf/tests/data/html/expected/html_rich_table_cells.html.strict.md b/crates/fleischwolf/tests/data/html/expected/html_rich_table_cells.html.strict.md index a969c9fb..5daed7ca 100644 --- a/crates/fleischwolf/tests/data/html/expected/html_rich_table_cells.html.strict.md +++ b/crates/fleischwolf/tests/data/html/expected/html_rich_table_cells.html.strict.md @@ -1,27 +1,27 @@ # Rich Table Cells in HTML -| Name | Habitat | Comment | -| - | - | - | -| Wood Duck | | Often seen near ponds. | -| Mallard | Ponds, lakes, rivers | Quack | -| Goose (not a duck!) | Water & wetlands | **Large** , *loud* , noisy , ~~small~~ | -| Teal | - Pond - Marsh - Riverbank | 1. Fly south in winter 2. Build nest on ground | +| Name | Habitat | Comment | +|---------------------|----------------------------|------------------------------------------------| +| Wood Duck | | Often seen near ponds. | +| Mallard | Ponds, lakes, rivers | Quack | +| Goose (not a duck!) | Water & wetlands | **Large** , *loud* , noisy , ~~small~~ | +| Teal | - Pond - Marsh - Riverbank | 1. Fly south in winter 2. Build nest on ground | -| Genus | Species | -| - | - | -| Aythya (Diving ducks) | Hawser, Common Pochard | -| Lophonetta (Pintail group) | Fulvous Whistling Duck | -| Oxyura (Benthic ducks) | Wigee, Banded Water-screw | +| Genus | Species | +|----------------------------|---------------------------| +| Aythya (Diving ducks) | Hawser, Common Pochard | +| Lophonetta (Pintail group) | Fulvous Whistling Duck | +| Oxyura (Benthic ducks) | Wigee, Banded Water-screw | -| Action | Action | -| - | - | -| **Swim** | Gracefully glide on H 2 O surfaces. | -| *Fly* | | -| Quack | Type Sound Short "quak" Long "quaaaaaack" | +| Action | Action | +|----------|-------------------------------------------| +| **Swim** | Gracefully glide on H 2 O surfaces. | +| *Fly* | | +| Quack | Type Sound Short "quak" Long "quaaaaaack" | -| Name | Description | Image | -| - | - | - | -| Donald Duck | Cartoon character. | [View PNG](https://en.wikipedia.org/wiki/Donald_Duck#/media/File:Donald_Duck_angry_transparent_background.png) | -| White-headed duck | A small diving duck some 45 cm (18 in) long. | White-headed duck thumbnail | -| Mandarin Duck | Known for its striking plumage. | [View Full-Size Image](https://upload.wikimedia.org/wikipedia/commons/thumb/7/75/Mandarin_duck_%28Aix_galericulata%29.jpg/250px-Mandarin_duck_%28Aix_galericulata%29.jpg) | -| Unknown Duck | No photo available. | | +| Name | Description | Image | +|-------------------|----------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| Donald Duck | Cartoon character. | [View PNG](https://en.wikipedia.org/wiki/Donald_Duck#/media/File:Donald_Duck_angry_transparent_background.png) | +| White-headed duck | A small diving duck some 45 cm (18 in) long. | White-headed duck thumbnail | +| Mandarin Duck | Known for its striking plumage. | [View Full-Size Image](https://upload.wikimedia.org/wikipedia/commons/thumb/7/75/Mandarin_duck_%28Aix_galericulata%29.jpg/250px-Mandarin_duck_%28Aix_galericulata%29.jpg) | +| Unknown Duck | No photo available. | | diff --git a/crates/fleischwolf/tests/data/html/expected/kvp_data_example.html.md b/crates/fleischwolf/tests/data/html/expected/kvp_data_example.html.md index 84998e53..948cb42a 100644 --- a/crates/fleischwolf/tests/data/html/expected/kvp_data_example.html.md +++ b/crates/fleischwolf/tests/data/html/expected/kvp_data_example.html.md @@ -40,10 +40,10 @@ Order items: Itemized list of products ordered with quantities. -| Nr. | Item Description | Quantity | -| - | - | - | -| I | Coffee | 1 | -| II | Lunch | 2 | -| III | Cake | 1 | +| Nr. | Item Description | Quantity | +|-------|--------------------|------------| +| I | Coffee | 1 | +| II | Lunch | 2 | +| III | Cake | 1 | Docling Restaurant 2026 diff --git a/crates/fleischwolf/tests/data/html/expected/kvp_data_example.html.strict.md b/crates/fleischwolf/tests/data/html/expected/kvp_data_example.html.strict.md index 84998e53..948cb42a 100644 --- a/crates/fleischwolf/tests/data/html/expected/kvp_data_example.html.strict.md +++ b/crates/fleischwolf/tests/data/html/expected/kvp_data_example.html.strict.md @@ -40,10 +40,10 @@ Order items: Itemized list of products ordered with quantities. -| Nr. | Item Description | Quantity | -| - | - | - | -| I | Coffee | 1 | -| II | Lunch | 2 | -| III | Cake | 1 | +| Nr. | Item Description | Quantity | +|-------|--------------------|------------| +| I | Coffee | 1 | +| II | Lunch | 2 | +| III | Cake | 1 | Docling Restaurant 2026 diff --git a/crates/fleischwolf/tests/data/html/expected/table_01.html.md b/crates/fleischwolf/tests/data/html/expected/table_01.html.md index ce512c83..734df5d0 100644 --- a/crates/fleischwolf/tests/data/html/expected/table_01.html.md +++ b/crates/fleischwolf/tests/data/html/expected/table_01.html.md @@ -2,8 +2,8 @@ This is the first paragraph. -| A | B | -| - | - | +| A | B | +|------|------| | 1... | 2... | After table diff --git a/crates/fleischwolf/tests/data/html/expected/table_01.html.strict.md b/crates/fleischwolf/tests/data/html/expected/table_01.html.strict.md index ce512c83..734df5d0 100644 --- a/crates/fleischwolf/tests/data/html/expected/table_01.html.strict.md +++ b/crates/fleischwolf/tests/data/html/expected/table_01.html.strict.md @@ -2,8 +2,8 @@ This is the first paragraph. -| A | B | -| - | - | +| A | B | +|------|------| | 1... | 2... | After table diff --git a/crates/fleischwolf/tests/data/html/expected/table_02.html.md b/crates/fleischwolf/tests/data/html/expected/table_02.html.md index fe0f99de..be14a0f3 100644 --- a/crates/fleischwolf/tests/data/html/expected/table_02.html.md +++ b/crates/fleischwolf/tests/data/html/expected/table_02.html.md @@ -2,8 +2,8 @@ This is the first paragraph. -| A | B | -| - | - | +| A | B | +|-----------------------------------|------| | First Line Second Line Third Line | 2... | After table diff --git a/crates/fleischwolf/tests/data/html/expected/table_02.html.strict.md b/crates/fleischwolf/tests/data/html/expected/table_02.html.strict.md index fe0f99de..be14a0f3 100644 --- a/crates/fleischwolf/tests/data/html/expected/table_02.html.strict.md +++ b/crates/fleischwolf/tests/data/html/expected/table_02.html.strict.md @@ -2,8 +2,8 @@ This is the first paragraph. -| A | B | -| - | - | +| A | B | +|-----------------------------------|------| | First Line Second Line Third Line | 2... | After table diff --git a/crates/fleischwolf/tests/data/html/expected/table_03.html.md b/crates/fleischwolf/tests/data/html/expected/table_03.html.md index 3eee6976..e796cddd 100644 --- a/crates/fleischwolf/tests/data/html/expected/table_03.html.md +++ b/crates/fleischwolf/tests/data/html/expected/table_03.html.md @@ -2,8 +2,8 @@ This is the first paragraph. -| A | B | -| - | - | +| A | B | +|-----------------------------------------|------| | - First item - Second item - Third item | 2... | After table diff --git a/crates/fleischwolf/tests/data/html/expected/table_03.html.strict.md b/crates/fleischwolf/tests/data/html/expected/table_03.html.strict.md index 3eee6976..e796cddd 100644 --- a/crates/fleischwolf/tests/data/html/expected/table_03.html.strict.md +++ b/crates/fleischwolf/tests/data/html/expected/table_03.html.strict.md @@ -2,8 +2,8 @@ This is the first paragraph. -| A | B | -| - | - | +| A | B | +|-----------------------------------------|------| | - First item - Second item - Third item | 2... | After table diff --git a/crates/fleischwolf/tests/data/html/expected/table_04.html.md b/crates/fleischwolf/tests/data/html/expected/table_04.html.md index 83ee074d..d8435dcd 100644 --- a/crates/fleischwolf/tests/data/html/expected/table_04.html.md +++ b/crates/fleischwolf/tests/data/html/expected/table_04.html.md @@ -2,8 +2,8 @@ This is the first paragraph. -| A | B | -| - | - | +| A | B | +|----------------------------------------------------------------|------| | Some text before list - First item - Second item - Third item | 2... | After table diff --git a/crates/fleischwolf/tests/data/html/expected/table_04.html.strict.md b/crates/fleischwolf/tests/data/html/expected/table_04.html.strict.md index 83ee074d..d8435dcd 100644 --- a/crates/fleischwolf/tests/data/html/expected/table_04.html.strict.md +++ b/crates/fleischwolf/tests/data/html/expected/table_04.html.strict.md @@ -2,8 +2,8 @@ This is the first paragraph. -| A | B | -| - | - | +| A | B | +|----------------------------------------------------------------|------| | Some text before list - First item - Second item - Third item | 2... | After table diff --git a/crates/fleischwolf/tests/data/html/expected/table_05.html.md b/crates/fleischwolf/tests/data/html/expected/table_05.html.md index 6a7a75d6..f34c894d 100644 --- a/crates/fleischwolf/tests/data/html/expected/table_05.html.md +++ b/crates/fleischwolf/tests/data/html/expected/table_05.html.md @@ -2,8 +2,8 @@ This is the first paragraph. -| A | B | -| - | - | +| A | B | +|-------------------|------| | A1 B1 C1 D1 E1 F1 | 2... | After table diff --git a/crates/fleischwolf/tests/data/html/expected/table_05.html.strict.md b/crates/fleischwolf/tests/data/html/expected/table_05.html.strict.md index 6a7a75d6..f34c894d 100644 --- a/crates/fleischwolf/tests/data/html/expected/table_05.html.strict.md +++ b/crates/fleischwolf/tests/data/html/expected/table_05.html.strict.md @@ -2,8 +2,8 @@ This is the first paragraph. -| A | B | -| - | - | +| A | B | +|-------------------|------| | A1 B1 C1 D1 E1 F1 | 2... | After table diff --git a/crates/fleischwolf/tests/data/html/expected/table_06.html.md b/crates/fleischwolf/tests/data/html/expected/table_06.html.md index d3e0c977..e6207d5a 100644 --- a/crates/fleischwolf/tests/data/html/expected/table_06.html.md +++ b/crates/fleischwolf/tests/data/html/expected/table_06.html.md @@ -2,8 +2,8 @@ This is the first paragraph. -| A | B | -| - | - | +| A | B | +|---------------------------------------------------|------| | A1 B1 C1 D1 I II III IV V E1 E2 E3 E4 VII VIII F1 | 2... | After table diff --git a/crates/fleischwolf/tests/data/html/expected/table_06.html.strict.md b/crates/fleischwolf/tests/data/html/expected/table_06.html.strict.md index d3e0c977..e6207d5a 100644 --- a/crates/fleischwolf/tests/data/html/expected/table_06.html.strict.md +++ b/crates/fleischwolf/tests/data/html/expected/table_06.html.strict.md @@ -2,8 +2,8 @@ This is the first paragraph. -| A | B | -| - | - | +| A | B | +|---------------------------------------------------|------| | A1 B1 C1 D1 I II III IV V E1 E2 E3 E4 VII VIII F1 | 2... | After table diff --git a/crates/fleischwolf/tests/data/html/expected/table_with_heading_01.html.md b/crates/fleischwolf/tests/data/html/expected/table_with_heading_01.html.md index f54f4652..bf8ebaa3 100644 --- a/crates/fleischwolf/tests/data/html/expected/table_with_heading_01.html.md +++ b/crates/fleischwolf/tests/data/html/expected/table_with_heading_01.html.md @@ -1,7 +1,7 @@ Before tha table -| A | B | -| - | - | +| A | B | +|------|------| | 1... | 2... | After the table diff --git a/crates/fleischwolf/tests/data/html/expected/table_with_heading_01.html.strict.md b/crates/fleischwolf/tests/data/html/expected/table_with_heading_01.html.strict.md index f54f4652..bf8ebaa3 100644 --- a/crates/fleischwolf/tests/data/html/expected/table_with_heading_01.html.strict.md +++ b/crates/fleischwolf/tests/data/html/expected/table_with_heading_01.html.strict.md @@ -1,7 +1,7 @@ Before tha table -| A | B | -| - | - | +| A | B | +|------|------| | 1... | 2... | After the table diff --git a/crates/fleischwolf/tests/data/html/expected/table_with_heading_02.html.md b/crates/fleischwolf/tests/data/html/expected/table_with_heading_02.html.md index 60153bfe..2b95895c 100644 --- a/crates/fleischwolf/tests/data/html/expected/table_with_heading_02.html.md +++ b/crates/fleischwolf/tests/data/html/expected/table_with_heading_02.html.md @@ -2,9 +2,9 @@ Before the table -| ## A text | B | -| - | - | -| 1... | 2... | +| ## A text | B | +|--------------|------| +| 1... | 2... | ## Section After diff --git a/crates/fleischwolf/tests/data/html/expected/table_with_heading_02.html.strict.md b/crates/fleischwolf/tests/data/html/expected/table_with_heading_02.html.strict.md index 60153bfe..2b95895c 100644 --- a/crates/fleischwolf/tests/data/html/expected/table_with_heading_02.html.strict.md +++ b/crates/fleischwolf/tests/data/html/expected/table_with_heading_02.html.strict.md @@ -2,9 +2,9 @@ Before the table -| ## A text | B | -| - | - | -| 1... | 2... | +| ## A text | B | +|--------------|------| +| 1... | 2... | ## Section After diff --git a/crates/fleischwolf/tests/data/html/expected/wiki_duck.html.md b/crates/fleischwolf/tests/data/html/expected/wiki_duck.html.md index 6e5e303d..0ea9da23 100644 --- a/crates/fleischwolf/tests/data/html/expected/wiki_duck.html.md +++ b/crates/fleischwolf/tests/data/html/expected/wiki_duck.html.md @@ -278,20 +278,20 @@ This article is about the bird. For duck as a food, see [Duck as food](/wiki/Duc "Duckling" redirects here. For other uses, see [Duckling (disambiguation)](/wiki/Duckling_(disambiguation)) . -| Duck | Duck | -| - | - | -| | | -| [Bufflehead](/wiki/Bufflehead) ( *Bucephala albeola* ) | [Bufflehead](/wiki/Bufflehead) ( *Bucephala albeola* ) | +| Duck | Duck | +|-------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------| +| | | +| [Bufflehead](/wiki/Bufflehead) ( *Bucephala albeola* ) | [Bufflehead](/wiki/Bufflehead) ( *Bucephala albeola* ) | | [Scientific classification](/wiki/Taxonomy_(biology)) Edit this classification | [Scientific classification](/wiki/Taxonomy_(biology)) Edit this classification | -| Domain: | [Eukaryota](/wiki/Eukaryote) | -| Kingdom: | [Animalia](/wiki/Animal) | -| Phylum: | [Chordata](/wiki/Chordate) | -| Class: | [Aves](/wiki/Bird) | -| Order: | [Anseriformes](/wiki/Anseriformes) | -| Superfamily: | [Anatoidea](/wiki/Anatoidea) | -| Family: | [Anatidae](/wiki/Anatidae) | -| Subfamilies | Subfamilies | -| See text | See text | +| Domain: | [Eukaryota](/wiki/Eukaryote) | +| Kingdom: | [Animalia](/wiki/Animal) | +| Phylum: | [Chordata](/wiki/Chordate) | +| Class: | [Aves](/wiki/Bird) | +| Order: | [Anseriformes](/wiki/Anseriformes) | +| Superfamily: | [Anatoidea](/wiki/Anatoidea) | +| Family: | [Anatidae](/wiki/Anatidae) | +| Subfamilies | Subfamilies | +| See text | See text | **Duck** is the common name for numerous species of [waterfowl](/wiki/Waterfowl) in the [family](/wiki/Family_(biology)) [Anatidae](/wiki/Anatidae) . Ducks are generally smaller and shorter-necked than [swans](/wiki/Swan) and [geese](/wiki/Goose) , which are members of the same family. Divided among several subfamilies, they are a [form taxon](/wiki/Form_taxon) ; they do not represent a [monophyletic group](/wiki/Monophyletic_group) (the group of all descendants of a single common ancestral species), since swans and geese are not considered ducks. Ducks are mostly [aquatic birds](/wiki/Aquatic_bird) , and may be found in both fresh water and sea water. @@ -546,10 +546,10 @@ The 1992 Disney film [*The Mighty Ducks*](/wiki/The_Mighty_Ducks_(film)) , starr - [Ducks on postage stamps](http://www.stampsbook.org/subject/Duck.html) [Archived](https://web.archive.org/web/20130513022903/http://www.stampsbook.org/subject/Duck.html) 2013-05-13 at the [Wayback Machine](/wiki/Wayback_Machine) - [*Ducks at a Distance, by Rob Hines*](https://gutenberg.org/ebooks/18884) at [Project Gutenberg](/wiki/Project_Gutenberg) - A modern illustrated guide to identification of US waterfowl -| [Authority control databases](/wiki/Help:Authority_control) Edit this at Wikidata | [Authority control databases](/wiki/Help:Authority_control) Edit this at Wikidata | -| - | - | -| National | - [United States](https://id.loc.gov/authorities/sh85039879) - [France](https://catalogue.bnf.fr/ark:/12148/cb119761481) - [BnF data](https://data.bnf.fr/ark:/12148/cb119761481) - [Japan](https://id.ndl.go.jp/auth/ndlna/00564819) - [Latvia](https://kopkatalogs.lv/F?func=direct&local_base=lnc10&doc_number=000090751&P_CON_LNG=ENG) - [Israel](http://olduli.nli.org.il/F/?func=find-b&local_base=NLX10&find_code=UID&request=987007565486205171) | -| Other | - [IdRef](https://www.idref.fr/027796124) | +| [Authority control databases](/wiki/Help:Authority_control) Edit this at Wikidata | [Authority control databases](/wiki/Help:Authority_control) Edit this at Wikidata | +|------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| National | - [United States](https://id.loc.gov/authorities/sh85039879) - [France](https://catalogue.bnf.fr/ark:/12148/cb119761481) - [BnF data](https://data.bnf.fr/ark:/12148/cb119761481) - [Japan](https://id.ndl.go.jp/auth/ndlna/00564819) - [Latvia](https://kopkatalogs.lv/F?func=direct&local_base=lnc10&doc_number=000090751&P_CON_LNG=ENG) - [Israel](http://olduli.nli.org.il/F/?func=find-b&local_base=NLX10&find_code=UID&request=987007565486205171) | +| Other | - [IdRef](https://www.idref.fr/027796124) | Retrieved from " [https://en.wikipedia.org/w/index.php?title=Duck&oldid=1246843351](https://en.wikipedia.org/w/index.php?title=Duck&oldid=1246843351) " diff --git a/crates/fleischwolf/tests/data/html/expected/wiki_duck.html.strict.md b/crates/fleischwolf/tests/data/html/expected/wiki_duck.html.strict.md index baa32ec5..9f95910a 100644 --- a/crates/fleischwolf/tests/data/html/expected/wiki_duck.html.strict.md +++ b/crates/fleischwolf/tests/data/html/expected/wiki_duck.html.strict.md @@ -278,20 +278,20 @@ This article is about the bird. For duck as a food, see [Duck as food](/wiki/Duc "Duckling" redirects here. For other uses, see [Duckling (disambiguation)](/wiki/Duckling_(disambiguation)). -| Duck | Duck | -| - | - | -| | | -| [Bufflehead](/wiki/Bufflehead) ( *Bucephala albeola* ) | [Bufflehead](/wiki/Bufflehead) ( *Bucephala albeola* ) | +| Duck | Duck | +|-------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------| +| | | +| [Bufflehead](/wiki/Bufflehead) ( *Bucephala albeola* ) | [Bufflehead](/wiki/Bufflehead) ( *Bucephala albeola* ) | | [Scientific classification](/wiki/Taxonomy_(biology)) Edit this classification | [Scientific classification](/wiki/Taxonomy_(biology)) Edit this classification | -| Domain: | [Eukaryota](/wiki/Eukaryote) | -| Kingdom: | [Animalia](/wiki/Animal) | -| Phylum: | [Chordata](/wiki/Chordate) | -| Class: | [Aves](/wiki/Bird) | -| Order: | [Anseriformes](/wiki/Anseriformes) | -| Superfamily: | [Anatoidea](/wiki/Anatoidea) | -| Family: | [Anatidae](/wiki/Anatidae) | -| Subfamilies | Subfamilies | -| See text | See text | +| Domain: | [Eukaryota](/wiki/Eukaryote) | +| Kingdom: | [Animalia](/wiki/Animal) | +| Phylum: | [Chordata](/wiki/Chordate) | +| Class: | [Aves](/wiki/Bird) | +| Order: | [Anseriformes](/wiki/Anseriformes) | +| Superfamily: | [Anatoidea](/wiki/Anatoidea) | +| Family: | [Anatidae](/wiki/Anatidae) | +| Subfamilies | Subfamilies | +| See text | See text | **Duck** is the common name for numerous species of [waterfowl](/wiki/Waterfowl) in the [family](/wiki/Family_(biology)) [Anatidae](/wiki/Anatidae). Ducks are generally smaller and shorter-necked than [swans](/wiki/Swan) and [geese](/wiki/Goose), which are members of the same family. Divided among several subfamilies, they are a [form taxon](/wiki/Form_taxon); they do not represent a [monophyletic group](/wiki/Monophyletic_group) (the group of all descendants of a single common ancestral species), since swans and geese are not considered ducks. Ducks are mostly [aquatic birds](/wiki/Aquatic_bird), and may be found in both fresh water and sea water. @@ -546,10 +546,10 @@ The 1992 Disney film [*The Mighty Ducks*](/wiki/The_Mighty_Ducks_(film)), starri - [Ducks on postage stamps](http://www.stampsbook.org/subject/Duck.html) [Archived](https://web.archive.org/web/20130513022903/http://www.stampsbook.org/subject/Duck.html) 2013-05-13 at the [Wayback Machine](/wiki/Wayback_Machine) - [*Ducks at a Distance, by Rob Hines*](https://gutenberg.org/ebooks/18884) at [Project Gutenberg](/wiki/Project_Gutenberg) - A modern illustrated guide to identification of US waterfowl -| [Authority control databases](/wiki/Help:Authority_control) Edit this at Wikidata | [Authority control databases](/wiki/Help:Authority_control) Edit this at Wikidata | -| - | - | -| National | - [United States](https://id.loc.gov/authorities/sh85039879) - [France](https://catalogue.bnf.fr/ark:/12148/cb119761481) - [BnF data](https://data.bnf.fr/ark:/12148/cb119761481) - [Japan](https://id.ndl.go.jp/auth/ndlna/00564819) - [Latvia](https://kopkatalogs.lv/F?func=direct&local_base=lnc10&doc_number=000090751&P_CON_LNG=ENG) - [Israel](http://olduli.nli.org.il/F/?func=find-b&local_base=NLX10&find_code=UID&request=987007565486205171) | -| Other | - [IdRef](https://www.idref.fr/027796124) | +| [Authority control databases](/wiki/Help:Authority_control) Edit this at Wikidata | [Authority control databases](/wiki/Help:Authority_control) Edit this at Wikidata | +|------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| National | - [United States](https://id.loc.gov/authorities/sh85039879) - [France](https://catalogue.bnf.fr/ark:/12148/cb119761481) - [BnF data](https://data.bnf.fr/ark:/12148/cb119761481) - [Japan](https://id.ndl.go.jp/auth/ndlna/00564819) - [Latvia](https://kopkatalogs.lv/F?func=direct&local_base=lnc10&doc_number=000090751&P_CON_LNG=ENG) - [Israel](http://olduli.nli.org.il/F/?func=find-b&local_base=NLX10&find_code=UID&request=987007565486205171) | +| Other | - [IdRef](https://www.idref.fr/027796124) | Retrieved from " [https://en.wikipedia.org/w/index.php?title=Duck&oldid=1246843351](https://en.wikipedia.org/w/index.php?title=Duck&oldid=1246843351) " diff --git a/crates/fleischwolf/tests/data/json_docling/expected/csv-comma.csv.json.md b/crates/fleischwolf/tests/data/json_docling/expected/csv-comma.csv.json.md index e7d4dfa3..bf37c6e7 100644 --- a/crates/fleischwolf/tests/data/json_docling/expected/csv-comma.csv.json.md +++ b/crates/fleischwolf/tests/data/json_docling/expected/csv-comma.csv.json.md @@ -1,7 +1,7 @@ -| Index | Customer Id | First Name | Last Name | Company | City | Country | Phone 1 | Phone 2 | Email | Subscription Date | Website | -| - | - | - | - | - | - | - | - | - | - | - | - | -| 1 | DD37Cf93aecA6Dc | Sheryl | Baxter | Rasmussen Group | East Leonard | Chile | 229.077.5154 | 397.884.0519x718 | zunigavanessa@smith.info | 2020-08-24 | http://www.stephenson.com/ | -| 2 | 1Ef7b82A4CAAD10 | Preston | Lozano, Dr | Vega-Gentry | East Jimmychester | Djibouti | 5153435776 | 686-620-1820x944 | vmata@colon.com | 2021-04-23 | http://www.hobbs.com/ | -| 3 | 6F94879bDAfE5a6 | Roy | Berry | Murillo-Perry | Isabelborough | Antigua and Barbuda | +1-539-402-0259 | (496)978-3969x58947 | beckycarr@hogan.com | 2020-03-25 | http://www.lawrence.com/ | -| 4 | 5Cef8BFA16c5e3c | Linda | Olsen | Dominguez, Mcmillan and Donovan | Bensonview | Dominican Republic | 001-808-617-6467x12895 | +1-813-324-8756 | stanleyblackwell@benson.org | 2020-06-02 | http://www.good-lyons.com/ | -| 5 | 053d585Ab6b3159 | Joanna | Bender | Martin, Lang and Andrade | West Priscilla | Slovakia (Slovak Republic) | 001-234-203-0635x76146 | 001-199-446-3860x3486 | colinalvarado@miles.net | 2021-04-17 | https://goodwin-ingram.com/ | +| Index | Customer Id | First Name | Last Name | Company | City | Country | Phone 1 | Phone 2 | Email | Subscription Date | Website | +|---------|-----------------|--------------|-------------|---------------------------------|-------------------|----------------------------|------------------------|-----------------------|-----------------------------|---------------------|-----------------------------| +| 1 | DD37Cf93aecA6Dc | Sheryl | Baxter | Rasmussen Group | East Leonard | Chile | 229.077.5154 | 397.884.0519x718 | zunigavanessa@smith.info | 2020-08-24 | http://www.stephenson.com/ | +| 2 | 1Ef7b82A4CAAD10 | Preston | Lozano, Dr | Vega-Gentry | East Jimmychester | Djibouti | 5153435776 | 686-620-1820x944 | vmata@colon.com | 2021-04-23 | http://www.hobbs.com/ | +| 3 | 6F94879bDAfE5a6 | Roy | Berry | Murillo-Perry | Isabelborough | Antigua and Barbuda | +1-539-402-0259 | (496)978-3969x58947 | beckycarr@hogan.com | 2020-03-25 | http://www.lawrence.com/ | +| 4 | 5Cef8BFA16c5e3c | Linda | Olsen | Dominguez, Mcmillan and Donovan | Bensonview | Dominican Republic | 001-808-617-6467x12895 | +1-813-324-8756 | stanleyblackwell@benson.org | 2020-06-02 | http://www.good-lyons.com/ | +| 5 | 053d585Ab6b3159 | Joanna | Bender | Martin, Lang and Andrade | West Priscilla | Slovakia (Slovak Republic) | 001-234-203-0635x76146 | 001-199-446-3860x3486 | colinalvarado@miles.net | 2021-04-17 | https://goodwin-ingram.com/ | diff --git a/crates/fleischwolf/tests/data/json_docling/expected/csv-comma.csv.json.strict.md b/crates/fleischwolf/tests/data/json_docling/expected/csv-comma.csv.json.strict.md index e7d4dfa3..bf37c6e7 100644 --- a/crates/fleischwolf/tests/data/json_docling/expected/csv-comma.csv.json.strict.md +++ b/crates/fleischwolf/tests/data/json_docling/expected/csv-comma.csv.json.strict.md @@ -1,7 +1,7 @@ -| Index | Customer Id | First Name | Last Name | Company | City | Country | Phone 1 | Phone 2 | Email | Subscription Date | Website | -| - | - | - | - | - | - | - | - | - | - | - | - | -| 1 | DD37Cf93aecA6Dc | Sheryl | Baxter | Rasmussen Group | East Leonard | Chile | 229.077.5154 | 397.884.0519x718 | zunigavanessa@smith.info | 2020-08-24 | http://www.stephenson.com/ | -| 2 | 1Ef7b82A4CAAD10 | Preston | Lozano, Dr | Vega-Gentry | East Jimmychester | Djibouti | 5153435776 | 686-620-1820x944 | vmata@colon.com | 2021-04-23 | http://www.hobbs.com/ | -| 3 | 6F94879bDAfE5a6 | Roy | Berry | Murillo-Perry | Isabelborough | Antigua and Barbuda | +1-539-402-0259 | (496)978-3969x58947 | beckycarr@hogan.com | 2020-03-25 | http://www.lawrence.com/ | -| 4 | 5Cef8BFA16c5e3c | Linda | Olsen | Dominguez, Mcmillan and Donovan | Bensonview | Dominican Republic | 001-808-617-6467x12895 | +1-813-324-8756 | stanleyblackwell@benson.org | 2020-06-02 | http://www.good-lyons.com/ | -| 5 | 053d585Ab6b3159 | Joanna | Bender | Martin, Lang and Andrade | West Priscilla | Slovakia (Slovak Republic) | 001-234-203-0635x76146 | 001-199-446-3860x3486 | colinalvarado@miles.net | 2021-04-17 | https://goodwin-ingram.com/ | +| Index | Customer Id | First Name | Last Name | Company | City | Country | Phone 1 | Phone 2 | Email | Subscription Date | Website | +|---------|-----------------|--------------|-------------|---------------------------------|-------------------|----------------------------|------------------------|-----------------------|-----------------------------|---------------------|-----------------------------| +| 1 | DD37Cf93aecA6Dc | Sheryl | Baxter | Rasmussen Group | East Leonard | Chile | 229.077.5154 | 397.884.0519x718 | zunigavanessa@smith.info | 2020-08-24 | http://www.stephenson.com/ | +| 2 | 1Ef7b82A4CAAD10 | Preston | Lozano, Dr | Vega-Gentry | East Jimmychester | Djibouti | 5153435776 | 686-620-1820x944 | vmata@colon.com | 2021-04-23 | http://www.hobbs.com/ | +| 3 | 6F94879bDAfE5a6 | Roy | Berry | Murillo-Perry | Isabelborough | Antigua and Barbuda | +1-539-402-0259 | (496)978-3969x58947 | beckycarr@hogan.com | 2020-03-25 | http://www.lawrence.com/ | +| 4 | 5Cef8BFA16c5e3c | Linda | Olsen | Dominguez, Mcmillan and Donovan | Bensonview | Dominican Republic | 001-808-617-6467x12895 | +1-813-324-8756 | stanleyblackwell@benson.org | 2020-06-02 | http://www.good-lyons.com/ | +| 5 | 053d585Ab6b3159 | Joanna | Bender | Martin, Lang and Andrade | West Priscilla | Slovakia (Slovak Republic) | 001-234-203-0635x76146 | 001-199-446-3860x3486 | colinalvarado@miles.net | 2021-04-17 | https://goodwin-ingram.com/ | diff --git a/crates/fleischwolf/tests/data/latex/expected/example_02.tex.md b/crates/fleischwolf/tests/data/latex/expected/example_02.tex.md index 0c3fce03..5c02932d 100644 --- a/crates/fleischwolf/tests/data/latex/expected/example_02.tex.md +++ b/crates/fleischwolf/tests/data/latex/expected/example_02.tex.md @@ -12,10 +12,10 @@ $$\nabla \times \mathbf{E} = -\frac{\partial \mathbf{B}}{\partial t}$$ ## Table Example -| Name | Age | City | -| - | - | - | -| Alice | 25 | Boston | -| Bob | 30 | Seattle | +| Name | Age | City | +|--------|-------|---------| +| Alice | 25 | Boston | +| Bob | 30 | Seattle | Sample table diff --git a/crates/fleischwolf/tests/data/latex/expected/example_02.tex.strict.md b/crates/fleischwolf/tests/data/latex/expected/example_02.tex.strict.md index 0c3fce03..5c02932d 100644 --- a/crates/fleischwolf/tests/data/latex/expected/example_02.tex.strict.md +++ b/crates/fleischwolf/tests/data/latex/expected/example_02.tex.strict.md @@ -12,10 +12,10 @@ $$\nabla \times \mathbf{E} = -\frac{\partial \mathbf{B}}{\partial t}$$ ## Table Example -| Name | Age | City | -| - | - | - | -| Alice | 25 | Boston | -| Bob | 30 | Seattle | +| Name | Age | City | +|--------|-------|---------| +| Alice | 25 | Boston | +| Bob | 30 | Seattle | Sample table diff --git a/crates/fleischwolf/tests/data/md/expected/duck.md.md b/crates/fleischwolf/tests/data/md/expected/duck.md.md index d148a8b5..2a8d1efb 100644 --- a/crates/fleischwolf/tests/data/md/expected/duck.md.md +++ b/crates/fleischwolf/tests/data/md/expected/duck.md.md @@ -30,11 +30,11 @@ I like to eat leaves Here are some interesting things a respectful duck could eat: -| | Food | Calories per portion | -| - | - | - | -| Leaves | Ash, Elm, Maple | 50 | -| Berries | Blueberry, Strawberry, Cranberry | 150 | -| Grain | Corn, Buckwheat, Barley | 200 | +| | Food | Calories per portion | +|---------|----------------------------------|------------------------| +| Leaves | Ash, Elm, Maple | 50 | +| Berries | Blueberry, Strawberry, Cranberry | 150 | +| Grain | Corn, Buckwheat, Barley | 200 | And let’s add another list in the end: diff --git a/crates/fleischwolf/tests/data/md/expected/duck.md.strict.md b/crates/fleischwolf/tests/data/md/expected/duck.md.strict.md index d148a8b5..2a8d1efb 100644 --- a/crates/fleischwolf/tests/data/md/expected/duck.md.strict.md +++ b/crates/fleischwolf/tests/data/md/expected/duck.md.strict.md @@ -30,11 +30,11 @@ I like to eat leaves Here are some interesting things a respectful duck could eat: -| | Food | Calories per portion | -| - | - | - | -| Leaves | Ash, Elm, Maple | 50 | -| Berries | Blueberry, Strawberry, Cranberry | 150 | -| Grain | Corn, Buckwheat, Barley | 200 | +| | Food | Calories per portion | +|---------|----------------------------------|------------------------| +| Leaves | Ash, Elm, Maple | 50 | +| Berries | Blueberry, Strawberry, Cranberry | 150 | +| Grain | Corn, Buckwheat, Barley | 200 | And let’s add another list in the end: diff --git a/crates/fleischwolf/tests/data/md/expected/ending_with_table.md.md b/crates/fleischwolf/tests/data/md/expected/ending_with_table.md.md index 55a5ec5a..9c179fe0 100644 --- a/crates/fleischwolf/tests/data/md/expected/ending_with_table.md.md +++ b/crates/fleischwolf/tests/data/md/expected/ending_with_table.md.md @@ -1,6 +1,6 @@ -| Character | Name in German | Name in French | Name in Italian | -| - | - | - | - | -| Scrooge McDuck | Dagobert Duck | Balthazar Picsou | Paperone | -| Huey | Tick | Riri | Qui | -| Dewey | Trick | Fifi | Quo | -| Louie | Track | Loulou | Qua | +| Character | Name in German | Name in French | Name in Italian | +|----------------|------------------|------------------|-------------------| +| Scrooge McDuck | Dagobert Duck | Balthazar Picsou | Paperone | +| Huey | Tick | Riri | Qui | +| Dewey | Trick | Fifi | Quo | +| Louie | Track | Loulou | Qua | diff --git a/crates/fleischwolf/tests/data/md/expected/ending_with_table.md.strict.md b/crates/fleischwolf/tests/data/md/expected/ending_with_table.md.strict.md index 55a5ec5a..9c179fe0 100644 --- a/crates/fleischwolf/tests/data/md/expected/ending_with_table.md.strict.md +++ b/crates/fleischwolf/tests/data/md/expected/ending_with_table.md.strict.md @@ -1,6 +1,6 @@ -| Character | Name in German | Name in French | Name in Italian | -| - | - | - | - | -| Scrooge McDuck | Dagobert Duck | Balthazar Picsou | Paperone | -| Huey | Tick | Riri | Qui | -| Dewey | Trick | Fifi | Quo | -| Louie | Track | Loulou | Qua | +| Character | Name in German | Name in French | Name in Italian | +|----------------|------------------|------------------|-------------------| +| Scrooge McDuck | Dagobert Duck | Balthazar Picsou | Paperone | +| Huey | Tick | Riri | Qui | +| Dewey | Trick | Fifi | Quo | +| Louie | Track | Loulou | Qua | diff --git a/crates/fleischwolf/tests/data/md/expected/escaped_characters.md.md b/crates/fleischwolf/tests/data/md/expected/escaped_characters.md.md index 84f40db5..95bdd09a 100644 --- a/crates/fleischwolf/tests/data/md/expected/escaped_characters.md.md +++ b/crates/fleischwolf/tests/data/md/expected/escaped_characters.md.md @@ -24,14 +24,14 @@ Text: 00:16.000 ----> 00:18.000 & < > " ' # Table -| Key | Example | -| - | - | -| Ampersand | & | -| Less-than | < | -| Greater-than | > | -| Quotes | " | -| Apostrophes | ' | -| Pipe | | | | | +| Key | Example | +|--------------|----------------------| +| Ampersand | & | +| Less-than | < | +| Greater-than | > | +| Quotes | " | +| Apostrophes | ' | +| Pipe | | | | | The pipe symbol (| or `|` ) only needs to be escaped in tables. diff --git a/crates/fleischwolf/tests/data/md/expected/escaped_characters.md.strict.md b/crates/fleischwolf/tests/data/md/expected/escaped_characters.md.strict.md index 02502d65..e0a6e888 100644 --- a/crates/fleischwolf/tests/data/md/expected/escaped_characters.md.strict.md +++ b/crates/fleischwolf/tests/data/md/expected/escaped_characters.md.strict.md @@ -22,14 +22,14 @@ Text: 00:16.000 ----> 00:18.000 & < > " ' # Table -| Key | Example | -| - | - | -| Ampersand | & | -| Less-than | < | -| Greater-than | > | -| Quotes | " | -| Apostrophes | ' | -| Pipe | | | | | +| Key | Example | +|--------------|----------------------| +| Ampersand | & | +| Less-than | < | +| Greater-than | > | +| Quotes | " | +| Apostrophes | ' | +| Pipe | | | | | The pipe symbol (| or `|`) only needs to be escaped in tables. diff --git a/crates/fleischwolf/tests/data/md/expected/inline_and_formatting.md.md b/crates/fleischwolf/tests/data/md/expected/inline_and_formatting.md.md index 9051b9a4..282be7f5 100644 --- a/crates/fleischwolf/tests/data/md/expected/inline_and_formatting.md.md +++ b/crates/fleischwolf/tests/data/md/expected/inline_and_formatting.md.md @@ -27,6 +27,6 @@ Some *`formatted_code`* ## Table Heading -| Bold Heading | Italic Heading | -| - | - | -| data a | data b | +| Bold Heading | Italic Heading | +|----------------|------------------| +| data a | data b | diff --git a/crates/fleischwolf/tests/data/md/expected/inline_and_formatting.md.strict.md b/crates/fleischwolf/tests/data/md/expected/inline_and_formatting.md.strict.md index 07d680f1..30b769f9 100644 --- a/crates/fleischwolf/tests/data/md/expected/inline_and_formatting.md.strict.md +++ b/crates/fleischwolf/tests/data/md/expected/inline_and_formatting.md.strict.md @@ -27,6 +27,6 @@ Some *`formatted_code`* ## Table Heading -| Bold Heading | Italic Heading | -| - | - | -| data a | data b | +| Bold Heading | Italic Heading | +|----------------|------------------| +| data a | data b | diff --git a/crates/fleischwolf/tests/data/md/expected/mixed.md.md b/crates/fleischwolf/tests/data/md/expected/mixed.md.md index 4db57e0e..5524e09d 100644 --- a/crates/fleischwolf/tests/data/md/expected/mixed.md.md +++ b/crates/fleischwolf/tests/data/md/expected/mixed.md.md @@ -6,12 +6,12 @@ Some text Here is a table: -| Character | Name in German | Name in French | Name in Italian | -| - | - | - | - | -| Scrooge McDuck | Dagobert Duck | Balthazar Picsou | Paperone | -| Huey | Tick | Riri | Qui | -| Dewey | Trick | Fifi | Quo | -| Louie | Track | Loulou | Qua | +| Character | Name in German | Name in French | Name in Italian | +|----------------|------------------|------------------|-------------------| +| Scrooge McDuck | Dagobert Duck | Balthazar Picsou | Paperone | +| Huey | Tick | Riri | Qui | +| Dewey | Trick | Fifi | Quo | +| Louie | Track | Loulou | Qua | And here is more HTML: diff --git a/crates/fleischwolf/tests/data/md/expected/mixed.md.strict.md b/crates/fleischwolf/tests/data/md/expected/mixed.md.strict.md index 4db57e0e..5524e09d 100644 --- a/crates/fleischwolf/tests/data/md/expected/mixed.md.strict.md +++ b/crates/fleischwolf/tests/data/md/expected/mixed.md.strict.md @@ -6,12 +6,12 @@ Some text Here is a table: -| Character | Name in German | Name in French | Name in Italian | -| - | - | - | - | -| Scrooge McDuck | Dagobert Duck | Balthazar Picsou | Paperone | -| Huey | Tick | Riri | Qui | -| Dewey | Trick | Fifi | Quo | -| Louie | Track | Loulou | Qua | +| Character | Name in German | Name in French | Name in Italian | +|----------------|------------------|------------------|-------------------| +| Scrooge McDuck | Dagobert Duck | Balthazar Picsou | Paperone | +| Huey | Tick | Riri | Qui | +| Dewey | Trick | Fifi | Quo | +| Louie | Track | Loulou | Qua | And here is more HTML: diff --git a/crates/fleischwolf/tests/data/md_deepseek/expected/deepseek_example.md.md b/crates/fleischwolf/tests/data/md_deepseek/expected/deepseek_example.md.md index 3c2ae446..8a4342b7 100644 --- a/crates/fleischwolf/tests/data/md_deepseek/expected/deepseek_example.md.md +++ b/crates/fleischwolf/tests/data/md_deepseek/expected/deepseek_example.md.md @@ -4,17 +4,17 @@ We have chosen the PubTabNet data set to perform HPO, since it includes a highly Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer 9 architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart. -| # enc-layers | # dec-layers | Language | TEDs | TEDs | TEDs | mAP (0.75) | Inference time (secs) | -| - | - | - | - | - | - | - | - | -| # enc-layers | # dec-layers | Language | simple | complex | all | mAP (0.75) | Inference time (secs) | -| 6 | 6 | OTSL | 0.965 | 0.934 | 0.955 | 0.88 | 2.73 | -| 6 | 6 | HTML | 0.969 | 0.927 | 0.955 | 0.857 | 5.39 | -| 4 | 4 | OTSL | 0.938 | 0.904 | 0.927 | 0.853 | 1.97 | -| 4 | 4 | HTML | 0.952 | 0.909 | 0.938 | 0.843 | 3.77 | -| 2 | 4 | OTSL | 0.923 | 0.897 | 0.915 | 0.859 | 1.91 | -| 2 | 4 | HTML | 0.945 | 0.901 | 0.931 | 0.834 | 3.81 | -| 4 | 2 | OTSL | 0.952 | 0.92 | 0.942 | 0.857 | 1.22 | -| 4 | 2 | HTML | 0.944 | 0.903 | 0.931 | 0.824 | 2 | +| # enc-layers | # dec-layers | Language | TEDs | TEDs | TEDs | mAP (0.75) | Inference time (secs) | +|----------------|----------------|------------|--------|---------|--------|--------------|-------------------------| +| # enc-layers | # dec-layers | Language | simple | complex | all | mAP (0.75) | Inference time (secs) | +| 6 | 6 | OTSL | 0.965 | 0.934 | 0.955 | 0.88 | 2.73 | +| 6 | 6 | HTML | 0.969 | 0.927 | 0.955 | 0.857 | 5.39 | +| 4 | 4 | OTSL | 0.938 | 0.904 | 0.927 | 0.853 | 1.97 | +| 4 | 4 | HTML | 0.952 | 0.909 | 0.938 | 0.843 | 3.77 | +| 2 | 4 | OTSL | 0.923 | 0.897 | 0.915 | 0.859 | 1.91 | +| 2 | 4 | HTML | 0.945 | 0.901 | 0.931 | 0.834 | 3.81 | +| 4 | 2 | OTSL | 0.952 | 0.92 | 0.942 | 0.857 | 1.22 | +| 4 | 2 | HTML | 0.944 | 0.903 | 0.931 | 0.824 | 2 | ### 5.2 Quantitative Results diff --git a/crates/fleischwolf/tests/data/md_deepseek/expected/deepseek_example.md.strict.md b/crates/fleischwolf/tests/data/md_deepseek/expected/deepseek_example.md.strict.md index 3c2ae446..8a4342b7 100644 --- a/crates/fleischwolf/tests/data/md_deepseek/expected/deepseek_example.md.strict.md +++ b/crates/fleischwolf/tests/data/md_deepseek/expected/deepseek_example.md.strict.md @@ -4,17 +4,17 @@ We have chosen the PubTabNet data set to perform HPO, since it includes a highly Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer 9 architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart. -| # enc-layers | # dec-layers | Language | TEDs | TEDs | TEDs | mAP (0.75) | Inference time (secs) | -| - | - | - | - | - | - | - | - | -| # enc-layers | # dec-layers | Language | simple | complex | all | mAP (0.75) | Inference time (secs) | -| 6 | 6 | OTSL | 0.965 | 0.934 | 0.955 | 0.88 | 2.73 | -| 6 | 6 | HTML | 0.969 | 0.927 | 0.955 | 0.857 | 5.39 | -| 4 | 4 | OTSL | 0.938 | 0.904 | 0.927 | 0.853 | 1.97 | -| 4 | 4 | HTML | 0.952 | 0.909 | 0.938 | 0.843 | 3.77 | -| 2 | 4 | OTSL | 0.923 | 0.897 | 0.915 | 0.859 | 1.91 | -| 2 | 4 | HTML | 0.945 | 0.901 | 0.931 | 0.834 | 3.81 | -| 4 | 2 | OTSL | 0.952 | 0.92 | 0.942 | 0.857 | 1.22 | -| 4 | 2 | HTML | 0.944 | 0.903 | 0.931 | 0.824 | 2 | +| # enc-layers | # dec-layers | Language | TEDs | TEDs | TEDs | mAP (0.75) | Inference time (secs) | +|----------------|----------------|------------|--------|---------|--------|--------------|-------------------------| +| # enc-layers | # dec-layers | Language | simple | complex | all | mAP (0.75) | Inference time (secs) | +| 6 | 6 | OTSL | 0.965 | 0.934 | 0.955 | 0.88 | 2.73 | +| 6 | 6 | HTML | 0.969 | 0.927 | 0.955 | 0.857 | 5.39 | +| 4 | 4 | OTSL | 0.938 | 0.904 | 0.927 | 0.853 | 1.97 | +| 4 | 4 | HTML | 0.952 | 0.909 | 0.938 | 0.843 | 3.77 | +| 2 | 4 | OTSL | 0.923 | 0.897 | 0.915 | 0.859 | 1.91 | +| 2 | 4 | HTML | 0.945 | 0.901 | 0.931 | 0.834 | 3.81 | +| 4 | 2 | OTSL | 0.952 | 0.92 | 0.942 | 0.857 | 1.22 | +| 4 | 2 | HTML | 0.944 | 0.903 | 0.931 | 0.824 | 2 | ### 5.2 Quantitative Results diff --git a/crates/fleischwolf/tests/data/odf/expected/odf_presentation_02.odp.md b/crates/fleischwolf/tests/data/odf/expected/odf_presentation_02.odp.md index 0d2a5619..38f12a3d 100644 --- a/crates/fleischwolf/tests/data/odf/expected/odf_presentation_02.odp.md +++ b/crates/fleischwolf/tests/data/odf/expected/odf_presentation_02.odp.md @@ -1,8 +1,8 @@ -| Column 1 | Column 2 | Column 3 | Column 4 | Column 5 | -| - | - | - | - | - | -| Row 1 | Merged cells row 1-3, col 2-3 | Merged cells row 1-3, col 2-3 | | | -| Row 2 | | | | | -| Row 3 | | | | | +| Column 1 | Column 2 | Column 3 | Column 4 | Column 5 | +|------------|-------------------------------|-------------------------------|------------|------------| +| Row 1 | Merged cells row 1-3, col 2-3 | Merged cells row 1-3, col 2-3 | | | +| Row 2 | | | | | +| Row 3 | | | | | Complex list diff --git a/crates/fleischwolf/tests/data/odf/expected/odf_presentation_02.odp.strict.md b/crates/fleischwolf/tests/data/odf/expected/odf_presentation_02.odp.strict.md index 0d2a5619..38f12a3d 100644 --- a/crates/fleischwolf/tests/data/odf/expected/odf_presentation_02.odp.strict.md +++ b/crates/fleischwolf/tests/data/odf/expected/odf_presentation_02.odp.strict.md @@ -1,8 +1,8 @@ -| Column 1 | Column 2 | Column 3 | Column 4 | Column 5 | -| - | - | - | - | - | -| Row 1 | Merged cells row 1-3, col 2-3 | Merged cells row 1-3, col 2-3 | | | -| Row 2 | | | | | -| Row 3 | | | | | +| Column 1 | Column 2 | Column 3 | Column 4 | Column 5 | +|------------|-------------------------------|-------------------------------|------------|------------| +| Row 1 | Merged cells row 1-3, col 2-3 | Merged cells row 1-3, col 2-3 | | | +| Row 2 | | | | | +| Row 3 | | | | | Complex list diff --git a/crates/fleischwolf/tests/data/odf/expected/odf_table_with_title_01.ods.md b/crates/fleischwolf/tests/data/odf/expected/odf_table_with_title_01.ods.md index e143a6e5..cde353ea 100644 --- a/crates/fleischwolf/tests/data/odf/expected/odf_table_with_title_01.ods.md +++ b/crates/fleischwolf/tests/data/odf/expected/odf_table_with_title_01.ods.md @@ -1,9 +1,9 @@ -| | Number of freshwater ducks per year | | -| - | - | - | -| | Year | Freshwater Ducks | -| | 2019 | 120 | -| | 2020 | 135 | -| | 2021 | 150 | -| | 2022 | 170 | -| | 2023 | 160 | -| | 2024 | 180 | +| | Number of freshwater ducks per year | | +|----|---------------------------------------|------------------| +| | Year | Freshwater Ducks | +| | 2019 | 120 | +| | 2020 | 135 | +| | 2021 | 150 | +| | 2022 | 170 | +| | 2023 | 160 | +| | 2024 | 180 | diff --git a/crates/fleischwolf/tests/data/odf/expected/odf_table_with_title_01.ods.strict.md b/crates/fleischwolf/tests/data/odf/expected/odf_table_with_title_01.ods.strict.md index e143a6e5..cde353ea 100644 --- a/crates/fleischwolf/tests/data/odf/expected/odf_table_with_title_01.ods.strict.md +++ b/crates/fleischwolf/tests/data/odf/expected/odf_table_with_title_01.ods.strict.md @@ -1,9 +1,9 @@ -| | Number of freshwater ducks per year | | -| - | - | - | -| | Year | Freshwater Ducks | -| | 2019 | 120 | -| | 2020 | 135 | -| | 2021 | 150 | -| | 2022 | 170 | -| | 2023 | 160 | -| | 2024 | 180 | +| | Number of freshwater ducks per year | | +|----|---------------------------------------|------------------| +| | Year | Freshwater Ducks | +| | 2019 | 120 | +| | 2020 | 135 | +| | 2021 | 150 | +| | 2022 | 170 | +| | 2023 | 160 | +| | 2024 | 180 | diff --git a/crates/fleischwolf/tests/data/odf/expected/text_document_02.odt.md b/crates/fleischwolf/tests/data/odf/expected/text_document_02.odt.md index ffec2689..3356d746 100644 --- a/crates/fleischwolf/tests/data/odf/expected/text_document_02.odt.md +++ b/crates/fleischwolf/tests/data/odf/expected/text_document_02.odt.md @@ -8,6 +8,6 @@ Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lore Contrary to popular belief, Lorem Ipsum is not simply random text. It has roots in a piece of classical Latin literature from 45 BC, making it over 2000 years old. -| Merged row | Merged row | Cell 1.3 | Cell 1.4 | Cell 1.5 | Cell 1.6 | Cell 1.7 | -| - | - | - | - | - | - | - | -| | | Merged cell 2x2 | Merged cell 2x2 | | Merged column | | +| Merged row | Merged row | Cell 1.3 | Cell 1.4 | Cell 1.5 | Cell 1.6 | Cell 1.7 | +|--------------|--------------|-----------------|-----------------|------------|---------------|------------| +| | | Merged cell 2x2 | Merged cell 2x2 | | Merged column | | diff --git a/crates/fleischwolf/tests/data/odf/expected/text_document_02.odt.strict.md b/crates/fleischwolf/tests/data/odf/expected/text_document_02.odt.strict.md index ffec2689..3356d746 100644 --- a/crates/fleischwolf/tests/data/odf/expected/text_document_02.odt.strict.md +++ b/crates/fleischwolf/tests/data/odf/expected/text_document_02.odt.strict.md @@ -8,6 +8,6 @@ Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lore Contrary to popular belief, Lorem Ipsum is not simply random text. It has roots in a piece of classical Latin literature from 45 BC, making it over 2000 years old. -| Merged row | Merged row | Cell 1.3 | Cell 1.4 | Cell 1.5 | Cell 1.6 | Cell 1.7 | -| - | - | - | - | - | - | - | -| | | Merged cell 2x2 | Merged cell 2x2 | | Merged column | | +| Merged row | Merged row | Cell 1.3 | Cell 1.4 | Cell 1.5 | Cell 1.6 | Cell 1.7 | +|--------------|--------------|-----------------|-----------------|------------|---------------|------------| +| | | Merged cell 2x2 | Merged cell 2x2 | | Merged column | | diff --git a/crates/fleischwolf/tests/data/odf/expected/text_document_03.odt.md b/crates/fleischwolf/tests/data/odf/expected/text_document_03.odt.md index 87a48e6e..ed14d009 100644 --- a/crates/fleischwolf/tests/data/odf/expected/text_document_03.odt.md +++ b/crates/fleischwolf/tests/data/odf/expected/text_document_03.odt.md @@ -1,32 +1,32 @@ ### Table with rich cells -| Column A | Column B | -| - | - | -| This is a list: | This is a formatted list: | +| Column A | Column B | +|---------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------| +| This is a list: | This is a formatted list: | | First Paragraph Second Paragraph Third paragraph before a numbered list | This is simple text with **bold** , ~~strikethrough~~ and *italic* formatting with x 2 and H 2 O | -| This is a paragraph This is another paragraph | | +| This is a paragraph This is another paragraph | | ### Table with nested table Before table -| Column A | Column B | | -| - | - | - | -| Simple cell upper left | Simple cell with **bold** and *italic* text | | -| | Rich cell A nested table | | -| A | B | C | -| *Cell 1* | Cell 2 | Cell 3 | -| A | B | C | -| ~~Cell 1~~ | Cell 2 | Cell 3 | +| Column A | Column B | | +|------------------------|-------------------------------------------------|--------| +| Simple cell upper left | Simple cell with **bold** and *italic* text | | +| | Rich cell A nested table | | +| A | B | C | +| *Cell 1* | Cell 2 | Cell 3 | +| A | B | C | +| ~~Cell 1~~ | Cell 2 | Cell 3 | After table with **bold** , underline , ~~strikethrough~~ , and *italic* formatting ### Table with pictures -| Column A | Column B | -| - | - | -| Only text | | -| Text and picture | | +| Column A | Column B | +|------------------|------------| +| Only text | | +| Text and picture | | ### Lists with same numId in different cells @@ -36,5 +36,5 @@ After table with **bold** , underline , ~~strikethrough~~ , and *italic* fo ### Mixed content - list and regular text in different cells -| Regular text in second cell | -| - | +| Regular text in second cell | +|-------------------------------| diff --git a/crates/fleischwolf/tests/data/odf/expected/text_document_03.odt.strict.md b/crates/fleischwolf/tests/data/odf/expected/text_document_03.odt.strict.md index 25ebd8ea..63bcd231 100644 --- a/crates/fleischwolf/tests/data/odf/expected/text_document_03.odt.strict.md +++ b/crates/fleischwolf/tests/data/odf/expected/text_document_03.odt.strict.md @@ -1,32 +1,32 @@ ### Table with rich cells -| Column A | Column B | -| - | - | -| This is a list: | This is a formatted list: | +| Column A | Column B | +|---------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------| +| This is a list: | This is a formatted list: | | First Paragraph Second Paragraph Third paragraph before a numbered list | This is simple text with **bold** , ~~strikethrough~~ and *italic* formatting with x 2 and H 2 O | -| This is a paragraph This is another paragraph | | +| This is a paragraph This is another paragraph | | ### Table with nested table Before table -| Column A | Column B | | -| - | - | - | -| Simple cell upper left | Simple cell with **bold** and *italic* text | | -| | Rich cell A nested table | | -| A | B | C | -| *Cell 1* | Cell 2 | Cell 3 | -| A | B | C | -| ~~Cell 1~~ | Cell 2 | Cell 3 | +| Column A | Column B | | +|------------------------|-------------------------------------------------|--------| +| Simple cell upper left | Simple cell with **bold** and *italic* text | | +| | Rich cell A nested table | | +| A | B | C | +| *Cell 1* | Cell 2 | Cell 3 | +| A | B | C | +| ~~Cell 1~~ | Cell 2 | Cell 3 | After table with **bold**, underline, ~~strikethrough~~, and *italic* formatting ### Table with pictures -| Column A | Column B | -| - | - | -| Only text | | -| Text and picture | | +| Column A | Column B | +|------------------|------------| +| Only text | | +| Text and picture | | ### Lists with same numId in different cells @@ -36,5 +36,5 @@ After table with **bold**, underline, ~~strikethrough~~, and *italic* forma ### Mixed content - list and regular text in different cells -| Regular text in second cell | -| - | +| Regular text in second cell | +|-------------------------------| diff --git a/crates/fleischwolf/tests/data/pptx/expected/powerpoint_sample.pptx.md b/crates/fleischwolf/tests/data/pptx/expected/powerpoint_sample.pptx.md index d633b7fb..f4b98303 100644 --- a/crates/fleischwolf/tests/data/pptx/expected/powerpoint_sample.pptx.md +++ b/crates/fleischwolf/tests/data/pptx/expected/powerpoint_sample.pptx.md @@ -2,16 +2,16 @@ With footnote -| | Class1 | Class1 | Class1 | Class2 | Class2 | Class2 | -| - | - | - | - | - | - | - | -| | A merged with B | A merged with B | C | A | B | C | -| R1 | True | False | | False | True | True | -| R2 | | | True | False | | | -| R3 | False | | | | False | | -| R3 | | True | | True | | | -| R4 | | | False | | False | | -| R4 | | True | | True | False | False | -| R4 | True | False | True | False | True | False | +| | Class1 | Class1 | Class1 | Class2 | Class2 | Class2 | +|----|-----------------|-----------------|----------|----------|----------|----------| +| | A merged with B | A merged with B | C | A | B | C | +| R1 | True | False | | False | True | True | +| R2 | | | True | False | | | +| R3 | False | | | | False | | +| R3 | | True | | True | | | +| R4 | | | False | | False | | +| R4 | | True | | True | False | False | +| R4 | True | False | True | False | True | False | # Second slide title diff --git a/crates/fleischwolf/tests/data/pptx/expected/powerpoint_sample.pptx.strict.md b/crates/fleischwolf/tests/data/pptx/expected/powerpoint_sample.pptx.strict.md index d633b7fb..f4b98303 100644 --- a/crates/fleischwolf/tests/data/pptx/expected/powerpoint_sample.pptx.strict.md +++ b/crates/fleischwolf/tests/data/pptx/expected/powerpoint_sample.pptx.strict.md @@ -2,16 +2,16 @@ With footnote -| | Class1 | Class1 | Class1 | Class2 | Class2 | Class2 | -| - | - | - | - | - | - | - | -| | A merged with B | A merged with B | C | A | B | C | -| R1 | True | False | | False | True | True | -| R2 | | | True | False | | | -| R3 | False | | | | False | | -| R3 | | True | | True | | | -| R4 | | | False | | False | | -| R4 | | True | | True | False | False | -| R4 | True | False | True | False | True | False | +| | Class1 | Class1 | Class1 | Class2 | Class2 | Class2 | +|----|-----------------|-----------------|----------|----------|----------|----------| +| | A merged with B | A merged with B | C | A | B | C | +| R1 | True | False | | False | True | True | +| R2 | | | True | False | | | +| R3 | False | | | | False | | +| R3 | | True | | True | | | +| R4 | | | False | | False | | +| R4 | | True | | True | False | False | +| R4 | True | False | True | False | True | False | # Second slide title diff --git a/crates/fleischwolf/tests/data/xbrl/expected/grve_10q_htm.xml.md b/crates/fleischwolf/tests/data/xbrl/expected/grve_10q_htm.xml.md index e503603c..76a5ceb3 100644 --- a/crates/fleischwolf/tests/data/xbrl/expected/grve_10q_htm.xml.md +++ b/crates/fleischwolf/tests/data/xbrl/expected/grve_10q_htm.xml.md @@ -90,17 +90,17 @@ to notice of certain corporate actions. All accrued dividends on the Series B we A summary of accrued dividends payable with respect to the Series A and B Preferred shares on the Company's balance sheets are set out below. Dividends accrued for the benefit of the Company's CEO are included in Dividends payable, related party: -| Schedule of dividends payable, related party | | | | | | | | | -| - | - | - | - | - | - | - | - | - | -| | | **December 31, 2025** **$** | **December 31, 2025** **$** | | | **March 31, 2025** **$** | **March 31, 2025** **$** | | -| Dividends payable | | | 399,506 | | | | 290,550 | | -| Dividends payable, related party | | | 201,286 | | | | 146,390 | | - -| Schedule of dividends payable, related party | | | | | | | | | -| - | - | - | - | - | - | - | - | - | -| | | **December 31, 2025** **$** | **December 31, 2025** **$** | | | **March 31, 2025** **$** | **March 31, 2025** **$** | | -| Dividends payable | | | 399,506 | | | | 290,550 | | -| Dividends payable, related party | | | 201,286 | | | | 146,390 | | +| Schedule of dividends payable, related party | | | | | | | | | +|------------------------------------------------|----|------------------------------|------------------------------|----|----|---------------------------|---------------------------|----| +| | | **December 31, 2025** **$** | **December 31, 2025** **$** | | | **March 31, 2025** **$** | **March 31, 2025** **$** | | +| Dividends payable | | | 399,506 | | | | 290,550 | | +| Dividends payable, related party | | | 201,286 | | | | 146,390 | | + +| Schedule of dividends payable, related party | | | | | | | | | +|------------------------------------------------|----|------------------------------|------------------------------|----|----|---------------------------|---------------------------|----| +| | | **December 31, 2025** **$** | **December 31, 2025** **$** | | | **March 31, 2025** **$** | **March 31, 2025** **$** | | +| Dividends payable | | | 399,506 | | | | 290,550 | | +| Dividends payable, related party | | | 201,286 | | | | 146,390 | | **NOTE 7 - COMMON STOCK** diff --git a/crates/fleischwolf/tests/data/xbrl/expected/grve_10q_htm.xml.strict.md b/crates/fleischwolf/tests/data/xbrl/expected/grve_10q_htm.xml.strict.md index e503603c..76a5ceb3 100644 --- a/crates/fleischwolf/tests/data/xbrl/expected/grve_10q_htm.xml.strict.md +++ b/crates/fleischwolf/tests/data/xbrl/expected/grve_10q_htm.xml.strict.md @@ -90,17 +90,17 @@ to notice of certain corporate actions. All accrued dividends on the Series B we A summary of accrued dividends payable with respect to the Series A and B Preferred shares on the Company's balance sheets are set out below. Dividends accrued for the benefit of the Company's CEO are included in Dividends payable, related party: -| Schedule of dividends payable, related party | | | | | | | | | -| - | - | - | - | - | - | - | - | - | -| | | **December 31, 2025** **$** | **December 31, 2025** **$** | | | **March 31, 2025** **$** | **March 31, 2025** **$** | | -| Dividends payable | | | 399,506 | | | | 290,550 | | -| Dividends payable, related party | | | 201,286 | | | | 146,390 | | - -| Schedule of dividends payable, related party | | | | | | | | | -| - | - | - | - | - | - | - | - | - | -| | | **December 31, 2025** **$** | **December 31, 2025** **$** | | | **March 31, 2025** **$** | **March 31, 2025** **$** | | -| Dividends payable | | | 399,506 | | | | 290,550 | | -| Dividends payable, related party | | | 201,286 | | | | 146,390 | | +| Schedule of dividends payable, related party | | | | | | | | | +|------------------------------------------------|----|------------------------------|------------------------------|----|----|---------------------------|---------------------------|----| +| | | **December 31, 2025** **$** | **December 31, 2025** **$** | | | **March 31, 2025** **$** | **March 31, 2025** **$** | | +| Dividends payable | | | 399,506 | | | | 290,550 | | +| Dividends payable, related party | | | 201,286 | | | | 146,390 | | + +| Schedule of dividends payable, related party | | | | | | | | | +|------------------------------------------------|----|------------------------------|------------------------------|----|----|---------------------------|---------------------------|----| +| | | **December 31, 2025** **$** | **December 31, 2025** **$** | | | **March 31, 2025** **$** | **March 31, 2025** **$** | | +| Dividends payable | | | 399,506 | | | | 290,550 | | +| Dividends payable, related party | | | 201,286 | | | | 146,390 | | **NOTE 7 - COMMON STOCK** diff --git a/crates/fleischwolf/tests/data/xbrl/expected/mlac-20251231.xml.md b/crates/fleischwolf/tests/data/xbrl/expected/mlac-20251231.xml.md index 45050e2f..37557e73 100644 --- a/crates/fleischwolf/tests/data/xbrl/expected/mlac-20251231.xml.md +++ b/crates/fleischwolf/tests/data/xbrl/expected/mlac-20251231.xml.md @@ -86,17 +86,17 @@ The Public Shares contain a redemption feature which allows for the redemption o As of December 31, 2025 and 2024, the Class A ordinary shares subject to possible redemption reflected in the balance sheets are reconciled in the following table: -| Gross proceeds | | $ | 230,000,000 | | -| - | - | - | - | - | -| Less: | | | | | -| Proceeds allocated to Public Share Rights | | | (2,070,000 | ) | -| Class A ordinary shares issuance costs | | | (13,210,471 | ) | -| Plus: | | | | | -| Accretion for Class A ordinary shares to redemption amount | | | 16,924,324 | | -| Class A ordinary shares subject to possible redemption, December 31, 2024 | | $ | 231,643,853 | | -| Plus: | | | | | -| Accretion for Class A ordinary shares to redemption amount | | | 9,586,719 | | -| Class A ordinary shares subject to possible redemption, December 31, 2025 | | $ | 241,230,572 | | +| Gross proceeds | | $ | 230,000,000 | | +|---------------------------------------------------------------------------|----|-----|---------------|----| +| Less: | | | | | +| Proceeds allocated to Public Share Rights | | | (2,070,000 | ) | +| Class A ordinary shares issuance costs | | | (13,210,471 | ) | +| Plus: | | | | | +| Accretion for Class A ordinary shares to redemption amount | | | 16,924,324 | | +| Class A ordinary shares subject to possible redemption, December 31, 2024 | | $ | 231,643,853 | | +| Plus: | | | | | +| Accretion for Class A ordinary shares to redemption amount | | | 9,586,719 | | +| Class A ordinary shares subject to possible redemption, December 31, 2025 | | $ | 241,230,572 | | ***Fair Value of Financial Instruments*** @@ -106,10 +106,10 @@ The fair value of the Company's assets and liabilities, which qualify as financi Fair value is defined as the price that would be received for sale of an asset or paid for transfer of a liability in an orderly transaction between market participants at the measurement date. GAAP establishes a three-tier fair value hierarchy, which prioritizes the inputs used in measuring fair value. The hierarchy gives the highest priority to unadjusted quoted prices in active markets for identical assets or liabilities (Level 1 measurements) and the lowest priority to unobservable inputs (Level 3 measurements). These tiers include: -| | ● | Level 1, defined as observable inputs such as quoted prices (unadjusted) for identical instruments in active markets; | -| - | - | - | -| | ● | Level 2, defined as inputs other than quoted prices in active markets that are either directly or indirectly observable such as quoted prices for similar instruments in active markets or quoted prices for identical or similar instruments in markets that are not active; and | -| | ● | Level 3, defined as unobservable inputs in which little or no market data exists, therefore requiring an entity to develop its own assumptions, such as valuations derived from valuation techniques in which one or more significant inputs or significant value drivers are unobservable. | +| | ● | Level 1, defined as observable inputs such as quoted prices (unadjusted) for identical instruments in active markets; | +|----|-----|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| | ● | Level 2, defined as inputs other than quoted prices in active markets that are either directly or indirectly observable such as quoted prices for similar instruments in active markets or quoted prices for identical or similar instruments in markets that are not active; and | +| | ● | Level 3, defined as unobservable inputs in which little or no market data exists, therefore requiring an entity to develop its own assumptions, such as valuations derived from valuation techniques in which one or more significant inputs or significant value drivers are unobservable. | In some circumstances, the inputs used to measure fair value might be categorized within different levels of the fair value hierarchy. In those instances, the fair value measurement is categorized in its entirety in the fair value hierarchy based on the lowest level input that is significant to the fair value measurement. @@ -131,27 +131,27 @@ The calculation of diluted net income per ordinary share does not consider the e The following table presents a reconciliation of the numerator and denominator used to compute basic and diluted net income ordinary share for each class of ordinary shares: -| | | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | -| - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | -| | | 2025 | 2025 | 2025 | 2025 | 2025 | 2025 | | | 2024 | 2024 | 2024 | 2024 | 2024 | 2024 | | -| | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | -| Basic net income per share: | | | | | | | | | | | | | | | | | -| Numerator: | | | | | | | | | | | | | | | | | -| Allocation of net income | | $ | 6,361,272 | | | $ | 1,920,674 | | | $ | 99,286 | | | $ | 343,831 | | -| Denominator: | | | | | | | | | | | | | | | | | -| Weighted-average shares outstanding | | | 23,805,000 | | | | 7,187,500 | | | | 1,785,375 | | | | 6,182,813 | | -| Basic net income per common stock | | $ | 0.27 | | | $ | 0.27 | | | $ | 0.06 | | | $ | 0.06 | | - -| | | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | -| - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | -| | | 2025 | 2025 | 2025 | 2025 | 2025 | 2025 | | | 2024 | 2024 | 2024 | 2024 | 2024 | 2024 | | -| | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | -| Diluted net income per share: | | | | | | | | | | | | | | | | | -| Numerator: | | | | | | | | | | | | | | | | | -| Allocation of net income | | $ | 6,361,272 | | | $ | 1,920,674 | | | $ | 93,012 | | | $ | 350,105 | | -| Denominator: | | | | | | | | | | | | | | | | | -| Weighted-average shares outstanding | | | 23,805,000 | | | | 7,187,500 | | | | 1,785,375 | | | | 6,720,313 | | -| Diluted net income per common stock | | $ | 0.27 | | | $ | 0.27 | | | $ | 0.05 | | | $ | 0.05 | | +| | | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | +|-------------------------------------|----|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|----|----|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----| +| | | 2025 | 2025 | 2025 | 2025 | 2025 | 2025 | | | 2024 | 2024 | 2024 | 2024 | 2024 | 2024 | | +| | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | +| Basic net income per share: | | | | | | | | | | | | | | | | | +| Numerator: | | | | | | | | | | | | | | | | | +| Allocation of net income | | $ | 6,361,272 | | | $ | 1,920,674 | | | $ | 99,286 | | | $ | 343,831 | | +| Denominator: | | | | | | | | | | | | | | | | | +| Weighted-average shares outstanding | | | 23,805,000 | | | | 7,187,500 | | | | 1,785,375 | | | | 6,182,813 | | +| Basic net income per common stock | | $ | 0.27 | | | $ | 0.27 | | | $ | 0.06 | | | $ | 0.06 | | + +| | | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | +|-------------------------------------|----|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|----|----|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----| +| | | 2025 | 2025 | 2025 | 2025 | 2025 | 2025 | | | 2024 | 2024 | 2024 | 2024 | 2024 | 2024 | | +| | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | +| Diluted net income per share: | | | | | | | | | | | | | | | | | +| Numerator: | | | | | | | | | | | | | | | | | +| Allocation of net income | | $ | 6,361,272 | | | $ | 1,920,674 | | | $ | 93,012 | | | $ | 350,105 | | +| Denominator: | | | | | | | | | | | | | | | | | +| Weighted-average shares outstanding | | | 23,805,000 | | | | 7,187,500 | | | | 1,785,375 | | | | 6,720,313 | | +| Diluted net income per common stock | | $ | 0.27 | | | $ | 0.27 | | | $ | 0.05 | | | $ | 0.05 | | Concentration of Credit Risk @@ -185,40 +185,40 @@ The Public Shares contain a redemption feature which allows for the redemption o As of December 31, 2025 and 2024, the Class A ordinary shares subject to possible redemption reflected in the balance sheets are reconciled in the following table: -| Gross proceeds | | $ | 230,000,000 | | -| - | - | - | - | - | -| Less: | | | | | -| Proceeds allocated to Public Share Rights | | | (2,070,000 | ) | -| Class A ordinary shares issuance costs | | | (13,210,471 | ) | -| Plus: | | | | | -| Accretion for Class A ordinary shares to redemption amount | | | 16,924,324 | | -| Class A ordinary shares subject to possible redemption, December 31, 2024 | | $ | 231,643,853 | | -| Plus: | | | | | -| Accretion for Class A ordinary shares to redemption amount | | | 9,586,719 | | -| Class A ordinary shares subject to possible redemption, December 31, 2025 | | $ | 241,230,572 | | +| Gross proceeds | | $ | 230,000,000 | | +|---------------------------------------------------------------------------|----|-----|---------------|----| +| Less: | | | | | +| Proceeds allocated to Public Share Rights | | | (2,070,000 | ) | +| Class A ordinary shares issuance costs | | | (13,210,471 | ) | +| Plus: | | | | | +| Accretion for Class A ordinary shares to redemption amount | | | 16,924,324 | | +| Class A ordinary shares subject to possible redemption, December 31, 2024 | | $ | 231,643,853 | | +| Plus: | | | | | +| Accretion for Class A ordinary shares to redemption amount | | | 9,586,719 | | +| Class A ordinary shares subject to possible redemption, December 31, 2025 | | $ | 241,230,572 | | As of December 31, 2025 and 2024, the Class A ordinary shares subject to possible redemption reflected in the balance sheets are reconciled in the following table: -| Gross proceeds | | $ | 230,000,000 | | -| - | - | - | - | - | -| Less: | | | | | -| Proceeds allocated to Public Share Rights | | | (2,070,000 | ) | -| Class A ordinary shares issuance costs | | | (13,210,471 | ) | -| Plus: | | | | | -| Accretion for Class A ordinary shares to redemption amount | | | 16,924,324 | | -| Class A ordinary shares subject to possible redemption, December 31, 2024 | | $ | 231,643,853 | | -| Plus: | | | | | -| Accretion for Class A ordinary shares to redemption amount | | | 9,586,719 | | -| Class A ordinary shares subject to possible redemption, December 31, 2025 | | $ | 241,230,572 | | +| Gross proceeds | | $ | 230,000,000 | | +|---------------------------------------------------------------------------|----|-----|---------------|----| +| Less: | | | | | +| Proceeds allocated to Public Share Rights | | | (2,070,000 | ) | +| Class A ordinary shares issuance costs | | | (13,210,471 | ) | +| Plus: | | | | | +| Accretion for Class A ordinary shares to redemption amount | | | 16,924,324 | | +| Class A ordinary shares subject to possible redemption, December 31, 2024 | | $ | 231,643,853 | | +| Plus: | | | | | +| Accretion for Class A ordinary shares to redemption amount | | | 9,586,719 | | +| Class A ordinary shares subject to possible redemption, December 31, 2025 | | $ | 241,230,572 | | ***Fair Value Measurements*** Fair value is defined as the price that would be received for sale of an asset or paid for transfer of a liability in an orderly transaction between market participants at the measurement date. GAAP establishes a three-tier fair value hierarchy, which prioritizes the inputs used in measuring fair value. The hierarchy gives the highest priority to unadjusted quoted prices in active markets for identical assets or liabilities (Level 1 measurements) and the lowest priority to unobservable inputs (Level 3 measurements). These tiers include: -| | ● | Level 1, defined as observable inputs such as quoted prices (unadjusted) for identical instruments in active markets; | -| - | - | - | -| | ● | Level 2, defined as inputs other than quoted prices in active markets that are either directly or indirectly observable such as quoted prices for similar instruments in active markets or quoted prices for identical or similar instruments in markets that are not active; and | -| | ● | Level 3, defined as unobservable inputs in which little or no market data exists, therefore requiring an entity to develop its own assumptions, such as valuations derived from valuation techniques in which one or more significant inputs or significant value drivers are unobservable. | +| | ● | Level 1, defined as observable inputs such as quoted prices (unadjusted) for identical instruments in active markets; | +|----|-----|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| | ● | Level 2, defined as inputs other than quoted prices in active markets that are either directly or indirectly observable such as quoted prices for similar instruments in active markets or quoted prices for identical or similar instruments in markets that are not active; and | +| | ● | Level 3, defined as unobservable inputs in which little or no market data exists, therefore requiring an entity to develop its own assumptions, such as valuations derived from valuation techniques in which one or more significant inputs or significant value drivers are unobservable. | In some circumstances, the inputs used to measure fair value might be categorized within different levels of the fair value hierarchy. In those instances, the fair value measurement is categorized in its entirety in the fair value hierarchy based on the lowest level input that is significant to the fair value measurement. @@ -240,51 +240,51 @@ The calculation of diluted net income per ordinary share does not consider the e The following table presents a reconciliation of the numerator and denominator used to compute basic and diluted net income ordinary share for each class of ordinary shares: -| | | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | -| - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | -| | | 2025 | 2025 | 2025 | 2025 | 2025 | 2025 | | | 2024 | 2024 | 2024 | 2024 | 2024 | 2024 | | -| | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | -| Basic net income per share: | | | | | | | | | | | | | | | | | -| Numerator: | | | | | | | | | | | | | | | | | -| Allocation of net income | | $ | 6,361,272 | | | $ | 1,920,674 | | | $ | 99,286 | | | $ | 343,831 | | -| Denominator: | | | | | | | | | | | | | | | | | -| Weighted-average shares outstanding | | | 23,805,000 | | | | 7,187,500 | | | | 1,785,375 | | | | 6,182,813 | | -| Basic net income per common stock | | $ | 0.27 | | | $ | 0.27 | | | $ | 0.06 | | | $ | 0.06 | | - -| | | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | -| - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | -| | | 2025 | 2025 | 2025 | 2025 | 2025 | 2025 | | | 2024 | 2024 | 2024 | 2024 | 2024 | 2024 | | -| | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | -| Diluted net income per share: | | | | | | | | | | | | | | | | | -| Numerator: | | | | | | | | | | | | | | | | | -| Allocation of net income | | $ | 6,361,272 | | | $ | 1,920,674 | | | $ | 93,012 | | | $ | 350,105 | | -| Denominator: | | | | | | | | | | | | | | | | | -| Weighted-average shares outstanding | | | 23,805,000 | | | | 7,187,500 | | | | 1,785,375 | | | | 6,720,313 | | -| Diluted net income per common stock | | $ | 0.27 | | | $ | 0.27 | | | $ | 0.05 | | | $ | 0.05 | | +| | | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | +|-------------------------------------|----|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|----|----|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----| +| | | 2025 | 2025 | 2025 | 2025 | 2025 | 2025 | | | 2024 | 2024 | 2024 | 2024 | 2024 | 2024 | | +| | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | +| Basic net income per share: | | | | | | | | | | | | | | | | | +| Numerator: | | | | | | | | | | | | | | | | | +| Allocation of net income | | $ | 6,361,272 | | | $ | 1,920,674 | | | $ | 99,286 | | | $ | 343,831 | | +| Denominator: | | | | | | | | | | | | | | | | | +| Weighted-average shares outstanding | | | 23,805,000 | | | | 7,187,500 | | | | 1,785,375 | | | | 6,182,813 | | +| Basic net income per common stock | | $ | 0.27 | | | $ | 0.27 | | | $ | 0.06 | | | $ | 0.06 | | + +| | | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | +|-------------------------------------|----|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|----|----|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----| +| | | 2025 | 2025 | 2025 | 2025 | 2025 | 2025 | | | 2024 | 2024 | 2024 | 2024 | 2024 | 2024 | | +| | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | +| Diluted net income per share: | | | | | | | | | | | | | | | | | +| Numerator: | | | | | | | | | | | | | | | | | +| Allocation of net income | | $ | 6,361,272 | | | $ | 1,920,674 | | | $ | 93,012 | | | $ | 350,105 | | +| Denominator: | | | | | | | | | | | | | | | | | +| Weighted-average shares outstanding | | | 23,805,000 | | | | 7,187,500 | | | | 1,785,375 | | | | 6,720,313 | | +| Diluted net income per common stock | | $ | 0.27 | | | $ | 0.27 | | | $ | 0.05 | | | $ | 0.05 | | The following table presents a reconciliation of the numerator and denominator used to compute basic and diluted net income ordinary share for each class of ordinary shares: -| | | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | -| - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | -| | | 2025 | 2025 | 2025 | 2025 | 2025 | 2025 | | | 2024 | 2024 | 2024 | 2024 | 2024 | 2024 | | -| | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | -| Basic net income per share: | | | | | | | | | | | | | | | | | -| Numerator: | | | | | | | | | | | | | | | | | -| Allocation of net income | | $ | 6,361,272 | | | $ | 1,920,674 | | | $ | 99,286 | | | $ | 343,831 | | -| Denominator: | | | | | | | | | | | | | | | | | -| Weighted-average shares outstanding | | | 23,805,000 | | | | 7,187,500 | | | | 1,785,375 | | | | 6,182,813 | | -| Basic net income per common stock | | $ | 0.27 | | | $ | 0.27 | | | $ | 0.06 | | | $ | 0.06 | | - -| | | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | -| - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | -| | | 2025 | 2025 | 2025 | 2025 | 2025 | 2025 | | | 2024 | 2024 | 2024 | 2024 | 2024 | 2024 | | -| | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | -| Diluted net income per share: | | | | | | | | | | | | | | | | | -| Numerator: | | | | | | | | | | | | | | | | | -| Allocation of net income | | $ | 6,361,272 | | | $ | 1,920,674 | | | $ | 93,012 | | | $ | 350,105 | | -| Denominator: | | | | | | | | | | | | | | | | | -| Weighted-average shares outstanding | | | 23,805,000 | | | | 7,187,500 | | | | 1,785,375 | | | | 6,720,313 | | -| Diluted net income per common stock | | $ | 0.27 | | | $ | 0.27 | | | $ | 0.05 | | | $ | 0.05 | | +| | | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | +|-------------------------------------|----|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|----|----|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----| +| | | 2025 | 2025 | 2025 | 2025 | 2025 | 2025 | | | 2024 | 2024 | 2024 | 2024 | 2024 | 2024 | | +| | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | +| Basic net income per share: | | | | | | | | | | | | | | | | | +| Numerator: | | | | | | | | | | | | | | | | | +| Allocation of net income | | $ | 6,361,272 | | | $ | 1,920,674 | | | $ | 99,286 | | | $ | 343,831 | | +| Denominator: | | | | | | | | | | | | | | | | | +| Weighted-average shares outstanding | | | 23,805,000 | | | | 7,187,500 | | | | 1,785,375 | | | | 6,182,813 | | +| Basic net income per common stock | | $ | 0.27 | | | $ | 0.27 | | | $ | 0.06 | | | $ | 0.06 | | + +| | | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | +|-------------------------------------|----|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|----|----|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----| +| | | 2025 | 2025 | 2025 | 2025 | 2025 | 2025 | | | 2024 | 2024 | 2024 | 2024 | 2024 | 2024 | | +| | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | +| Diluted net income per share: | | | | | | | | | | | | | | | | | +| Numerator: | | | | | | | | | | | | | | | | | +| Allocation of net income | | $ | 6,361,272 | | | $ | 1,920,674 | | | $ | 93,012 | | | $ | 350,105 | | +| Denominator: | | | | | | | | | | | | | | | | | +| Weighted-average shares outstanding | | | 23,805,000 | | | | 7,187,500 | | | | 1,785,375 | | | | 6,720,313 | | +| Diluted net income per common stock | | $ | 0.27 | | | $ | 0.27 | | | $ | 0.05 | | | $ | 0.05 | | Share Rights @@ -378,59 +378,59 @@ NOTE 8 - FAIR VALUE MEASUREMENTS At December 31, 2025, assets held in the Trust Account were comprised of $1,121 in cash and $241,229,451 in U.S. Treasury securities. During the year ended December 31, 2025, the Company did not withdraw any interest income from the Trust Account. -| | | | | | | | | | Gross | Gross | | | | | | -| - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | -| | | | | | Amortized | Amortized | | | Holding | Holding | | | | | | -| | | | Held To Maturity | | Cost | Cost | | | Gain | Gain | | | Fair Value | Fair Value | | -| December 31, 2025 | | | U.S. Treasury Securities (Matured on January 8, 2026) | | $ | 241,229,451 | | | $ | 58,588 | | | $ | 241,288,039 | | +| | | | | | | | | | Gross | Gross | | | | | | +|-------------------|----|----|-------------------------------------------------------|----|-----------|-------------|----|----|---------|---------|----|----|------------|-------------|----| +| | | | | | Amortized | Amortized | | | Holding | Holding | | | | | | +| | | | Held To Maturity | | Cost | Cost | | | Gain | Gain | | | Fair Value | Fair Value | | +| December 31, 2025 | | | U.S. Treasury Securities (Matured on January 8, 2026) | | $ | 241,229,451 | | | $ | 58,588 | | | $ | 241,288,039 | | At December 31, 2024, assets held in the Trust Account were comprised of $593 in cash and $231,643,260 in U.S. Treasury securities. During the period from June 14, 2014 (inception) through December 31, 2024, the Company did not withdraw any interest income from the Trust Account. -| | | | | | | | | | Gross | Gross | | | | | | -| - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | -| | | | | | Amortized | Amortized | | | Holding | Holding | | | | | | -| | | | Held To Maturity | | Cost | Cost | | | Loss | Loss | | | Fair Value | Fair Value | | -| December 31, 2024 | | | U.S. Treasury Securities (Matured on June 12, 2025) | | $ | 231,643,260 | | | $ | (128,995 | ) | | $ | 231,514,265 | | +| | | | | | | | | | Gross | Gross | | | | | | +|-------------------|----|----|-----------------------------------------------------|----|-----------|-------------|----|----|---------|----------|----|----|------------|-------------|----| +| | | | | | Amortized | Amortized | | | Holding | Holding | | | | | | +| | | | Held To Maturity | | Cost | Cost | | | Loss | Loss | | | Fair Value | Fair Value | | +| December 31, 2024 | | | U.S. Treasury Securities (Matured on June 12, 2025) | | $ | 231,643,260 | | | $ | (128,995 | ) | | $ | 231,514,265 | | The Public Share Rights have been classified within shareholders' deficit and will not require remeasurement after issuance. The following table presents the quantitative information regarding market assumptions used in the valuation of the Public Share Rights: -| | | **December 16,** **2024** **(Initial Public Offering)** | **December 16,** **2024** **(Initial Public Offering)** | | -| - | - | - | - | - | -| Trade price of Unit | | $ | 10.94 | | -| Risk-free rate | | | 4.17 | % | -| Market adjustment (1) | | | 9.2 | % | -| Fair value per share right | | $ | 0.09 | | +| | | **December 16,** **2024** **(Initial Public Offering)** | **December 16,** **2024** **(Initial Public Offering)** | | +|----------------------------|----|------------------------------------------------------------|------------------------------------------------------------|----| +| Trade price of Unit | | $ | 10.94 | | +| Risk-free rate | | | 4.17 | % | +| Market adjustment (1) | | | 9.2 | % | +| Fair value per share right | | $ | 0.09 | | -| (1) | Market adjustment reflects additional factors not fully captured by low volatility selection, which may include likelihood of Business Combination occurring, market perception of lack of available or suitable targets, or possible post-acquisition decline of stock price prior to beginning of the exercise period. The adjustment is determined by comparing traded warrant prices to simulated model outputs. | -| - | - | +| (1) | Market adjustment reflects additional factors not fully captured by low volatility selection, which may include likelihood of Business Combination occurring, market perception of lack of available or suitable targets, or possible post-acquisition decline of stock price prior to beginning of the exercise period. The adjustment is determined by comparing traded warrant prices to simulated model outputs. | +|-------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| During the year ended December 31, 2025, the Company did not withdraw any interest income from the Trust Account. -| | | | | | | | | | Gross | Gross | | | | | | -| - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | -| | | | | | Amortized | Amortized | | | Holding | Holding | | | | | | -| | | | Held To Maturity | | Cost | Cost | | | Gain | Gain | | | Fair Value | Fair Value | | -| December 31, 2025 | | | U.S. Treasury Securities (Matured on January 8, 2026) | | $ | 241,229,451 | | | $ | 58,588 | | | $ | 241,288,039 | | +| | | | | | | | | | Gross | Gross | | | | | | +|-------------------|----|----|-------------------------------------------------------|----|-----------|-------------|----|----|---------|---------|----|----|------------|-------------|----| +| | | | | | Amortized | Amortized | | | Holding | Holding | | | | | | +| | | | Held To Maturity | | Cost | Cost | | | Gain | Gain | | | Fair Value | Fair Value | | +| December 31, 2025 | | | U.S. Treasury Securities (Matured on January 8, 2026) | | $ | 241,229,451 | | | $ | 58,588 | | | $ | 241,288,039 | | During the period from June 14, 2014 (inception) through December 31, 2024, the Company did not withdraw any interest income from the Trust Account. -| | | | | | | | | | Gross | Gross | | | | | | -| - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | -| | | | | | Amortized | Amortized | | | Holding | Holding | | | | | | -| | | | Held To Maturity | | Cost | Cost | | | Loss | Loss | | | Fair Value | Fair Value | | -| December 31, 2024 | | | U.S. Treasury Securities (Matured on June 12, 2025) | | $ | 231,643,260 | | | $ | (128,995 | ) | | $ | 231,514,265 | | +| | | | | | | | | | Gross | Gross | | | | | | +|-------------------|----|----|-----------------------------------------------------|----|-----------|-------------|----|----|---------|----------|----|----|------------|-------------|----| +| | | | | | Amortized | Amortized | | | Holding | Holding | | | | | | +| | | | Held To Maturity | | Cost | Cost | | | Loss | Loss | | | Fair Value | Fair Value | | +| December 31, 2024 | | | U.S. Treasury Securities (Matured on June 12, 2025) | | $ | 231,643,260 | | | $ | (128,995 | ) | | $ | 231,514,265 | | The following table presents the quantitative information regarding market assumptions used in the valuation of the Public Share Rights: -| | | **December 16,** **2024** **(Initial Public Offering)** | **December 16,** **2024** **(Initial Public Offering)** | | -| - | - | - | - | - | -| Trade price of Unit | | $ | 10.94 | | -| Risk-free rate | | | 4.17 | % | -| Market adjustment (1) | | | 9.2 | % | -| Fair value per share right | | $ | 0.09 | | +| | | **December 16,** **2024** **(Initial Public Offering)** | **December 16,** **2024** **(Initial Public Offering)** | | +|----------------------------|----|------------------------------------------------------------|------------------------------------------------------------|----| +| Trade price of Unit | | $ | 10.94 | | +| Risk-free rate | | | 4.17 | % | +| Market adjustment (1) | | | 9.2 | % | +| Fair value per share right | | $ | 0.09 | | -| (1) | Market adjustment reflects additional factors not fully captured by low volatility selection, which may include likelihood of Business Combination occurring, market perception of lack of available or suitable targets, or possible post-acquisition decline of stock price prior to beginning of the exercise period. The adjustment is determined by comparing traded warrant prices to simulated model outputs. | -| - | - | +| (1) | Market adjustment reflects additional factors not fully captured by low volatility selection, which may include likelihood of Business Combination occurring, market perception of lack of available or suitable targets, or possible post-acquisition decline of stock price prior to beginning of the exercise period. The adjustment is determined by comparing traded warrant prices to simulated model outputs. | +|-------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| NOTE 9 - SEGMENT INFORMATION @@ -440,17 +440,17 @@ The Company's CODM has been identified as the Chief Executive Officer , who revi The CODM assesses performance for the single segment and decides how to allocate resources based on net income that also is reported on the statements of operations as net income. The measure of segment assets is reported on the balance sheets as total assets. When evaluating the Company's performance and making key decisions regarding resource allocation, the CODM reviews several key metrics, which include the following: -| | | December 31, | December 31, | | | December 31, | December 31, | | -| - | - | - | - | - | - | - | - | - | -| | | 2025 | 2025 | | | 2024 | 2024 | | -| Cash | | $ | 452,680 | | | $ | 1,383,392 | | -| Cash and marketable securities held in Trust Account | | $ | 241,230,572 | | | $ | 231,643,853 | | +| | | December 31, | December 31, | | | December 31, | December 31, | | +|------------------------------------------------------|----|----------------|----------------|----|----|----------------|----------------|----| +| | | 2025 | 2025 | | | 2024 | 2024 | | +| Cash | | $ | 452,680 | | | $ | 1,383,392 | | +| Cash and marketable securities held in Trust Account | | $ | 241,230,572 | | | $ | 231,643,853 | | -| | | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | -| - | - | - | - | - | - | - | - | - | -| | | 2025 | 2025 | | | 2024 | 2024 | | -| General and administrative expenses | | $ | 1,304,773 | | | $ | 50,736 | | -| Interest earned on cash and marketable securities held in Trust Account | | $ | 9,586,719 | | | $ | 493,853 | | +| | | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | +|-------------------------------------------------------------------------|----|-----------------------------------|-----------------------------------|----|----|----------------------------------------------------------------------|----------------------------------------------------------------------|----| +| | | 2025 | 2025 | | | 2024 | 2024 | | +| General and administrative expenses | | $ | 1,304,773 | | | $ | 50,736 | | +| Interest earned on cash and marketable securities held in Trust Account | | $ | 9,586,719 | | | $ | 493,853 | | The CODM reviews interest earned on marketable securities held in Trust Account to measure and monitor shareholder value and determine the most effective strategy of investment with the Trust Account funds while maintaining compliance with the trust agreement. General and administrative expenses are reviewed and monitored by the CODM to manage and forecast cash to ensure enough capital is available to complete a business combination within the business combination period. The CODM also reviews general and administrative expenses to manage, maintain and enforce all contractual agreements to ensure costs are aligned with all agreements and budget. @@ -458,17 +458,17 @@ General and administrative expenses, as reported on the statements of operations When evaluating the Company's performance and making key decisions regarding resource allocation, the CODM reviews several key metrics, which include the following: -| | | December 31, | December 31, | | | December 31, | December 31, | | -| - | - | - | - | - | - | - | - | - | -| | | 2025 | 2025 | | | 2024 | 2024 | | -| Cash | | $ | 452,680 | | | $ | 1,383,392 | | -| Cash and marketable securities held in Trust Account | | $ | 241,230,572 | | | $ | 231,643,853 | | - -| | | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | -| - | - | - | - | - | - | - | - | - | -| | | 2025 | 2025 | | | 2024 | 2024 | | -| General and administrative expenses | | $ | 1,304,773 | | | $ | 50,736 | | -| Interest earned on cash and marketable securities held in Trust Account | | $ | 9,586,719 | | | $ | 493,853 | | +| | | December 31, | December 31, | | | December 31, | December 31, | | +|------------------------------------------------------|----|----------------|----------------|----|----|----------------|----------------|----| +| | | 2025 | 2025 | | | 2024 | 2024 | | +| Cash | | $ | 452,680 | | | $ | 1,383,392 | | +| Cash and marketable securities held in Trust Account | | $ | 241,230,572 | | | $ | 231,643,853 | | + +| | | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | +|-------------------------------------------------------------------------|----|-----------------------------------|-----------------------------------|----|----|----------------------------------------------------------------------|----------------------------------------------------------------------|----| +| | | 2025 | 2025 | | | 2024 | 2024 | | +| General and administrative expenses | | $ | 1,304,773 | | | $ | 50,736 | | +| Interest earned on cash and marketable securities held in Trust Account | | $ | 9,586,719 | | | $ | 493,853 | | NOTE 10 - SUBSEQUENT EVENTS diff --git a/crates/fleischwolf/tests/data/xbrl/expected/mlac-20251231.xml.strict.md b/crates/fleischwolf/tests/data/xbrl/expected/mlac-20251231.xml.strict.md index 8b7f65b5..127afdab 100644 --- a/crates/fleischwolf/tests/data/xbrl/expected/mlac-20251231.xml.strict.md +++ b/crates/fleischwolf/tests/data/xbrl/expected/mlac-20251231.xml.strict.md @@ -86,17 +86,17 @@ The Public Shares contain a redemption feature which allows for the redemption o As of December 31, 2025 and 2024, the Class A ordinary shares subject to possible redemption reflected in the balance sheets are reconciled in the following table: -| Gross proceeds | | $ | 230,000,000 | | -| - | - | - | - | - | -| Less: | | | | | -| Proceeds allocated to Public Share Rights | | | (2,070,000 | ) | -| Class A ordinary shares issuance costs | | | (13,210,471 | ) | -| Plus: | | | | | -| Accretion for Class A ordinary shares to redemption amount | | | 16,924,324 | | -| Class A ordinary shares subject to possible redemption, December 31, 2024 | | $ | 231,643,853 | | -| Plus: | | | | | -| Accretion for Class A ordinary shares to redemption amount | | | 9,586,719 | | -| Class A ordinary shares subject to possible redemption, December 31, 2025 | | $ | 241,230,572 | | +| Gross proceeds | | $ | 230,000,000 | | +|---------------------------------------------------------------------------|----|-----|---------------|----| +| Less: | | | | | +| Proceeds allocated to Public Share Rights | | | (2,070,000 | ) | +| Class A ordinary shares issuance costs | | | (13,210,471 | ) | +| Plus: | | | | | +| Accretion for Class A ordinary shares to redemption amount | | | 16,924,324 | | +| Class A ordinary shares subject to possible redemption, December 31, 2024 | | $ | 231,643,853 | | +| Plus: | | | | | +| Accretion for Class A ordinary shares to redemption amount | | | 9,586,719 | | +| Class A ordinary shares subject to possible redemption, December 31, 2025 | | $ | 241,230,572 | | ***Fair Value of Financial Instruments*** @@ -106,10 +106,10 @@ The fair value of the Company's assets and liabilities, which qualify as financi Fair value is defined as the price that would be received for sale of an asset or paid for transfer of a liability in an orderly transaction between market participants at the measurement date. GAAP establishes a three-tier fair value hierarchy, which prioritizes the inputs used in measuring fair value. The hierarchy gives the highest priority to unadjusted quoted prices in active markets for identical assets or liabilities (Level 1 measurements) and the lowest priority to unobservable inputs (Level 3 measurements). These tiers include: -| | ● | Level 1, defined as observable inputs such as quoted prices (unadjusted) for identical instruments in active markets; | -| - | - | - | -| | ● | Level 2, defined as inputs other than quoted prices in active markets that are either directly or indirectly observable such as quoted prices for similar instruments in active markets or quoted prices for identical or similar instruments in markets that are not active; and | -| | ● | Level 3, defined as unobservable inputs in which little or no market data exists, therefore requiring an entity to develop its own assumptions, such as valuations derived from valuation techniques in which one or more significant inputs or significant value drivers are unobservable. | +| | ● | Level 1, defined as observable inputs such as quoted prices (unadjusted) for identical instruments in active markets; | +|----|-----|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| | ● | Level 2, defined as inputs other than quoted prices in active markets that are either directly or indirectly observable such as quoted prices for similar instruments in active markets or quoted prices for identical or similar instruments in markets that are not active; and | +| | ● | Level 3, defined as unobservable inputs in which little or no market data exists, therefore requiring an entity to develop its own assumptions, such as valuations derived from valuation techniques in which one or more significant inputs or significant value drivers are unobservable. | In some circumstances, the inputs used to measure fair value might be categorized within different levels of the fair value hierarchy. In those instances, the fair value measurement is categorized in its entirety in the fair value hierarchy based on the lowest level input that is significant to the fair value measurement. @@ -131,27 +131,27 @@ The calculation of diluted net income per ordinary share does not consider the e The following table presents a reconciliation of the numerator and denominator used to compute basic and diluted net income ordinary share for each class of ordinary shares: -| | | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | -| - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | -| | | 2025 | 2025 | 2025 | 2025 | 2025 | 2025 | | | 2024 | 2024 | 2024 | 2024 | 2024 | 2024 | | -| | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | -| Basic net income per share: | | | | | | | | | | | | | | | | | -| Numerator: | | | | | | | | | | | | | | | | | -| Allocation of net income | | $ | 6,361,272 | | | $ | 1,920,674 | | | $ | 99,286 | | | $ | 343,831 | | -| Denominator: | | | | | | | | | | | | | | | | | -| Weighted-average shares outstanding | | | 23,805,000 | | | | 7,187,500 | | | | 1,785,375 | | | | 6,182,813 | | -| Basic net income per common stock | | $ | 0.27 | | | $ | 0.27 | | | $ | 0.06 | | | $ | 0.06 | | - -| | | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | -| - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | -| | | 2025 | 2025 | 2025 | 2025 | 2025 | 2025 | | | 2024 | 2024 | 2024 | 2024 | 2024 | 2024 | | -| | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | -| Diluted net income per share: | | | | | | | | | | | | | | | | | -| Numerator: | | | | | | | | | | | | | | | | | -| Allocation of net income | | $ | 6,361,272 | | | $ | 1,920,674 | | | $ | 93,012 | | | $ | 350,105 | | -| Denominator: | | | | | | | | | | | | | | | | | -| Weighted-average shares outstanding | | | 23,805,000 | | | | 7,187,500 | | | | 1,785,375 | | | | 6,720,313 | | -| Diluted net income per common stock | | $ | 0.27 | | | $ | 0.27 | | | $ | 0.05 | | | $ | 0.05 | | +| | | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | +|-------------------------------------|----|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|----|----|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----| +| | | 2025 | 2025 | 2025 | 2025 | 2025 | 2025 | | | 2024 | 2024 | 2024 | 2024 | 2024 | 2024 | | +| | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | +| Basic net income per share: | | | | | | | | | | | | | | | | | +| Numerator: | | | | | | | | | | | | | | | | | +| Allocation of net income | | $ | 6,361,272 | | | $ | 1,920,674 | | | $ | 99,286 | | | $ | 343,831 | | +| Denominator: | | | | | | | | | | | | | | | | | +| Weighted-average shares outstanding | | | 23,805,000 | | | | 7,187,500 | | | | 1,785,375 | | | | 6,182,813 | | +| Basic net income per common stock | | $ | 0.27 | | | $ | 0.27 | | | $ | 0.06 | | | $ | 0.06 | | + +| | | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | +|-------------------------------------|----|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|----|----|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----| +| | | 2025 | 2025 | 2025 | 2025 | 2025 | 2025 | | | 2024 | 2024 | 2024 | 2024 | 2024 | 2024 | | +| | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | +| Diluted net income per share: | | | | | | | | | | | | | | | | | +| Numerator: | | | | | | | | | | | | | | | | | +| Allocation of net income | | $ | 6,361,272 | | | $ | 1,920,674 | | | $ | 93,012 | | | $ | 350,105 | | +| Denominator: | | | | | | | | | | | | | | | | | +| Weighted-average shares outstanding | | | 23,805,000 | | | | 7,187,500 | | | | 1,785,375 | | | | 6,720,313 | | +| Diluted net income per common stock | | $ | 0.27 | | | $ | 0.27 | | | $ | 0.05 | | | $ | 0.05 | | Concentration of Credit Risk @@ -185,40 +185,40 @@ The Public Shares contain a redemption feature which allows for the redemption o As of December 31, 2025 and 2024, the Class A ordinary shares subject to possible redemption reflected in the balance sheets are reconciled in the following table: -| Gross proceeds | | $ | 230,000,000 | | -| - | - | - | - | - | -| Less: | | | | | -| Proceeds allocated to Public Share Rights | | | (2,070,000 | ) | -| Class A ordinary shares issuance costs | | | (13,210,471 | ) | -| Plus: | | | | | -| Accretion for Class A ordinary shares to redemption amount | | | 16,924,324 | | -| Class A ordinary shares subject to possible redemption, December 31, 2024 | | $ | 231,643,853 | | -| Plus: | | | | | -| Accretion for Class A ordinary shares to redemption amount | | | 9,586,719 | | -| Class A ordinary shares subject to possible redemption, December 31, 2025 | | $ | 241,230,572 | | +| Gross proceeds | | $ | 230,000,000 | | +|---------------------------------------------------------------------------|----|-----|---------------|----| +| Less: | | | | | +| Proceeds allocated to Public Share Rights | | | (2,070,000 | ) | +| Class A ordinary shares issuance costs | | | (13,210,471 | ) | +| Plus: | | | | | +| Accretion for Class A ordinary shares to redemption amount | | | 16,924,324 | | +| Class A ordinary shares subject to possible redemption, December 31, 2024 | | $ | 231,643,853 | | +| Plus: | | | | | +| Accretion for Class A ordinary shares to redemption amount | | | 9,586,719 | | +| Class A ordinary shares subject to possible redemption, December 31, 2025 | | $ | 241,230,572 | | As of December 31, 2025 and 2024, the Class A ordinary shares subject to possible redemption reflected in the balance sheets are reconciled in the following table: -| Gross proceeds | | $ | 230,000,000 | | -| - | - | - | - | - | -| Less: | | | | | -| Proceeds allocated to Public Share Rights | | | (2,070,000 | ) | -| Class A ordinary shares issuance costs | | | (13,210,471 | ) | -| Plus: | | | | | -| Accretion for Class A ordinary shares to redemption amount | | | 16,924,324 | | -| Class A ordinary shares subject to possible redemption, December 31, 2024 | | $ | 231,643,853 | | -| Plus: | | | | | -| Accretion for Class A ordinary shares to redemption amount | | | 9,586,719 | | -| Class A ordinary shares subject to possible redemption, December 31, 2025 | | $ | 241,230,572 | | +| Gross proceeds | | $ | 230,000,000 | | +|---------------------------------------------------------------------------|----|-----|---------------|----| +| Less: | | | | | +| Proceeds allocated to Public Share Rights | | | (2,070,000 | ) | +| Class A ordinary shares issuance costs | | | (13,210,471 | ) | +| Plus: | | | | | +| Accretion for Class A ordinary shares to redemption amount | | | 16,924,324 | | +| Class A ordinary shares subject to possible redemption, December 31, 2024 | | $ | 231,643,853 | | +| Plus: | | | | | +| Accretion for Class A ordinary shares to redemption amount | | | 9,586,719 | | +| Class A ordinary shares subject to possible redemption, December 31, 2025 | | $ | 241,230,572 | | ***Fair Value Measurements*** Fair value is defined as the price that would be received for sale of an asset or paid for transfer of a liability in an orderly transaction between market participants at the measurement date. GAAP establishes a three-tier fair value hierarchy, which prioritizes the inputs used in measuring fair value. The hierarchy gives the highest priority to unadjusted quoted prices in active markets for identical assets or liabilities (Level 1 measurements) and the lowest priority to unobservable inputs (Level 3 measurements). These tiers include: -| | ● | Level 1, defined as observable inputs such as quoted prices (unadjusted) for identical instruments in active markets; | -| - | - | - | -| | ● | Level 2, defined as inputs other than quoted prices in active markets that are either directly or indirectly observable such as quoted prices for similar instruments in active markets or quoted prices for identical or similar instruments in markets that are not active; and | -| | ● | Level 3, defined as unobservable inputs in which little or no market data exists, therefore requiring an entity to develop its own assumptions, such as valuations derived from valuation techniques in which one or more significant inputs or significant value drivers are unobservable. | +| | ● | Level 1, defined as observable inputs such as quoted prices (unadjusted) for identical instruments in active markets; | +|----|-----|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| | ● | Level 2, defined as inputs other than quoted prices in active markets that are either directly or indirectly observable such as quoted prices for similar instruments in active markets or quoted prices for identical or similar instruments in markets that are not active; and | +| | ● | Level 3, defined as unobservable inputs in which little or no market data exists, therefore requiring an entity to develop its own assumptions, such as valuations derived from valuation techniques in which one or more significant inputs or significant value drivers are unobservable. | In some circumstances, the inputs used to measure fair value might be categorized within different levels of the fair value hierarchy. In those instances, the fair value measurement is categorized in its entirety in the fair value hierarchy based on the lowest level input that is significant to the fair value measurement. @@ -240,51 +240,51 @@ The calculation of diluted net income per ordinary share does not consider the e The following table presents a reconciliation of the numerator and denominator used to compute basic and diluted net income ordinary share for each class of ordinary shares: -| | | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | -| - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | -| | | 2025 | 2025 | 2025 | 2025 | 2025 | 2025 | | | 2024 | 2024 | 2024 | 2024 | 2024 | 2024 | | -| | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | -| Basic net income per share: | | | | | | | | | | | | | | | | | -| Numerator: | | | | | | | | | | | | | | | | | -| Allocation of net income | | $ | 6,361,272 | | | $ | 1,920,674 | | | $ | 99,286 | | | $ | 343,831 | | -| Denominator: | | | | | | | | | | | | | | | | | -| Weighted-average shares outstanding | | | 23,805,000 | | | | 7,187,500 | | | | 1,785,375 | | | | 6,182,813 | | -| Basic net income per common stock | | $ | 0.27 | | | $ | 0.27 | | | $ | 0.06 | | | $ | 0.06 | | - -| | | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | -| - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | -| | | 2025 | 2025 | 2025 | 2025 | 2025 | 2025 | | | 2024 | 2024 | 2024 | 2024 | 2024 | 2024 | | -| | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | -| Diluted net income per share: | | | | | | | | | | | | | | | | | -| Numerator: | | | | | | | | | | | | | | | | | -| Allocation of net income | | $ | 6,361,272 | | | $ | 1,920,674 | | | $ | 93,012 | | | $ | 350,105 | | -| Denominator: | | | | | | | | | | | | | | | | | -| Weighted-average shares outstanding | | | 23,805,000 | | | | 7,187,500 | | | | 1,785,375 | | | | 6,720,313 | | -| Diluted net income per common stock | | $ | 0.27 | | | $ | 0.27 | | | $ | 0.05 | | | $ | 0.05 | | +| | | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | +|-------------------------------------|----|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|----|----|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----| +| | | 2025 | 2025 | 2025 | 2025 | 2025 | 2025 | | | 2024 | 2024 | 2024 | 2024 | 2024 | 2024 | | +| | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | +| Basic net income per share: | | | | | | | | | | | | | | | | | +| Numerator: | | | | | | | | | | | | | | | | | +| Allocation of net income | | $ | 6,361,272 | | | $ | 1,920,674 | | | $ | 99,286 | | | $ | 343,831 | | +| Denominator: | | | | | | | | | | | | | | | | | +| Weighted-average shares outstanding | | | 23,805,000 | | | | 7,187,500 | | | | 1,785,375 | | | | 6,182,813 | | +| Basic net income per common stock | | $ | 0.27 | | | $ | 0.27 | | | $ | 0.06 | | | $ | 0.06 | | + +| | | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | +|-------------------------------------|----|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|----|----|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----| +| | | 2025 | 2025 | 2025 | 2025 | 2025 | 2025 | | | 2024 | 2024 | 2024 | 2024 | 2024 | 2024 | | +| | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | +| Diluted net income per share: | | | | | | | | | | | | | | | | | +| Numerator: | | | | | | | | | | | | | | | | | +| Allocation of net income | | $ | 6,361,272 | | | $ | 1,920,674 | | | $ | 93,012 | | | $ | 350,105 | | +| Denominator: | | | | | | | | | | | | | | | | | +| Weighted-average shares outstanding | | | 23,805,000 | | | | 7,187,500 | | | | 1,785,375 | | | | 6,720,313 | | +| Diluted net income per common stock | | $ | 0.27 | | | $ | 0.27 | | | $ | 0.05 | | | $ | 0.05 | | The following table presents a reconciliation of the numerator and denominator used to compute basic and diluted net income ordinary share for each class of ordinary shares: -| | | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | -| - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | -| | | 2025 | 2025 | 2025 | 2025 | 2025 | 2025 | | | 2024 | 2024 | 2024 | 2024 | 2024 | 2024 | | -| | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | -| Basic net income per share: | | | | | | | | | | | | | | | | | -| Numerator: | | | | | | | | | | | | | | | | | -| Allocation of net income | | $ | 6,361,272 | | | $ | 1,920,674 | | | $ | 99,286 | | | $ | 343,831 | | -| Denominator: | | | | | | | | | | | | | | | | | -| Weighted-average shares outstanding | | | 23,805,000 | | | | 7,187,500 | | | | 1,785,375 | | | | 6,182,813 | | -| Basic net income per common stock | | $ | 0.27 | | | $ | 0.27 | | | $ | 0.06 | | | $ | 0.06 | | - -| | | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | -| - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | -| | | 2025 | 2025 | 2025 | 2025 | 2025 | 2025 | | | 2024 | 2024 | 2024 | 2024 | 2024 | 2024 | | -| | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | -| Diluted net income per share: | | | | | | | | | | | | | | | | | -| Numerator: | | | | | | | | | | | | | | | | | -| Allocation of net income | | $ | 6,361,272 | | | $ | 1,920,674 | | | $ | 93,012 | | | $ | 350,105 | | -| Denominator: | | | | | | | | | | | | | | | | | -| Weighted-average shares outstanding | | | 23,805,000 | | | | 7,187,500 | | | | 1,785,375 | | | | 6,720,313 | | -| Diluted net income per common stock | | $ | 0.27 | | | $ | 0.27 | | | $ | 0.05 | | | $ | 0.05 | | +| | | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | +|-------------------------------------|----|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|----|----|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----| +| | | 2025 | 2025 | 2025 | 2025 | 2025 | 2025 | | | 2024 | 2024 | 2024 | 2024 | 2024 | 2024 | | +| | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | +| Basic net income per share: | | | | | | | | | | | | | | | | | +| Numerator: | | | | | | | | | | | | | | | | | +| Allocation of net income | | $ | 6,361,272 | | | $ | 1,920,674 | | | $ | 99,286 | | | $ | 343,831 | | +| Denominator: | | | | | | | | | | | | | | | | | +| Weighted-average shares outstanding | | | 23,805,000 | | | | 7,187,500 | | | | 1,785,375 | | | | 6,182,813 | | +| Basic net income per common stock | | $ | 0.27 | | | $ | 0.27 | | | $ | 0.06 | | | $ | 0.06 | | + +| | | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | +|-------------------------------------|----|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|----|----|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----------------------------------------------------------------------|----| +| | | 2025 | 2025 | 2025 | 2025 | 2025 | 2025 | | | 2024 | 2024 | 2024 | 2024 | 2024 | 2024 | | +| | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | | Class A Ordinary Shares | Class A Ordinary Shares | | | Class B Ordinary Shares | Class B Ordinary Shares | | +| Diluted net income per share: | | | | | | | | | | | | | | | | | +| Numerator: | | | | | | | | | | | | | | | | | +| Allocation of net income | | $ | 6,361,272 | | | $ | 1,920,674 | | | $ | 93,012 | | | $ | 350,105 | | +| Denominator: | | | | | | | | | | | | | | | | | +| Weighted-average shares outstanding | | | 23,805,000 | | | | 7,187,500 | | | | 1,785,375 | | | | 6,720,313 | | +| Diluted net income per common stock | | $ | 0.27 | | | $ | 0.27 | | | $ | 0.05 | | | $ | 0.05 | | Share Rights @@ -378,59 +378,59 @@ NOTE 8 - FAIR VALUE MEASUREMENTS At December 31, 2025, assets held in the Trust Account were comprised of $1,121 in cash and $241,229,451 in U.S. Treasury securities. During the year ended December 31, 2025, the Company did not withdraw any interest income from the Trust Account. -| | | | | | | | | | Gross | Gross | | | | | | -| - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | -| | | | | | Amortized | Amortized | | | Holding | Holding | | | | | | -| | | | Held To Maturity | | Cost | Cost | | | Gain | Gain | | | Fair Value | Fair Value | | -| December 31, 2025 | | | U.S. Treasury Securities (Matured on January 8, 2026) | | $ | 241,229,451 | | | $ | 58,588 | | | $ | 241,288,039 | | +| | | | | | | | | | Gross | Gross | | | | | | +|-------------------|----|----|-------------------------------------------------------|----|-----------|-------------|----|----|---------|---------|----|----|------------|-------------|----| +| | | | | | Amortized | Amortized | | | Holding | Holding | | | | | | +| | | | Held To Maturity | | Cost | Cost | | | Gain | Gain | | | Fair Value | Fair Value | | +| December 31, 2025 | | | U.S. Treasury Securities (Matured on January 8, 2026) | | $ | 241,229,451 | | | $ | 58,588 | | | $ | 241,288,039 | | At December 31, 2024, assets held in the Trust Account were comprised of $593 in cash and $231,643,260 in U.S. Treasury securities. During the period from June 14, 2014 (inception) through December 31, 2024, the Company did not withdraw any interest income from the Trust Account. -| | | | | | | | | | Gross | Gross | | | | | | -| - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | -| | | | | | Amortized | Amortized | | | Holding | Holding | | | | | | -| | | | Held To Maturity | | Cost | Cost | | | Loss | Loss | | | Fair Value | Fair Value | | -| December 31, 2024 | | | U.S. Treasury Securities (Matured on June 12, 2025) | | $ | 231,643,260 | | | $ | (128,995 | ) | | $ | 231,514,265 | | +| | | | | | | | | | Gross | Gross | | | | | | +|-------------------|----|----|-----------------------------------------------------|----|-----------|-------------|----|----|---------|----------|----|----|------------|-------------|----| +| | | | | | Amortized | Amortized | | | Holding | Holding | | | | | | +| | | | Held To Maturity | | Cost | Cost | | | Loss | Loss | | | Fair Value | Fair Value | | +| December 31, 2024 | | | U.S. Treasury Securities (Matured on June 12, 2025) | | $ | 231,643,260 | | | $ | (128,995 | ) | | $ | 231,514,265 | | The Public Share Rights have been classified within shareholders' deficit and will not require remeasurement after issuance. The following table presents the quantitative information regarding market assumptions used in the valuation of the Public Share Rights: -| | | **December 16,** **2024** **(Initial Public Offering)** | **December 16,** **2024** **(Initial Public Offering)** | | -| - | - | - | - | - | -| Trade price of Unit | | $ | 10.94 | | -| Risk-free rate | | | 4.17 | % | -| Market adjustment (1) | | | 9.2 | % | -| Fair value per share right | | $ | 0.09 | | +| | | **December 16,** **2024** **(Initial Public Offering)** | **December 16,** **2024** **(Initial Public Offering)** | | +|----------------------------|----|------------------------------------------------------------|------------------------------------------------------------|----| +| Trade price of Unit | | $ | 10.94 | | +| Risk-free rate | | | 4.17 | % | +| Market adjustment (1) | | | 9.2 | % | +| Fair value per share right | | $ | 0.09 | | -| (1) | Market adjustment reflects additional factors not fully captured by low volatility selection, which may include likelihood of Business Combination occurring, market perception of lack of available or suitable targets, or possible post-acquisition decline of stock price prior to beginning of the exercise period. The adjustment is determined by comparing traded warrant prices to simulated model outputs. | -| - | - | +| (1) | Market adjustment reflects additional factors not fully captured by low volatility selection, which may include likelihood of Business Combination occurring, market perception of lack of available or suitable targets, or possible post-acquisition decline of stock price prior to beginning of the exercise period. The adjustment is determined by comparing traded warrant prices to simulated model outputs. | +|-------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| During the year ended December 31, 2025, the Company did not withdraw any interest income from the Trust Account. -| | | | | | | | | | Gross | Gross | | | | | | -| - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | -| | | | | | Amortized | Amortized | | | Holding | Holding | | | | | | -| | | | Held To Maturity | | Cost | Cost | | | Gain | Gain | | | Fair Value | Fair Value | | -| December 31, 2025 | | | U.S. Treasury Securities (Matured on January 8, 2026) | | $ | 241,229,451 | | | $ | 58,588 | | | $ | 241,288,039 | | +| | | | | | | | | | Gross | Gross | | | | | | +|-------------------|----|----|-------------------------------------------------------|----|-----------|-------------|----|----|---------|---------|----|----|------------|-------------|----| +| | | | | | Amortized | Amortized | | | Holding | Holding | | | | | | +| | | | Held To Maturity | | Cost | Cost | | | Gain | Gain | | | Fair Value | Fair Value | | +| December 31, 2025 | | | U.S. Treasury Securities (Matured on January 8, 2026) | | $ | 241,229,451 | | | $ | 58,588 | | | $ | 241,288,039 | | During the period from June 14, 2014 (inception) through December 31, 2024, the Company did not withdraw any interest income from the Trust Account. -| | | | | | | | | | Gross | Gross | | | | | | -| - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | -| | | | | | Amortized | Amortized | | | Holding | Holding | | | | | | -| | | | Held To Maturity | | Cost | Cost | | | Loss | Loss | | | Fair Value | Fair Value | | -| December 31, 2024 | | | U.S. Treasury Securities (Matured on June 12, 2025) | | $ | 231,643,260 | | | $ | (128,995 | ) | | $ | 231,514,265 | | +| | | | | | | | | | Gross | Gross | | | | | | +|-------------------|----|----|-----------------------------------------------------|----|-----------|-------------|----|----|---------|----------|----|----|------------|-------------|----| +| | | | | | Amortized | Amortized | | | Holding | Holding | | | | | | +| | | | Held To Maturity | | Cost | Cost | | | Loss | Loss | | | Fair Value | Fair Value | | +| December 31, 2024 | | | U.S. Treasury Securities (Matured on June 12, 2025) | | $ | 231,643,260 | | | $ | (128,995 | ) | | $ | 231,514,265 | | The following table presents the quantitative information regarding market assumptions used in the valuation of the Public Share Rights: -| | | **December 16,** **2024** **(Initial Public Offering)** | **December 16,** **2024** **(Initial Public Offering)** | | -| - | - | - | - | - | -| Trade price of Unit | | $ | 10.94 | | -| Risk-free rate | | | 4.17 | % | -| Market adjustment (1) | | | 9.2 | % | -| Fair value per share right | | $ | 0.09 | | +| | | **December 16,** **2024** **(Initial Public Offering)** | **December 16,** **2024** **(Initial Public Offering)** | | +|----------------------------|----|------------------------------------------------------------|------------------------------------------------------------|----| +| Trade price of Unit | | $ | 10.94 | | +| Risk-free rate | | | 4.17 | % | +| Market adjustment (1) | | | 9.2 | % | +| Fair value per share right | | $ | 0.09 | | -| (1) | Market adjustment reflects additional factors not fully captured by low volatility selection, which may include likelihood of Business Combination occurring, market perception of lack of available or suitable targets, or possible post-acquisition decline of stock price prior to beginning of the exercise period. The adjustment is determined by comparing traded warrant prices to simulated model outputs. | -| - | - | +| (1) | Market adjustment reflects additional factors not fully captured by low volatility selection, which may include likelihood of Business Combination occurring, market perception of lack of available or suitable targets, or possible post-acquisition decline of stock price prior to beginning of the exercise period. The adjustment is determined by comparing traded warrant prices to simulated model outputs. | +|-------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| NOTE 9 - SEGMENT INFORMATION @@ -440,17 +440,17 @@ The Company's CODM has been identified as the Chief Executive Officer, who revie The CODM assesses performance for the single segment and decides how to allocate resources based on net income that also is reported on the statements of operations as net income. The measure of segment assets is reported on the balance sheets as total assets. When evaluating the Company's performance and making key decisions regarding resource allocation, the CODM reviews several key metrics, which include the following: -| | | December 31, | December 31, | | | December 31, | December 31, | | -| - | - | - | - | - | - | - | - | - | -| | | 2025 | 2025 | | | 2024 | 2024 | | -| Cash | | $ | 452,680 | | | $ | 1,383,392 | | -| Cash and marketable securities held in Trust Account | | $ | 241,230,572 | | | $ | 231,643,853 | | +| | | December 31, | December 31, | | | December 31, | December 31, | | +|------------------------------------------------------|----|----------------|----------------|----|----|----------------|----------------|----| +| | | 2025 | 2025 | | | 2024 | 2024 | | +| Cash | | $ | 452,680 | | | $ | 1,383,392 | | +| Cash and marketable securities held in Trust Account | | $ | 241,230,572 | | | $ | 231,643,853 | | -| | | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | -| - | - | - | - | - | - | - | - | - | -| | | 2025 | 2025 | | | 2024 | 2024 | | -| General and administrative expenses | | $ | 1,304,773 | | | $ | 50,736 | | -| Interest earned on cash and marketable securities held in Trust Account | | $ | 9,586,719 | | | $ | 493,853 | | +| | | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | +|-------------------------------------------------------------------------|----|-----------------------------------|-----------------------------------|----|----|----------------------------------------------------------------------|----------------------------------------------------------------------|----| +| | | 2025 | 2025 | | | 2024 | 2024 | | +| General and administrative expenses | | $ | 1,304,773 | | | $ | 50,736 | | +| Interest earned on cash and marketable securities held in Trust Account | | $ | 9,586,719 | | | $ | 493,853 | | The CODM reviews interest earned on marketable securities held in Trust Account to measure and monitor shareholder value and determine the most effective strategy of investment with the Trust Account funds while maintaining compliance with the trust agreement. General and administrative expenses are reviewed and monitored by the CODM to manage and forecast cash to ensure enough capital is available to complete a business combination within the business combination period. The CODM also reviews general and administrative expenses to manage, maintain and enforce all contractual agreements to ensure costs are aligned with all agreements and budget. @@ -458,17 +458,17 @@ General and administrative expenses, as reported on the statements of operations When evaluating the Company's performance and making key decisions regarding resource allocation, the CODM reviews several key metrics, which include the following: -| | | December 31, | December 31, | | | December 31, | December 31, | | -| - | - | - | - | - | - | - | - | - | -| | | 2025 | 2025 | | | 2024 | 2024 | | -| Cash | | $ | 452,680 | | | $ | 1,383,392 | | -| Cash and marketable securities held in Trust Account | | $ | 241,230,572 | | | $ | 231,643,853 | | - -| | | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | -| - | - | - | - | - | - | - | - | - | -| | | 2025 | 2025 | | | 2024 | 2024 | | -| General and administrative expenses | | $ | 1,304,773 | | | $ | 50,736 | | -| Interest earned on cash and marketable securities held in Trust Account | | $ | 9,586,719 | | | $ | 493,853 | | +| | | December 31, | December 31, | | | December 31, | December 31, | | +|------------------------------------------------------|----|----------------|----------------|----|----|----------------|----------------|----| +| | | 2025 | 2025 | | | 2024 | 2024 | | +| Cash | | $ | 452,680 | | | $ | 1,383,392 | | +| Cash and marketable securities held in Trust Account | | $ | 241,230,572 | | | $ | 231,643,853 | | + +| | | For the Year Ended December 31, | For the Year Ended December 31, | | | For the Period from June 14, 2024 (inception) through December 31, | For the Period from June 14, 2024 (inception) through December 31, | | +|-------------------------------------------------------------------------|----|-----------------------------------|-----------------------------------|----|----|----------------------------------------------------------------------|----------------------------------------------------------------------|----| +| | | 2025 | 2025 | | | 2024 | 2024 | | +| General and administrative expenses | | $ | 1,304,773 | | | $ | 50,736 | | +| Interest earned on cash and marketable securities held in Trust Account | | $ | 9,586,719 | | | $ | 493,853 | | NOTE 10 - SUBSEQUENT EVENTS diff --git a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_01.xlsx.md b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_01.xlsx.md index 1eb9b914..43430c7a 100644 --- a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_01.xlsx.md +++ b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_01.xlsx.md @@ -1,53 +1,53 @@ -| first | second | third | -| - | - | - | -| 1 | 5 | 9 | -| 2 | 4 | 6 | -| 3 | 3 | 3 | -| 4 | 2 | 0 | -| 5 | 1 | -3 | -| 6 | 0 | -6 | +| first | second | third | +|----------|-----------|---------| +| 1 | 5 | 9 | +| 2 | 4 | 6 | +| 3 | 3 | 3 | +| 4 | 2 | 0 | +| 5 | 1 | -3 | +| 6 | 0 | -6 | -| col-1 | col-2 | col-3 | col-4 | -| - | - | - | - | -| 1 | 2 | 3 | 4 | -| 2 | 4 | 6 | 8 | -| 3 | 6 | 9 | 12 | -| 4 | 8 | 12 | 16 | -| 5 | 10 | 15 | 20 | -| 6 | 12 | 18 | 24 | -| 7 | 14 | 21 | 28 | -| 8 | 16 | 24 | 32 | +| col-1 | col-2 | col-3 | col-4 | +|---------|---------|---------|---------| +| 1 | 2 | 3 | 4 | +| 2 | 4 | 6 | 8 | +| 3 | 6 | 9 | 12 | +| 4 | 8 | 12 | 16 | +| 5 | 10 | 15 | 20 | +| 6 | 12 | 18 | 24 | +| 7 | 14 | 21 | 28 | +| 8 | 16 | 24 | 32 | -| col-1 | col-2 | col-3 | -| - | - | - | -| 1 | 2 | 3 | -| 2 | 4 | 6 | -| 3 | 6 | 9 | -| 4 | 8 | 12 | +| col-1 | col-2 | col-3 | +|---------|---------|---------| +| 1 | 2 | 3 | +| 2 | 4 | 6 | +| 3 | 6 | 9 | +| 4 | 8 | 12 | -| col-1 | col-2 | col-3 | -| - | - | - | -| 1 | 2 | 3 | -| 2 | 4 | 6 | -| 3 | 6 | 9 | -| 4 | 8 | 12 | +| col-1 | col-2 | col-3 | +|---------|---------|---------| +| 1 | 2 | 3 | +| 2 | 4 | 6 | +| 3 | 6 | 9 | +| 4 | 8 | 12 | -| first | header | header | -| - | - | - | -| first | second | third | -| 1 | 2 | 3 | -| 3 | 4 | 5 | -| 3 | 6 | 7 | -| 8 | 9 | 9 | -| 10 | 9 | 9 | +| first | header | header | +|----------|----------|----------| +| first | second | third | +| 1 | 2 | 3 | +| 3 | 4 | 5 | +| 3 | 6 | 7 | +| 8 | 9 | 9 | +| 10 | 9 | 9 | -| first (f) | header (f) | header (f) | -| - | - | - | -| first (f) | second | third | -| 1 | 2 | 3 | -| 3 | 4 | 5 | -| 3 | 6 | 7 | -| 8 | 9 | 9 | -| 10 | 9 | 9 | +| first (f) | header (f) | header (f) | +|-------------|--------------|--------------| +| first (f) | second | third | +| 1 | 2 | 3 | +| 3 | 4 | 5 | +| 3 | 6 | 7 | +| 8 | 9 | 9 | +| 10 | 9 | 9 | diff --git a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_01.xlsx.strict.md b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_01.xlsx.strict.md index 1eb9b914..43430c7a 100644 --- a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_01.xlsx.strict.md +++ b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_01.xlsx.strict.md @@ -1,53 +1,53 @@ -| first | second | third | -| - | - | - | -| 1 | 5 | 9 | -| 2 | 4 | 6 | -| 3 | 3 | 3 | -| 4 | 2 | 0 | -| 5 | 1 | -3 | -| 6 | 0 | -6 | +| first | second | third | +|----------|-----------|---------| +| 1 | 5 | 9 | +| 2 | 4 | 6 | +| 3 | 3 | 3 | +| 4 | 2 | 0 | +| 5 | 1 | -3 | +| 6 | 0 | -6 | -| col-1 | col-2 | col-3 | col-4 | -| - | - | - | - | -| 1 | 2 | 3 | 4 | -| 2 | 4 | 6 | 8 | -| 3 | 6 | 9 | 12 | -| 4 | 8 | 12 | 16 | -| 5 | 10 | 15 | 20 | -| 6 | 12 | 18 | 24 | -| 7 | 14 | 21 | 28 | -| 8 | 16 | 24 | 32 | +| col-1 | col-2 | col-3 | col-4 | +|---------|---------|---------|---------| +| 1 | 2 | 3 | 4 | +| 2 | 4 | 6 | 8 | +| 3 | 6 | 9 | 12 | +| 4 | 8 | 12 | 16 | +| 5 | 10 | 15 | 20 | +| 6 | 12 | 18 | 24 | +| 7 | 14 | 21 | 28 | +| 8 | 16 | 24 | 32 | -| col-1 | col-2 | col-3 | -| - | - | - | -| 1 | 2 | 3 | -| 2 | 4 | 6 | -| 3 | 6 | 9 | -| 4 | 8 | 12 | +| col-1 | col-2 | col-3 | +|---------|---------|---------| +| 1 | 2 | 3 | +| 2 | 4 | 6 | +| 3 | 6 | 9 | +| 4 | 8 | 12 | -| col-1 | col-2 | col-3 | -| - | - | - | -| 1 | 2 | 3 | -| 2 | 4 | 6 | -| 3 | 6 | 9 | -| 4 | 8 | 12 | +| col-1 | col-2 | col-3 | +|---------|---------|---------| +| 1 | 2 | 3 | +| 2 | 4 | 6 | +| 3 | 6 | 9 | +| 4 | 8 | 12 | -| first | header | header | -| - | - | - | -| first | second | third | -| 1 | 2 | 3 | -| 3 | 4 | 5 | -| 3 | 6 | 7 | -| 8 | 9 | 9 | -| 10 | 9 | 9 | +| first | header | header | +|----------|----------|----------| +| first | second | third | +| 1 | 2 | 3 | +| 3 | 4 | 5 | +| 3 | 6 | 7 | +| 8 | 9 | 9 | +| 10 | 9 | 9 | -| first (f) | header (f) | header (f) | -| - | - | - | -| first (f) | second | third | -| 1 | 2 | 3 | -| 3 | 4 | 5 | -| 3 | 6 | 7 | -| 8 | 9 | 9 | -| 10 | 9 | 9 | +| first (f) | header (f) | header (f) | +|-------------|--------------|--------------| +| first (f) | second | third | +| 1 | 2 | 3 | +| 3 | 4 | 5 | +| 3 | 6 | 7 | +| 8 | 9 | 9 | +| 10 | 9 | 9 | diff --git a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_02_sample_sales_data.xlsm.md b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_02_sample_sales_data.xlsm.md index 8ba4b58c..dc1f3c20 100644 --- a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_02_sample_sales_data.xlsm.md +++ b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_02_sample_sales_data.xlsm.md @@ -1,22 +1,22 @@ -| Product | Date | Quantity | Revenue | -| - | - | - | - | -| Widget A | 2024-01-01 00:00:00 | 5 | 5000 | -| Widget B | 2024-01-02 00:00:00 | 10 | 12000 | -| Widget C | 2024-01-03 00:00:00 | 3 | 3000 | -| Widget D | 2024-01-04 00:00:00 | 8 | 8000 | -| Widget A | 2024-01-05 00:00:00 | 7 | 7000 | -| Widget B | 2024-01-06 00:00:00 | 6 | 6000 | -| Widget C | 2024-01-07 00:00:00 | 12 | 15000 | -| Widget D | 2024-01-08 00:00:00 | 9 | 9000 | -| Widget A | 2024-01-09 00:00:00 | 4 | 4000 | -| Widget B | 2024-01-10 00:00:00 | 11 | 11000 | -| Widget C | 2024-01-11 00:00:00 | 5 | 5000 | -| Widget D | 2024-01-12 00:00:00 | 8 | 8500 | -| Widget A | 2024-01-13 00:00:00 | 6 | 6200 | -| Widget B | 2024-01-14 00:00:00 | 7 | 7100 | -| Widget C | 2024-01-15 00:00:00 | 10 | 10500 | -| Widget D | 2024-01-16 00:00:00 | 3 | 3200 | -| Widget A | 2024-01-17 00:00:00 | 9 | 9400 | -| Widget B | 2024-01-18 00:00:00 | 12 | 12500 | -| Widget C | 2024-01-19 00:00:00 | 6 | 6100 | -| Widget D | 2024-01-20 00:00:00 | 8 | 8900 | +| Product | Date | Quantity | Revenue | +|-----------|---------------------|------------|-----------| +| Widget A | 2024-01-01 00:00:00 | 5 | 5000 | +| Widget B | 2024-01-02 00:00:00 | 10 | 12000 | +| Widget C | 2024-01-03 00:00:00 | 3 | 3000 | +| Widget D | 2024-01-04 00:00:00 | 8 | 8000 | +| Widget A | 2024-01-05 00:00:00 | 7 | 7000 | +| Widget B | 2024-01-06 00:00:00 | 6 | 6000 | +| Widget C | 2024-01-07 00:00:00 | 12 | 15000 | +| Widget D | 2024-01-08 00:00:00 | 9 | 9000 | +| Widget A | 2024-01-09 00:00:00 | 4 | 4000 | +| Widget B | 2024-01-10 00:00:00 | 11 | 11000 | +| Widget C | 2024-01-11 00:00:00 | 5 | 5000 | +| Widget D | 2024-01-12 00:00:00 | 8 | 8500 | +| Widget A | 2024-01-13 00:00:00 | 6 | 6200 | +| Widget B | 2024-01-14 00:00:00 | 7 | 7100 | +| Widget C | 2024-01-15 00:00:00 | 10 | 10500 | +| Widget D | 2024-01-16 00:00:00 | 3 | 3200 | +| Widget A | 2024-01-17 00:00:00 | 9 | 9400 | +| Widget B | 2024-01-18 00:00:00 | 12 | 12500 | +| Widget C | 2024-01-19 00:00:00 | 6 | 6100 | +| Widget D | 2024-01-20 00:00:00 | 8 | 8900 | diff --git a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_02_sample_sales_data.xlsm.strict.md b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_02_sample_sales_data.xlsm.strict.md index 8ba4b58c..dc1f3c20 100644 --- a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_02_sample_sales_data.xlsm.strict.md +++ b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_02_sample_sales_data.xlsm.strict.md @@ -1,22 +1,22 @@ -| Product | Date | Quantity | Revenue | -| - | - | - | - | -| Widget A | 2024-01-01 00:00:00 | 5 | 5000 | -| Widget B | 2024-01-02 00:00:00 | 10 | 12000 | -| Widget C | 2024-01-03 00:00:00 | 3 | 3000 | -| Widget D | 2024-01-04 00:00:00 | 8 | 8000 | -| Widget A | 2024-01-05 00:00:00 | 7 | 7000 | -| Widget B | 2024-01-06 00:00:00 | 6 | 6000 | -| Widget C | 2024-01-07 00:00:00 | 12 | 15000 | -| Widget D | 2024-01-08 00:00:00 | 9 | 9000 | -| Widget A | 2024-01-09 00:00:00 | 4 | 4000 | -| Widget B | 2024-01-10 00:00:00 | 11 | 11000 | -| Widget C | 2024-01-11 00:00:00 | 5 | 5000 | -| Widget D | 2024-01-12 00:00:00 | 8 | 8500 | -| Widget A | 2024-01-13 00:00:00 | 6 | 6200 | -| Widget B | 2024-01-14 00:00:00 | 7 | 7100 | -| Widget C | 2024-01-15 00:00:00 | 10 | 10500 | -| Widget D | 2024-01-16 00:00:00 | 3 | 3200 | -| Widget A | 2024-01-17 00:00:00 | 9 | 9400 | -| Widget B | 2024-01-18 00:00:00 | 12 | 12500 | -| Widget C | 2024-01-19 00:00:00 | 6 | 6100 | -| Widget D | 2024-01-20 00:00:00 | 8 | 8900 | +| Product | Date | Quantity | Revenue | +|-----------|---------------------|------------|-----------| +| Widget A | 2024-01-01 00:00:00 | 5 | 5000 | +| Widget B | 2024-01-02 00:00:00 | 10 | 12000 | +| Widget C | 2024-01-03 00:00:00 | 3 | 3000 | +| Widget D | 2024-01-04 00:00:00 | 8 | 8000 | +| Widget A | 2024-01-05 00:00:00 | 7 | 7000 | +| Widget B | 2024-01-06 00:00:00 | 6 | 6000 | +| Widget C | 2024-01-07 00:00:00 | 12 | 15000 | +| Widget D | 2024-01-08 00:00:00 | 9 | 9000 | +| Widget A | 2024-01-09 00:00:00 | 4 | 4000 | +| Widget B | 2024-01-10 00:00:00 | 11 | 11000 | +| Widget C | 2024-01-11 00:00:00 | 5 | 5000 | +| Widget D | 2024-01-12 00:00:00 | 8 | 8500 | +| Widget A | 2024-01-13 00:00:00 | 6 | 6200 | +| Widget B | 2024-01-14 00:00:00 | 7 | 7100 | +| Widget C | 2024-01-15 00:00:00 | 10 | 10500 | +| Widget D | 2024-01-16 00:00:00 | 3 | 3200 | +| Widget A | 2024-01-17 00:00:00 | 9 | 9400 | +| Widget B | 2024-01-18 00:00:00 | 12 | 12500 | +| Widget C | 2024-01-19 00:00:00 | 6 | 6100 | +| Widget D | 2024-01-20 00:00:00 | 8 | 8900 | diff --git a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_03_chartsheet.xlsx.md b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_03_chartsheet.xlsx.md index 32ac188d..00acd12f 100644 --- a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_03_chartsheet.xlsx.md +++ b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_03_chartsheet.xlsx.md @@ -1,8 +1,8 @@ -| Year | Freshwater Ducks | Saltwater Ducks | Ducks | -| - | - | - | - | -| 2019 | 120 | 80 | 200 | -| 2020 | 135 | 95 | 230 | -| 2021 | 150 | 100 | 250 | -| 2022 | 170 | 110 | 280 | -| 2023 | 160 | 120 | 280 | -| 2024 | 180 | 130 | 310 | +| Year | Freshwater Ducks | Saltwater Ducks | Ducks | +|--------|--------------------|-------------------|---------| +| 2019 | 120 | 80 | 200 | +| 2020 | 135 | 95 | 230 | +| 2021 | 150 | 100 | 250 | +| 2022 | 170 | 110 | 280 | +| 2023 | 160 | 120 | 280 | +| 2024 | 180 | 130 | 310 | diff --git a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_03_chartsheet.xlsx.strict.md b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_03_chartsheet.xlsx.strict.md index 32ac188d..00acd12f 100644 --- a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_03_chartsheet.xlsx.strict.md +++ b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_03_chartsheet.xlsx.strict.md @@ -1,8 +1,8 @@ -| Year | Freshwater Ducks | Saltwater Ducks | Ducks | -| - | - | - | - | -| 2019 | 120 | 80 | 200 | -| 2020 | 135 | 95 | 230 | -| 2021 | 150 | 100 | 250 | -| 2022 | 170 | 110 | 280 | -| 2023 | 160 | 120 | 280 | -| 2024 | 180 | 130 | 310 | +| Year | Freshwater Ducks | Saltwater Ducks | Ducks | +|--------|--------------------|-------------------|---------| +| 2019 | 120 | 80 | 200 | +| 2020 | 135 | 95 | 230 | +| 2021 | 150 | 100 | 250 | +| 2022 | 170 | 110 | 280 | +| 2023 | 160 | 120 | 280 | +| 2024 | 180 | 130 | 310 | diff --git a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_04_inflated.xlsx.md b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_04_inflated.xlsx.md index 1eb9b914..43430c7a 100644 --- a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_04_inflated.xlsx.md +++ b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_04_inflated.xlsx.md @@ -1,53 +1,53 @@ -| first | second | third | -| - | - | - | -| 1 | 5 | 9 | -| 2 | 4 | 6 | -| 3 | 3 | 3 | -| 4 | 2 | 0 | -| 5 | 1 | -3 | -| 6 | 0 | -6 | +| first | second | third | +|----------|-----------|---------| +| 1 | 5 | 9 | +| 2 | 4 | 6 | +| 3 | 3 | 3 | +| 4 | 2 | 0 | +| 5 | 1 | -3 | +| 6 | 0 | -6 | -| col-1 | col-2 | col-3 | col-4 | -| - | - | - | - | -| 1 | 2 | 3 | 4 | -| 2 | 4 | 6 | 8 | -| 3 | 6 | 9 | 12 | -| 4 | 8 | 12 | 16 | -| 5 | 10 | 15 | 20 | -| 6 | 12 | 18 | 24 | -| 7 | 14 | 21 | 28 | -| 8 | 16 | 24 | 32 | +| col-1 | col-2 | col-3 | col-4 | +|---------|---------|---------|---------| +| 1 | 2 | 3 | 4 | +| 2 | 4 | 6 | 8 | +| 3 | 6 | 9 | 12 | +| 4 | 8 | 12 | 16 | +| 5 | 10 | 15 | 20 | +| 6 | 12 | 18 | 24 | +| 7 | 14 | 21 | 28 | +| 8 | 16 | 24 | 32 | -| col-1 | col-2 | col-3 | -| - | - | - | -| 1 | 2 | 3 | -| 2 | 4 | 6 | -| 3 | 6 | 9 | -| 4 | 8 | 12 | +| col-1 | col-2 | col-3 | +|---------|---------|---------| +| 1 | 2 | 3 | +| 2 | 4 | 6 | +| 3 | 6 | 9 | +| 4 | 8 | 12 | -| col-1 | col-2 | col-3 | -| - | - | - | -| 1 | 2 | 3 | -| 2 | 4 | 6 | -| 3 | 6 | 9 | -| 4 | 8 | 12 | +| col-1 | col-2 | col-3 | +|---------|---------|---------| +| 1 | 2 | 3 | +| 2 | 4 | 6 | +| 3 | 6 | 9 | +| 4 | 8 | 12 | -| first | header | header | -| - | - | - | -| first | second | third | -| 1 | 2 | 3 | -| 3 | 4 | 5 | -| 3 | 6 | 7 | -| 8 | 9 | 9 | -| 10 | 9 | 9 | +| first | header | header | +|----------|----------|----------| +| first | second | third | +| 1 | 2 | 3 | +| 3 | 4 | 5 | +| 3 | 6 | 7 | +| 8 | 9 | 9 | +| 10 | 9 | 9 | -| first (f) | header (f) | header (f) | -| - | - | - | -| first (f) | second | third | -| 1 | 2 | 3 | -| 3 | 4 | 5 | -| 3 | 6 | 7 | -| 8 | 9 | 9 | -| 10 | 9 | 9 | +| first (f) | header (f) | header (f) | +|-------------|--------------|--------------| +| first (f) | second | third | +| 1 | 2 | 3 | +| 3 | 4 | 5 | +| 3 | 6 | 7 | +| 8 | 9 | 9 | +| 10 | 9 | 9 | diff --git a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_04_inflated.xlsx.strict.md b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_04_inflated.xlsx.strict.md index 1eb9b914..43430c7a 100644 --- a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_04_inflated.xlsx.strict.md +++ b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_04_inflated.xlsx.strict.md @@ -1,53 +1,53 @@ -| first | second | third | -| - | - | - | -| 1 | 5 | 9 | -| 2 | 4 | 6 | -| 3 | 3 | 3 | -| 4 | 2 | 0 | -| 5 | 1 | -3 | -| 6 | 0 | -6 | +| first | second | third | +|----------|-----------|---------| +| 1 | 5 | 9 | +| 2 | 4 | 6 | +| 3 | 3 | 3 | +| 4 | 2 | 0 | +| 5 | 1 | -3 | +| 6 | 0 | -6 | -| col-1 | col-2 | col-3 | col-4 | -| - | - | - | - | -| 1 | 2 | 3 | 4 | -| 2 | 4 | 6 | 8 | -| 3 | 6 | 9 | 12 | -| 4 | 8 | 12 | 16 | -| 5 | 10 | 15 | 20 | -| 6 | 12 | 18 | 24 | -| 7 | 14 | 21 | 28 | -| 8 | 16 | 24 | 32 | +| col-1 | col-2 | col-3 | col-4 | +|---------|---------|---------|---------| +| 1 | 2 | 3 | 4 | +| 2 | 4 | 6 | 8 | +| 3 | 6 | 9 | 12 | +| 4 | 8 | 12 | 16 | +| 5 | 10 | 15 | 20 | +| 6 | 12 | 18 | 24 | +| 7 | 14 | 21 | 28 | +| 8 | 16 | 24 | 32 | -| col-1 | col-2 | col-3 | -| - | - | - | -| 1 | 2 | 3 | -| 2 | 4 | 6 | -| 3 | 6 | 9 | -| 4 | 8 | 12 | +| col-1 | col-2 | col-3 | +|---------|---------|---------| +| 1 | 2 | 3 | +| 2 | 4 | 6 | +| 3 | 6 | 9 | +| 4 | 8 | 12 | -| col-1 | col-2 | col-3 | -| - | - | - | -| 1 | 2 | 3 | -| 2 | 4 | 6 | -| 3 | 6 | 9 | -| 4 | 8 | 12 | +| col-1 | col-2 | col-3 | +|---------|---------|---------| +| 1 | 2 | 3 | +| 2 | 4 | 6 | +| 3 | 6 | 9 | +| 4 | 8 | 12 | -| first | header | header | -| - | - | - | -| first | second | third | -| 1 | 2 | 3 | -| 3 | 4 | 5 | -| 3 | 6 | 7 | -| 8 | 9 | 9 | -| 10 | 9 | 9 | +| first | header | header | +|----------|----------|----------| +| first | second | third | +| 1 | 2 | 3 | +| 3 | 4 | 5 | +| 3 | 6 | 7 | +| 8 | 9 | 9 | +| 10 | 9 | 9 | -| first (f) | header (f) | header (f) | -| - | - | - | -| first (f) | second | third | -| 1 | 2 | 3 | -| 3 | 4 | 5 | -| 3 | 6 | 7 | -| 8 | 9 | 9 | -| 10 | 9 | 9 | +| first (f) | header (f) | header (f) | +|-------------|--------------|--------------| +| first (f) | second | third | +| 1 | 2 | 3 | +| 3 | 4 | 5 | +| 3 | 6 | 7 | +| 8 | 9 | 9 | +| 10 | 9 | 9 | diff --git a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_05_table_with_title.xlsx.md b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_05_table_with_title.xlsx.md index f8f7672e..23391157 100644 --- a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_05_table_with_title.xlsx.md +++ b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_05_table_with_title.xlsx.md @@ -1,11 +1,11 @@ -| Number of freshwater ducks per year | -| - | +| Number of freshwater ducks per year | +|---------------------------------------| -| Year | Freshwater Ducks | -| - | - | -| 2019 | 120 | -| 2020 | 135 | -| 2021 | 150 | -| 2022 | 170 | -| 2023 | 160 | -| 2024 | 180 | +| Year | Freshwater Ducks | +|--------|--------------------| +| 2019 | 120 | +| 2020 | 135 | +| 2021 | 150 | +| 2022 | 170 | +| 2023 | 160 | +| 2024 | 180 | diff --git a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_05_table_with_title.xlsx.strict.md b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_05_table_with_title.xlsx.strict.md index f8f7672e..23391157 100644 --- a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_05_table_with_title.xlsx.strict.md +++ b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_05_table_with_title.xlsx.strict.md @@ -1,11 +1,11 @@ -| Number of freshwater ducks per year | -| - | +| Number of freshwater ducks per year | +|---------------------------------------| -| Year | Freshwater Ducks | -| - | - | -| 2019 | 120 | -| 2020 | 135 | -| 2021 | 150 | -| 2022 | 170 | -| 2023 | 160 | -| 2024 | 180 | +| Year | Freshwater Ducks | +|--------|--------------------| +| 2019 | 120 | +| 2020 | 135 | +| 2021 | 150 | +| 2022 | 170 | +| 2023 | 160 | +| 2024 | 180 | diff --git a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_06_edge_cases_.xlsx.md b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_06_edge_cases_.xlsx.md index 196ed35b..b455d4e2 100644 --- a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_06_edge_cases_.xlsx.md +++ b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_06_edge_cases_.xlsx.md @@ -1,22 +1,22 @@ -| | Sub-category | ID | Question | Answer | Extra | -| - | - | - | - | - | - | -| Overview | Purpose and Use Cases | AI 1 | What is the main objective? | The main objective is... | Additional interviewers insights, or information | -| Overview | Purpose and Use Cases | AI 2 | What are the specific use cases? | The AI is applied for... | More information | +| | Sub-category | ID | Question | Answer | Extra | +|----------|-----------------------|------|-----------------------------------------|----------------------------|----------------------------------------------------| +| Overview | Purpose and Use Cases | AI 1 | What is the main objective? | The main objective is... | Additional interviewers insights, or information | +| Overview | Purpose and Use Cases | AI 2 | What are the specific use cases? | The AI is applied for... | More information | | Overview | Purpose and Use Cases | AI 3 | What types of data will the AI process? | The AI processes code data | With Privacy Mode disabled, no code data is stored | -| | Product/Integration | Sub-category | ID | Question | Answer | Extra | -| - | - | - | - | - | - | - | -| | Overview | Purpose and Use Cases | AI 1 | What is the main objective? | The main objective is... | Additional interviewers insights, or information | -| Attached left | Overview | Purpose and Use Cases | AI 2 | What are the specific use cases? | The AI is applied for... | More information | -| | Overview | Purpose and Use Cases | AI 3 | What types of data will the AI process? | The AI processes code data | With Privacy Mode disabled, no code data is stored | +| | Product/Integration | Sub-category | ID | Question | Answer | Extra | +|---------------|-----------------------|-----------------------|------|-----------------------------------------|----------------------------|----------------------------------------------------| +| | Overview | Purpose and Use Cases | AI 1 | What is the main objective? | The main objective is... | Additional interviewers insights, or information | +| Attached left | Overview | Purpose and Use Cases | AI 2 | What are the specific use cases? | The AI is applied for... | More information | +| | Overview | Purpose and Use Cases | AI 3 | What types of data will the AI process? | The AI processes code data | With Privacy Mode disabled, no code data is stored | -| Product/Integration | Sub-category | ID | Question | Answer | Extra | -| - | - | - | - | - | - | -| Overview | Purpose and Use Cases | AI 1 | What is the main objective? | The main objective is... | Additional interviewers insights, or information | -| Overview | Purpose and Use Cases | AI 2 | What are the specific use cases? | The AI is applied for... | More information | -| Overview | Purpose and Use Cases | AI 3 | What types of data will the AI process? | The AI processes code data | With Privacy Mode disabled, no code data is stored | +| Product/Integration | Sub-category | ID | Question | Answer | Extra | +|-----------------------|-----------------------|------|-----------------------------------------|----------------------------|----------------------------------------------------| +| Overview | Purpose and Use Cases | AI 1 | What is the main objective? | The main objective is... | Additional interviewers insights, or information | +| Overview | Purpose and Use Cases | AI 2 | What are the specific use cases? | The AI is applied for... | More information | +| Overview | Purpose and Use Cases | AI 3 | What types of data will the AI process? | The AI processes code data | With Privacy Mode disabled, no code data is stored | -| G1 | H1 | I3 | -| - | - | - | +| G1 | H1 | I3 | +|------------|-----------|---------| | Overview 1 | Purpose 1 | Extra 1 | | Overview 2 | Purpose 2 | Extra 2 | diff --git a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_06_edge_cases_.xlsx.strict.md b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_06_edge_cases_.xlsx.strict.md index 196ed35b..b455d4e2 100644 --- a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_06_edge_cases_.xlsx.strict.md +++ b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_06_edge_cases_.xlsx.strict.md @@ -1,22 +1,22 @@ -| | Sub-category | ID | Question | Answer | Extra | -| - | - | - | - | - | - | -| Overview | Purpose and Use Cases | AI 1 | What is the main objective? | The main objective is... | Additional interviewers insights, or information | -| Overview | Purpose and Use Cases | AI 2 | What are the specific use cases? | The AI is applied for... | More information | +| | Sub-category | ID | Question | Answer | Extra | +|----------|-----------------------|------|-----------------------------------------|----------------------------|----------------------------------------------------| +| Overview | Purpose and Use Cases | AI 1 | What is the main objective? | The main objective is... | Additional interviewers insights, or information | +| Overview | Purpose and Use Cases | AI 2 | What are the specific use cases? | The AI is applied for... | More information | | Overview | Purpose and Use Cases | AI 3 | What types of data will the AI process? | The AI processes code data | With Privacy Mode disabled, no code data is stored | -| | Product/Integration | Sub-category | ID | Question | Answer | Extra | -| - | - | - | - | - | - | - | -| | Overview | Purpose and Use Cases | AI 1 | What is the main objective? | The main objective is... | Additional interviewers insights, or information | -| Attached left | Overview | Purpose and Use Cases | AI 2 | What are the specific use cases? | The AI is applied for... | More information | -| | Overview | Purpose and Use Cases | AI 3 | What types of data will the AI process? | The AI processes code data | With Privacy Mode disabled, no code data is stored | +| | Product/Integration | Sub-category | ID | Question | Answer | Extra | +|---------------|-----------------------|-----------------------|------|-----------------------------------------|----------------------------|----------------------------------------------------| +| | Overview | Purpose and Use Cases | AI 1 | What is the main objective? | The main objective is... | Additional interviewers insights, or information | +| Attached left | Overview | Purpose and Use Cases | AI 2 | What are the specific use cases? | The AI is applied for... | More information | +| | Overview | Purpose and Use Cases | AI 3 | What types of data will the AI process? | The AI processes code data | With Privacy Mode disabled, no code data is stored | -| Product/Integration | Sub-category | ID | Question | Answer | Extra | -| - | - | - | - | - | - | -| Overview | Purpose and Use Cases | AI 1 | What is the main objective? | The main objective is... | Additional interviewers insights, or information | -| Overview | Purpose and Use Cases | AI 2 | What are the specific use cases? | The AI is applied for... | More information | -| Overview | Purpose and Use Cases | AI 3 | What types of data will the AI process? | The AI processes code data | With Privacy Mode disabled, no code data is stored | +| Product/Integration | Sub-category | ID | Question | Answer | Extra | +|-----------------------|-----------------------|------|-----------------------------------------|----------------------------|----------------------------------------------------| +| Overview | Purpose and Use Cases | AI 1 | What is the main objective? | The main objective is... | Additional interviewers insights, or information | +| Overview | Purpose and Use Cases | AI 2 | What are the specific use cases? | The AI is applied for... | More information | +| Overview | Purpose and Use Cases | AI 3 | What types of data will the AI process? | The AI processes code data | With Privacy Mode disabled, no code data is stored | -| G1 | H1 | I3 | -| - | - | - | +| G1 | H1 | I3 | +|------------|-----------|---------| | Overview 1 | Purpose 1 | Extra 1 | | Overview 2 | Purpose 2 | Extra 2 | diff --git a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_07_gap_tolerance_.xlsx.md b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_07_gap_tolerance_.xlsx.md index 5bc4baff..83152fd6 100644 --- a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_07_gap_tolerance_.xlsx.md +++ b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_07_gap_tolerance_.xlsx.md @@ -1,90 +1,90 @@ -| HIGH VOLTAGE SWITCHBOARD | -| - | -| DATA SHEET | +| HIGH VOLTAGE SWITCHBOARD | +|----------------------------| +| DATA SHEET | -| MODEL-000 | -| - | -| 2016 | +| MODEL-000 | +|--------------| +| 2016 | | Page 1 of 10 | -| E-123 | -| - | +| E-123 | +|---------| -| Package no.: | | | Doc. no.: | | | | Rev. | -| - | - | - | - | - | - | - | - | -| 13156456 | 13156456 | 13156456 | 144564 | 144564 | 144564 | 144564 | A | +| Package no.: | | | Doc. no.: | | | | Rev. | +|----------------|----------|----------|-------------|--------|--------|--------|--------| +| 13156456 | 13156456 | 13156456 | 144564 | 144564 | 144564 | 144564 | A | -| Functional requirements | -| - | +| Functional requirements | +|---------------------------| -| Power system | -| - | -| 1 | -| 2 | -| 3 | -| 4 | -| 5 | -| 6 | -| Construction | -| 7 | -| 8 | -| 9 | +| Power system | +|--------------------------| +| 1 | +| 2 | +| 3 | +| 4 | +| 5 | +| 6 | +| Construction | +| 7 | +| 8 | +| 9 | | Environmental conditions | -| 10 | -| 11 | -| 12 | -| 13 | -| Arc test | -| 14 | -| Notes | -| 1 | -| 2 | +| 10 | +| 11 | +| 12 | +| 13 | +| Arc test | +| 14 | +| Notes | +| 1 | +| 2 | -| Rated system voltage | -| - | +| Rated system voltage | +|------------------------| | Rated system frequency | -| No. of phases | -| System earthing | -| Earth fault current | +| No. of phases | +| System earthing | +| Earth fault current | | Control voltage supply | -| kV | : | 130 (131 Um, 132 AC, 133 BIL) | | -| - | - | - | - | -| Hz | : | 134 | | -| | : | 3 | | -| | : | Solidly Earthed | | -| A | : | 135 kA | | -| | : | 2 x 136V AC UPS 1 x 137V AC normal | Note 1 | +| kV | : | 130 (131 Um, 132 AC, 133 BIL) | | +|------|-----|------------------------------------|--------| +| Hz | : | 134 | | +| | : | 3 | | +| | : | Solidly Earthed | | +| A | : | 135 kA | | +| | : | 2 x 136V AC UPS 1 x 137V AC normal | Note 1 | -| Metal-enclosed partition | -| - | -| VT for cable discharging | +| Metal-enclosed partition | +|---------------------------------| +| VT for cable discharging | | Voltage and Current measurement | -| : | Gas Insulated Switchgear (GIS) | Non Sticky | -| - | - | - | -| | No | | -| | Low Power Instrument Transformers | | +| : | Gas Insulated Switchgear (GIS) | Non Sticky | +|-----|-----------------------------------|--------------| +| | No | | +| | Low Power Instrument Transformers | | -| Hazardous area classification | -| - | -| Ambient temp. | -| Location | -| Humidity | +| Hazardous area classification | +|---------------------------------| +| Ambient temp. | +| Location | +| Humidity | -| | : | Non hazardous | -| - | - | - | -| ºC | : | Min. -5, max. +40 | -| | : | Indoor | -| % | : | 100 | +| | : | Non hazardous | +|----|-----|-------------------| +| ºC | : | Min. -5, max. +40 | +| | : | Indoor | +| % | : | 100 | -| Arc testing | -| - | +| Arc testing | +|---------------| -| Yes/no | : | Yes | Note 2 | -| - | - | - | - | +| Yes/no | : | Yes | Note 2 | +|----------|-----|-------|----------| -| Converted to 110VDC | Converted to 110VDC | Converted to 110VDC | Converted to 110VDC | Converted to 110VDC | -| - | - | - | - | - | -| Arc test (type test) | Arc test (type test) | Arc test (type test) | Arc test (type test) | Arc test (type test) | -| | | | | | +| Converted to 110VDC | Converted to 110VDC | Converted to 110VDC | Converted to 110VDC | Converted to 110VDC | +|------------------------|------------------------|------------------------|------------------------|------------------------| +| Arc test (type test) | Arc test (type test) | Arc test (type test) | Arc test (type test) | Arc test (type test) | +| | | | | | diff --git a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_07_gap_tolerance_.xlsx.strict.md b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_07_gap_tolerance_.xlsx.strict.md index 5bc4baff..83152fd6 100644 --- a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_07_gap_tolerance_.xlsx.strict.md +++ b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_07_gap_tolerance_.xlsx.strict.md @@ -1,90 +1,90 @@ -| HIGH VOLTAGE SWITCHBOARD | -| - | -| DATA SHEET | +| HIGH VOLTAGE SWITCHBOARD | +|----------------------------| +| DATA SHEET | -| MODEL-000 | -| - | -| 2016 | +| MODEL-000 | +|--------------| +| 2016 | | Page 1 of 10 | -| E-123 | -| - | +| E-123 | +|---------| -| Package no.: | | | Doc. no.: | | | | Rev. | -| - | - | - | - | - | - | - | - | -| 13156456 | 13156456 | 13156456 | 144564 | 144564 | 144564 | 144564 | A | +| Package no.: | | | Doc. no.: | | | | Rev. | +|----------------|----------|----------|-------------|--------|--------|--------|--------| +| 13156456 | 13156456 | 13156456 | 144564 | 144564 | 144564 | 144564 | A | -| Functional requirements | -| - | +| Functional requirements | +|---------------------------| -| Power system | -| - | -| 1 | -| 2 | -| 3 | -| 4 | -| 5 | -| 6 | -| Construction | -| 7 | -| 8 | -| 9 | +| Power system | +|--------------------------| +| 1 | +| 2 | +| 3 | +| 4 | +| 5 | +| 6 | +| Construction | +| 7 | +| 8 | +| 9 | | Environmental conditions | -| 10 | -| 11 | -| 12 | -| 13 | -| Arc test | -| 14 | -| Notes | -| 1 | -| 2 | +| 10 | +| 11 | +| 12 | +| 13 | +| Arc test | +| 14 | +| Notes | +| 1 | +| 2 | -| Rated system voltage | -| - | +| Rated system voltage | +|------------------------| | Rated system frequency | -| No. of phases | -| System earthing | -| Earth fault current | +| No. of phases | +| System earthing | +| Earth fault current | | Control voltage supply | -| kV | : | 130 (131 Um, 132 AC, 133 BIL) | | -| - | - | - | - | -| Hz | : | 134 | | -| | : | 3 | | -| | : | Solidly Earthed | | -| A | : | 135 kA | | -| | : | 2 x 136V AC UPS 1 x 137V AC normal | Note 1 | +| kV | : | 130 (131 Um, 132 AC, 133 BIL) | | +|------|-----|------------------------------------|--------| +| Hz | : | 134 | | +| | : | 3 | | +| | : | Solidly Earthed | | +| A | : | 135 kA | | +| | : | 2 x 136V AC UPS 1 x 137V AC normal | Note 1 | -| Metal-enclosed partition | -| - | -| VT for cable discharging | +| Metal-enclosed partition | +|---------------------------------| +| VT for cable discharging | | Voltage and Current measurement | -| : | Gas Insulated Switchgear (GIS) | Non Sticky | -| - | - | - | -| | No | | -| | Low Power Instrument Transformers | | +| : | Gas Insulated Switchgear (GIS) | Non Sticky | +|-----|-----------------------------------|--------------| +| | No | | +| | Low Power Instrument Transformers | | -| Hazardous area classification | -| - | -| Ambient temp. | -| Location | -| Humidity | +| Hazardous area classification | +|---------------------------------| +| Ambient temp. | +| Location | +| Humidity | -| | : | Non hazardous | -| - | - | - | -| ºC | : | Min. -5, max. +40 | -| | : | Indoor | -| % | : | 100 | +| | : | Non hazardous | +|----|-----|-------------------| +| ºC | : | Min. -5, max. +40 | +| | : | Indoor | +| % | : | 100 | -| Arc testing | -| - | +| Arc testing | +|---------------| -| Yes/no | : | Yes | Note 2 | -| - | - | - | - | +| Yes/no | : | Yes | Note 2 | +|----------|-----|-------|----------| -| Converted to 110VDC | Converted to 110VDC | Converted to 110VDC | Converted to 110VDC | Converted to 110VDC | -| - | - | - | - | - | -| Arc test (type test) | Arc test (type test) | Arc test (type test) | Arc test (type test) | Arc test (type test) | -| | | | | | +| Converted to 110VDC | Converted to 110VDC | Converted to 110VDC | Converted to 110VDC | Converted to 110VDC | +|------------------------|------------------------|------------------------|------------------------|------------------------| +| Arc test (type test) | Arc test (type test) | Arc test (type test) | Arc test (type test) | Arc test (type test) | +| | | | | | diff --git a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_08_one_cell_anchor.xlsx.md b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_08_one_cell_anchor.xlsx.md index 3a2e37f5..ab7814b8 100644 --- a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_08_one_cell_anchor.xlsx.md +++ b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_08_one_cell_anchor.xlsx.md @@ -1,6 +1,6 @@ -| Header1 | Header2 | -| - | - | -| data1 | data2 | -| data3 | data4 | +| Header1 | Header2 | +|-----------|-----------| +| data1 | data2 | +| data3 | data4 | diff --git a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_08_one_cell_anchor.xlsx.strict.md b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_08_one_cell_anchor.xlsx.strict.md index 3a2e37f5..ab7814b8 100644 --- a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_08_one_cell_anchor.xlsx.strict.md +++ b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_08_one_cell_anchor.xlsx.strict.md @@ -1,6 +1,6 @@ -| Header1 | Header2 | -| - | - | -| data1 | data2 | -| data3 | data4 | +| Header1 | Header2 | +|-----------|-----------| +| data1 | data2 | +| data3 | data4 | diff --git a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_comments.xlsx.md b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_comments.xlsx.md index f15e3075..5f5597ee 100644 --- a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_comments.xlsx.md +++ b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_comments.xlsx.md @@ -1,14 +1,14 @@ -| Python is great | -| - | +| Python is great | +|-------------------| -| Machine learning libraries | -| - | +| Machine learning libraries | +|------------------------------| -| Year | Freshwater Ducks | Saltwater Ducks | Ducks | -| - | - | - | - | -| 2019 | 120 | 80 | 200 | -| 2020 | 135 | 95 | 230 | -| 2021 | 150 | 100 | 250 | -| 2022 | 170 | 110 | 280 | -| 2023 | 160 | 120 | 280 | -| 2024 | 180 | 130 | 310 | +| Year | Freshwater Ducks | Saltwater Ducks | Ducks | +|--------|--------------------|-------------------|---------| +| 2019 | 120 | 80 | 200 | +| 2020 | 135 | 95 | 230 | +| 2021 | 150 | 100 | 250 | +| 2022 | 170 | 110 | 280 | +| 2023 | 160 | 120 | 280 | +| 2024 | 180 | 130 | 310 | diff --git a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_comments.xlsx.strict.md b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_comments.xlsx.strict.md index f15e3075..5f5597ee 100644 --- a/crates/fleischwolf/tests/data/xlsx/expected/xlsx_comments.xlsx.strict.md +++ b/crates/fleischwolf/tests/data/xlsx/expected/xlsx_comments.xlsx.strict.md @@ -1,14 +1,14 @@ -| Python is great | -| - | +| Python is great | +|-------------------| -| Machine learning libraries | -| - | +| Machine learning libraries | +|------------------------------| -| Year | Freshwater Ducks | Saltwater Ducks | Ducks | -| - | - | - | - | -| 2019 | 120 | 80 | 200 | -| 2020 | 135 | 95 | 230 | -| 2021 | 150 | 100 | 250 | -| 2022 | 170 | 110 | 280 | -| 2023 | 160 | 120 | 280 | -| 2024 | 180 | 130 | 310 | +| Year | Freshwater Ducks | Saltwater Ducks | Ducks | +|--------|--------------------|-------------------|---------| +| 2019 | 120 | 80 | 200 | +| 2020 | 135 | 95 | 230 | +| 2021 | 150 | 100 | 250 | +| 2022 | 170 | 110 | 280 | +| 2023 | 160 | 120 | 280 | +| 2024 | 180 | 130 | 310 | From 7bf2caa36a585552297f9cd4589dbfe0a6e47e48 Mon Sep 17 00:00:00 2001 From: artiz Date: Mon, 29 Jun 2026 12:20:08 +0200 Subject: [PATCH 19/42] docs(COMPARING): add PDF backend to the conformance overview Add a PDF row (3/14 byte-exact vs the committed groundtruth corpus, with a footnote explaining why PDF is scored against the corpus rather than live docling) and a paragraph describing the discriminative PDF pipeline (pdfium + layout/TableFormer/PaddleOCR ONNX stack), what is byte-exact today, and the text-extractor ceiling (pdfium vs docling-parse run boundaries; RTL bidi/ligature). Refresh HTML to 28/33 (corpus grew by a large Wikipedia page). Co-Authored-By: Claude Opus 4.8 (1M context) --- COMPARING.md | 34 ++++++++++++++++++++++++++++++---- 1 file changed, 30 insertions(+), 4 deletions(-) diff --git a/COMPARING.md b/COMPARING.md index a04cb7e7..dccd6de1 100644 --- a/COMPARING.md +++ b/COMPARING.md @@ -176,7 +176,32 @@ case — see the divergence table below. | **XLSX** | **9 / 9** ✅ | 9 / 9 | | **PPTX** | **7 / 7** ✅ | 7 / 7 | | **DOCX** | **25 / 26** | 25 / 26 | -| **HTML** | **28 / 32** | 29 / 32 | +| **HTML** | **28 / 33** | 28 / 33 | +| **PDF** | **3 / 14** † | 4 / 14 | + +> † The pure-parse backends above are scored against **live** docling. **PDF** is +> scored against the committed groundtruth corpus (`tests/data/pdf/groundtruth`) +> instead: it is a discriminative ML reconstruction pipeline (not a deterministic +> parse), and the corpus predates docling-core's padded table serializer, so PDF +> output uses the compact `| a | b |` table form. + +**PDF** (`*.pdf`) ports docling's *standard* (discriminative) PDF pipeline. pdfium +extracts the text layer (glyph cells + bounding boxes) and renders each page to a +bitmap; an ONNX model stack then interprets it — **layout detection** (the +`heron`/RT-DETR region model), **TableFormer** table-structure recognition (a full +port: image encoder + autoregressive OTSL structure decoder + cell-bbox decoder, +exported to ONNX — see `tableformer.rs`, with cv2-exact `INTER_AREA`/`INTER_LINEAR` +preprocessing in `resample.rs`), and **PaddleOCR** recognition for scanned / +image-only pages — and regions are assembled in reading order into a +`DoclingDocument`. Byte-exact today: `picture_classification`, `code_and_formula`, +and `2305.03393v1-pg9` (**including its TableFormer-reconstructed table, cell for +cell**). The rest are structurally correct but not byte-exact, and the ceiling is +the **text extractor**: pdfium differs from docling's own `docling-parse` C++ +parser at text-run boundaries (inter-run spacing such as `LABEL :`, ``, and +fractions) and on right-to-left scripts (bidi reordering + Arabic lam-alef ligature +decomposition) — differences a pdfium-based port cannot close without porting +docling-parse itself. Closest non-exact: `right_to_left_01` (within one line), +`amt_handbook_sample` (~8 lines). **DOCX** (`*.docx`) is a core port of `MsWordDocumentBackend` (`roxmltree` over the `ooxml` helper): paragraphs, headings (by style, incl. Title), **numbered @@ -218,9 +243,10 @@ the drawing parts via the shared `ooxml` zip/rels helper). The one miss all-empty table. CSV, Markdown, AsciiDoc, and the DeepSeek-OCR Markdown variant are fully -one-to-one. HTML's 4 remaining misses all require docling-internal subsystems -that depend on **headless-browser rendering** or are otherwise impractical to -port — see below. 28/32 is effectively the ceiling for a pure-parse port. +one-to-one. HTML's 5 remaining misses are a tail of docling-internal behaviours — +some requiring **headless-browser rendering**, others (a large Wikipedia page, +key-value form extraction) needing substantial structural work — see below. 28/33 +is roughly the ceiling for a pure-parse port. **AsciiDoc** (`*.asciidoc`/`*.adoc`) ports docling's line-oriented `AsciiDocBackend`: titles/sections, nested bullet/numbered lists (all rendered From 36e38ef251997ab19e2f51c5e8156bc90f83e315 Mon Sep 17 00:00:00 2001 From: artiz Date: Mon, 29 Jun 2026 13:14:33 +0200 Subject: [PATCH 20/42] =?UTF-8?q?feat(pdf):=20docling-parse=20migration=20?= =?UTF-8?q?phase=201=20=E2=80=94=20char-extraction=20parity=20+=20tooling?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Groundwork for porting docling-parse's text reconstruction (the path to higher PDF conformance). Two pieces: - Lam-alef ligature order: pdfium splits the Arabic lam-alef ligature into two chars at the *same* x in visual order (`alef-variant, lam`); docling-parse and logical order are `lam, alef-variant`. Reorder same-x ligature pairs at the glyph level. The shared-x test reliably distinguishes a real ligature from a genuine `alef + lam` sequence (article `ال`, `فعالة`) whose glyphs sit at different x — unlike the earlier ambiguous mid-word heuristic. - Debug tooling: `pdfium_backend::debug_glyphs` + `examples/dump_chars` dump the raw pdfium char stream (codepoint + x) to diff against docling-parse's char cells; `pdfium_backend` is now `pub` for inspection. Verified char-extraction parity against docling-parse: both emit chars in content-stream order (visual for RTL), and with the ligature fix the lam-alef order matches. The EXACT 3 stay exact; Arabic-only, no other regressions. Next (phase 2): port `cells.h`'s 3-pass line contraction (corner-distance adjacency + merge_with space insertion, LTR/RTL/LTR-reverse) to replace the ad-hoc reconstruction — targets multi_page (run-boundary colons) and the RTL PDFs. Co-Authored-By: Claude Opus 4.8 (1M context) --- crates/fleischwolf-pdf/examples/dump_chars.rs | 11 +++++ crates/fleischwolf-pdf/src/lib.rs | 2 +- crates/fleischwolf-pdf/src/pdfium_backend.rs | 45 +++++++++++++++++++ 3 files changed, 57 insertions(+), 1 deletion(-) create mode 100644 crates/fleischwolf-pdf/examples/dump_chars.rs diff --git a/crates/fleischwolf-pdf/examples/dump_chars.rs b/crates/fleischwolf-pdf/examples/dump_chars.rs new file mode 100644 index 00000000..922d9a19 --- /dev/null +++ b/crates/fleischwolf-pdf/examples/dump_chars.rs @@ -0,0 +1,11 @@ +//! Dump pdfium's raw char stream (codepoint + x) for a page, to compare char +//! extraction against docling-parse. Usage: `... --example dump_chars -- file.pdf` +fn main() { + let path = std::env::args().nth(1).expect("usage: dump_chars "); + let bytes = std::fs::read(&path).expect("read"); + let glyphs = fleischwolf_pdf::pdfium_backend::debug_glyphs(&bytes, 0); + println!("pdfium CHAR order (first 16):"); + for (ch, l, _b, _r, _t) in glyphs.iter().take(16) { + println!(" {:?} U+{:04X} xl={:.1}", ch, *ch as u32, l); + } +} diff --git a/crates/fleischwolf-pdf/src/lib.rs b/crates/fleischwolf-pdf/src/lib.rs index 5f112e8c..506ae43b 100644 --- a/crates/fleischwolf-pdf/src/lib.rs +++ b/crates/fleischwolf-pdf/src/lib.rs @@ -13,7 +13,7 @@ mod assemble; pub mod layout; mod mets; mod ocr; -mod pdfium_backend; +pub mod pdfium_backend; pub mod resample; pub mod tableformer; diff --git a/crates/fleischwolf-pdf/src/pdfium_backend.rs b/crates/fleischwolf-pdf/src/pdfium_backend.rs index a68090d1..60fee18c 100644 --- a/crates/fleischwolf-pdf/src/pdfium_backend.rs +++ b/crates/fleischwolf-pdf/src/pdfium_backend.rs @@ -237,6 +237,34 @@ impl Drop for FfiText<'_> { /// pdfium emits these on most lines and they pin word splits exactly. Hard line /// breaks are dropped (line structure comes from geometry); the gap heuristic in /// [`lines_from_glyphs`] is the fallback for the lines pdfium leaves space-less. +/// Debug helper: the raw pdfium glyph stream (codepoint + native bottom-left +/// box) for a page, in pdfium's character order. For comparing against +/// docling-parse's char cells. +pub fn debug_glyphs(bytes: &[u8], index: i32) -> Vec<(char, f32, f32, f32, f32)> { + let Ok(pdfium) = bind() else { + return Vec::new(); + }; + let ffi = FfiText::load(pdfium.bindings(), bytes, None); + if ffi.doc.is_null() { + return Vec::new(); + } + let b = ffi.bindings; + let page = b.FPDF_LoadPage(ffi.doc, index); + if page.is_null() { + return Vec::new(); + } + let tp = b.FPDFText_LoadPage(page); + let mut out = Vec::new(); + if !tp.is_null() { + for g in glyphs(b, tp) { + out.push((g.ch, g.l, g.b, g.r, g.t)); + } + b.FPDFText_ClosePage(tp); + } + b.FPDF_ClosePage(page); + out +} + fn glyphs(b: &dyn PdfiumLibraryBindings, tp: FPDF_TEXTPAGE) -> Vec { let n = b.FPDFText_CountChars(tp); let mut out = Vec::with_capacity(n.max(0) as usize); @@ -270,6 +298,23 @@ fn glyphs(b: &dyn PdfiumLibraryBindings, tp: FPDF_TEXTPAGE) -> Vec { t: top as f32, }); } + // pdfium splits the Arabic lam-alef ligature into two chars at the *same* x + // (it's one glyph) in visual order — `alef-variant, lam`. docling-parse and + // logical order are `lam, alef-variant`. Detect the ligature by the shared x + // and swap. The shared-x test reliably distinguishes a true ligature from a + // genuine `alef + lam` sequence (the article `ال`, or `فعالة`), whose two + // glyphs sit at different x and must NOT be reordered. + for i in 0..out.len().saturating_sub(1) { + let same_x = out[i].l.is_finite() + && out[i + 1].l.is_finite() + && (out[i].l - out[i + 1].l).abs() < 1.0; + if same_x + && matches!(out[i].ch, '\u{0622}' | '\u{0623}' | '\u{0625}' | '\u{0627}') + && out[i + 1].ch == '\u{0644}' + { + out.swap(i, i + 1); + } + } out } From 12301bf56f1e30f00d519a885bc74cdce228c3c0 Mon Sep 17 00:00:00 2001 From: artiz Date: Mon, 29 Jun 2026 13:44:42 +0200 Subject: [PATCH 21/42] =?UTF-8?q?feat(pdf):=20docling-parse=20migration=20?= =?UTF-8?q?phase=202=20=E2=80=94=20line-contraction=20sanitizer?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Port docling-parse's `create_line_cells` / `contract_cells_into_lines_v1` (cells.h) to `dp_lines.rs`: the 3-pass contraction (LTR → RTL → LTR-reverse) with corner-distance adjacency (`is_adjacent_to`), `merge_with` space insertion (at most one space when the bottom-corner gap exceeds 0.33×avg-char-width, plus literal space glyphs), ligature `eps_d1` relaxation, and `enforce_same_font`. Char cells are built from pdfium glyphs using the *loose* box (uniform font ascent/descent + advance, via the new `FPDFText_GetLooseCharBox`) so adjacent glyphs share a top edge like docling-parse's `compute_rect`; font is the hash of pdfium's `FPDFText_GetFontInfo` name+flags. Validated on multi_page: the sanitizer reproduces docling-parse's line cells cell-for-cell, including the font-driven split of a bold label from its regular value (`IBM MT/ST (…Typewriter)` | `: Introduced in 1964, this `) that the ad-hoc reconstruction merged. Behind the `DOCLING_DP_LINES` flag (default off) — the legacy path and all 14 PDFs are unchanged (3/14). Glyph carries both the tight box (legacy) and loose box+font (sanitizer); `debug_glyphs`/`dump_chars` now surface the font hash. Next (phase 3): wire it into assembly — join adjacent line cells with a space (docling joins line cells, where the legacy gap-aware join omits it) and have clean_text preserve the sanitizer's spacing — then validate all 14 and default on. Co-Authored-By: Claude Opus 4.8 (1M context) --- crates/fleischwolf-pdf/examples/dump_chars.rs | 13 +- crates/fleischwolf-pdf/src/dp_lines.rs | 312 ++++++++++++++++++ crates/fleischwolf-pdf/src/lib.rs | 1 + crates/fleischwolf-pdf/src/pdfium_backend.rs | 108 ++++-- 4 files changed, 410 insertions(+), 24 deletions(-) create mode 100644 crates/fleischwolf-pdf/src/dp_lines.rs diff --git a/crates/fleischwolf-pdf/examples/dump_chars.rs b/crates/fleischwolf-pdf/examples/dump_chars.rs index 922d9a19..4c5e2634 100644 --- a/crates/fleischwolf-pdf/examples/dump_chars.rs +++ b/crates/fleischwolf-pdf/examples/dump_chars.rs @@ -1,11 +1,14 @@ -//! Dump pdfium's raw char stream (codepoint + x) for a page, to compare char -//! extraction against docling-parse. Usage: `... --example dump_chars -- file.pdf` +//! Dump pdfium's raw char stream (codepoint + x + font hash) for a page. +//! Usage: `... --example dump_chars -- file.pdf` fn main() { let path = std::env::args().nth(1).expect("usage: dump_chars "); let bytes = std::fs::read(&path).expect("read"); let glyphs = fleischwolf_pdf::pdfium_backend::debug_glyphs(&bytes, 0); - println!("pdfium CHAR order (first 16):"); - for (ch, l, _b, _r, _t) in glyphs.iter().take(16) { - println!(" {:?} U+{:04X} xl={:.1}", ch, *ch as u32, l); + println!("pdfium CHAR order (ch / x / font-hash):"); + for (ch, l, font) in glyphs.iter().take(40) { + println!( + " {:?} U+{:04X} xl={:.1} font={}", + ch, *ch as u32, l, font + ); } } diff --git a/crates/fleischwolf-pdf/src/dp_lines.rs b/crates/fleischwolf-pdf/src/dp_lines.rs new file mode 100644 index 00000000..fe9428e3 --- /dev/null +++ b/crates/fleischwolf-pdf/src/dp_lines.rs @@ -0,0 +1,312 @@ +//! Port of docling-parse's line-cell sanitizer +//! (`src/parse/page_item_sanitators/cells.h` → `create_line_cells` / +//! `contract_cells_into_lines_v1`). It merges per-glyph char cells into line +//! cells via a 3-pass contraction — left-to-right, right-to-left, then +//! left-to-right with reverse — using corner-distance adjacency and inserting at +//! most one space per merge. This reproduces docling-parse's inter-word spacing +//! (justified double spaces, the space before a `:`, and RTL ordering) that the +//! ad-hoc `lines_from_glyphs` reconstruction can't. +//! +//! Geometry uses native PDF coordinates (y increases upward); each cell carries +//! its four transformed corners r0=bottom-left, r1=bottom-right, r2=top-right, +//! r3=top-left, exactly like `page_cell.h`. + +use crate::pdfium_backend::{Glyph, TextCell}; + +// config.h: the factors that actually bind for line cells. +const MERGE: f64 = 1.0; // line_space_width_factor_for_merge (adjacency gate) +const MERGE_WITH_SPACE: f64 = 0.33; // line_space_width_factor_for_merge_with_space +const H_TOL: f64 = 1.0; // horizontal_cell_tolerance (ligature eps_d1 relaxation) + +#[derive(Clone)] +struct Cell { + text: String, + rx0: f64, + ry0: f64, // bottom-left + rx1: f64, + ry1: f64, // bottom-right + rx2: f64, + ry2: f64, // top-right + rx3: f64, + ry3: f64, // top-left + ltr: bool, + active: bool, + lig_carry: bool, // last_merged_cell_was_ligature + font: u64, // hash of the PDF font name+flags (for enforce_same_font) +} + +impl Cell { + /// Length of the bottom edge (baseline advance) — `page_cell.h::length`. + fn length(&self) -> f64 { + ((self.rx1 - self.rx0).powi(2) + (self.ry1 - self.ry0).powi(2)).sqrt() + } + + /// Running mean glyph advance over the whole accumulated cell. + fn avg_char_width(&self) -> f64 { + let n = self.text.chars().count(); + if n > 0 { + self.length() / n as f64 + } else { + 0.0 + } + } + + /// Distance from this cell's bottom-right corner to `other`'s bottom-left. + fn gap(&self, other: &Cell) -> f64 { + ((self.rx1 - other.rx0).powi(2) + (self.ry1 - other.ry0).powi(2)).sqrt() + } + + /// `is_adjacent_to`: both the bottom-corner gap (`< eps0`) and the top-corner + /// gap (`< eps1`) must be small. The vertical component keeps different + /// baselines/lines from merging. + fn adjacent(&self, other: &Cell, eps0: f64, eps1: f64) -> bool { + let d0 = self.gap(other); + let d1 = ((self.rx2 - other.rx3).powi(2) + (self.ry2 - other.ry3).powi(2)).sqrt(); + d0 < eps0 && d1 < eps1 + } + + /// Punctuation/space cells are bidi-neutral bridges. + fn same_orientation(&self, other: &Cell) -> bool { + self.ltr == other.ltr || is_punct_or_space(&self.text) || is_punct_or_space(&other.text) + } + + /// `merge_with`: absorb `other` (which lies to this cell's right). Insert at + /// most one separator space when the gap exceeds `delta`. RTL prepends. + fn merge_with(&mut self, other: &Cell, delta: f64) { + let d0 = self.gap(other); + if !self.ltr || !other.ltr { + if delta < d0 { + self.text.insert(0, ' '); + } + self.text = format!("{}{}", other.text, self.text); + self.ltr = false; + } else { + if delta < d0 { + self.text.push(' '); + } + self.text.push_str(&other.text); + self.ltr = true; + } + // Extend the right edge to `other`. + self.rx1 = other.rx1; + self.ry1 = other.ry1; + self.rx2 = other.rx2; + self.ry2 = other.ry2; + } +} + +/// `applicable_for_merge`: both active, same font (ligatures bridge fonts), and +/// same reading orientation. +fn applicable(a: &Cell, b: &Cell) -> bool { + if !a.active || !b.active { + return false; + } + if a.font != b.font && !is_ligature(&a.text) && !is_ligature(&b.text) { + return false; + } + a.same_orientation(b) +} + +/// Left-to-right pass: `i` ascending accumulates cells to its right. +fn pass_ltr(cells: &mut [Cell], allow_reverse: bool) { + for i in 0..cells.len() { + if !cells[i].active { + continue; + } + let mut j = i + 1; + while j < cells.len() { + if !applicable(&cells[i], &cells[j]) { + break; + } + let i_lig = is_ligature(&cells[i].text) || cells[i].lig_carry; + let j_lig = is_ligature(&cells[j].text) || cells[j].lig_carry; + let d0 = cells[i].avg_char_width() * MERGE; + let d1 = cells[i].avg_char_width() * MERGE_WITH_SPACE; + let adj_d1 = d0 + if i_lig || j_lig { H_TOL } else { 0.0 }; + if cells[i].adjacent(&cells[j], d0, adj_d1) { + let other = cells[j].clone(); + cells[i].merge_with(&other, d1); + cells[i].lig_carry = is_ligature(&other.text); + cells[j].active = false; + j += 1; // i keeps absorbing the next cell to its right + } else if allow_reverse && cells[j].adjacent(&cells[i], d0, adj_d1) { + let other = cells[i].clone(); + cells[j].merge_with(&other, d1); + cells[j].lig_carry = is_ligature(&other.text); + cells[i].active = false; + break; // i is consumed + } else { + break; + } + } + } +} + +/// Right-to-left pass: `i` descending; its immediate left neighbour `i-1` +/// absorbs it (then the outer loop continues leftward through the absorber). +fn pass_rtl(cells: &mut [Cell]) { + let n = cells.len(); + for k in 0..n { + let i = n - 1 - k; + if !cells[i].active || i == 0 { + continue; + } + let j = i - 1; + if !applicable(&cells[i], &cells[j]) { + continue; + } + let i_lig = is_ligature(&cells[i].text) || cells[i].lig_carry; + let j_lig = is_ligature(&cells[j].text) || cells[j].lig_carry; + let d0 = cells[i].avg_char_width() * MERGE; + let d1 = cells[i].avg_char_width() * MERGE_WITH_SPACE; + let adj_d1 = d0 + if i_lig || j_lig { H_TOL } else { 0.0 }; + if cells[j].adjacent(&cells[i], d0, adj_d1) { + let other = cells[i].clone(); + cells[j].merge_with(&other, d1); + cells[j].lig_carry = is_ligature(&other.text); + cells[i].active = false; + } + } +} + +fn contract(cells: &mut Vec) { + pass_ltr(cells, false); + cells.retain(|c| c.active); + pass_rtl(cells); + cells.retain(|c| c.active); + pass_ltr(cells, true); + cells.retain(|c| c.active); +} + +/// Build line cells from a page's glyph stream via the docling-parse contraction. +pub(crate) fn line_cells(glyphs: &[Glyph], page_h: f32) -> Vec { + let mut cells: Vec = glyphs + .iter() + .filter_map(|g| { + // Use the loose box (uniform font ascent/descent + advance) so adjacent + // glyphs share a top edge, matching docling-parse's `compute_rect`. + if !g.ll.is_finite() { + return None; // a space pdfium gave no box for; can't place it + } + let text = g.ch.to_string(); + let ltr = !is_right_to_left(&text); + Some(Cell { + text, + rx0: g.ll as f64, + ry0: g.lb as f64, + rx1: g.lr as f64, + ry1: g.lb as f64, + rx2: g.lr as f64, + ry2: g.lt as f64, + rx3: g.ll as f64, + ry3: g.lt as f64, + ltr, + active: true, + lig_carry: false, + font: g.font, + }) + }) + .collect(); + contract(&mut cells); + cells + .into_iter() + .map(|c| { + let l = c.rx0.min(c.rx1).min(c.rx2).min(c.rx3) as f32; + let r = c.rx0.max(c.rx1).max(c.rx2).max(c.rx3) as f32; + let top = c.ry0.max(c.ry1).max(c.ry2).max(c.ry3) as f32; + let bot = c.ry0.min(c.ry1).min(c.ry2).min(c.ry3) as f32; + TextCell { + text: c.text, + l, + t: page_h - top, + r, + b: page_h - bot, + } + }) + .collect() +} + +fn is_rtl_char(c: char) -> bool { + let ch = c as u32; + (0x0600..=0x06FF).contains(&ch) + || (0x0750..=0x077F).contains(&ch) + || (0x08A0..=0x08FF).contains(&ch) + || (0xFB50..=0xFDFF).contains(&ch) + || (0xFE70..=0xFEFF).contains(&ch) + || (0x0590..=0x05FF).contains(&ch) + || (0xFB1D..=0xFB4F).contains(&ch) + || (0x0700..=0x074F).contains(&ch) + || (0x0780..=0x07BF).contains(&ch) + || (0x07C0..=0x07FF).contains(&ch) +} + +/// All codepoints are RTL-script (matches `string.h::is_right_to_left`). +fn is_right_to_left(s: &str) -> bool { + !s.is_empty() && s.chars().all(is_rtl_char) +} + +/// A single-codepoint punctuation/space cell (matches `string.h`). +fn is_punct_or_space(s: &str) -> bool { + let mut chars = s.chars(); + let (Some(c), None) = (chars.next(), chars.next()) else { + return false; + }; + if matches!( + c, + ' ' | '\t' + | '\n' + | '\r' + | '\u{0c}' + | '\u{0b}' + | '.' + | ',' + | ';' + | ':' + | '!' + | '?' + | '(' + | ')' + | '[' + | ']' + | '{' + | '}' + | '\'' + | '"' + | '`' + | '\u{2018}' + | '\u{2019}' + | '\u{201c}' + | '\u{201d}' + | '-' + | '\u{2013}' + | '\u{2014}' + | '_' + | '/' + | '\\' + | '|' + | '@' + | '#' + | '%' + | '&' + | '*' + | '+' + | '=' + | '<' + | '>' + ) { + return true; + } + let ch = c as u32; + (0x2000..=0x206F).contains(&ch) + || (0x3000..=0x303F).contains(&ch) + || (0xFE50..=0xFE6F).contains(&ch) + || (0xFF00..=0xFF0F).contains(&ch) + || (0xFF1A..=0xFF1F).contains(&ch) + || (0xFF3B..=0xFF5E).contains(&ch) +} + +/// Ligature glyph or its ASCII spelling (matches `string.h::is_ligature`). +fn is_ligature(s: &str) -> bool { + matches!(s, "ff" | "fi" | "fl" | "ffi" | "ffl") + || s.chars().any(|c| (0xFB00..=0xFB06).contains(&(c as u32))) +} diff --git a/crates/fleischwolf-pdf/src/lib.rs b/crates/fleischwolf-pdf/src/lib.rs index 506ae43b..8305205a 100644 --- a/crates/fleischwolf-pdf/src/lib.rs +++ b/crates/fleischwolf-pdf/src/lib.rs @@ -10,6 +10,7 @@ //! table-structure and OCR ONNX stages land behind [`Pipeline`] next. mod assemble; +mod dp_lines; pub mod layout; mod mets; mod ocr; diff --git a/crates/fleischwolf-pdf/src/pdfium_backend.rs b/crates/fleischwolf-pdf/src/pdfium_backend.rs index 60fee18c..03cde8c7 100644 --- a/crates/fleischwolf-pdf/src/pdfium_backend.rs +++ b/crates/fleischwolf-pdf/src/pdfium_backend.rs @@ -178,13 +178,24 @@ struct FfiText<'a> { doc: FPDF_DOCUMENT, } -/// One glyph: codepoint + native (bottom-left) box edges. -struct Glyph { - ch: char, - l: f32, - b: f32, - r: f32, - t: f32, +/// One glyph: codepoint + native (y-up) box edges. `l/b/r/t` is pdfium's *tight* +/// ink box (used by the legacy `lines_from_glyphs`); `ll/lb/lr/lt` is the *loose* +/// box (font ascent/descent + advance — uniform per font/size), which the +/// docling-parse-style sanitizer needs so adjacent glyphs share a top edge. +pub(crate) struct Glyph { + pub(crate) ch: char, + pub(crate) l: f32, + pub(crate) b: f32, + pub(crate) r: f32, + pub(crate) t: f32, + pub(crate) ll: f32, + pub(crate) lb: f32, + pub(crate) lr: f32, + pub(crate) lt: f32, + /// Hash of the PDF font name + flags (0 when not fetched). The sanitizer uses + /// it for docling-parse's `enforce_same_font` (keeps a bold label and regular + /// value as separate line cells, e.g. `LABEL : value`). + pub(crate) font: u64, } impl<'a> FfiText<'a> { @@ -211,10 +222,18 @@ impl<'a> FfiText<'a> { let out = if tp.is_null() { empty() } else { - let g = glyphs(b, tp); + let dp = std::env::var("DOCLING_DP_LINES").is_ok(); + let g = glyphs(b, tp, dp); b.FPDFText_ClosePage(tp); + // Prose line cells: the docling-parse-style sanitizer (behind a flag + // while it's validated) or the legacy gap-heuristic reconstruction. + let prose = if dp { + crate::dp_lines::line_cells(&g, page_h) + } else { + lines_from_glyphs(&g, page_h, false) + }; ( - lines_from_glyphs(&g, page_h, false), + prose, lines_from_glyphs(&g, page_h, true), words_from_glyphs(&g, page_h), ) @@ -240,7 +259,7 @@ impl Drop for FfiText<'_> { /// Debug helper: the raw pdfium glyph stream (codepoint + native bottom-left /// box) for a page, in pdfium's character order. For comparing against /// docling-parse's char cells. -pub fn debug_glyphs(bytes: &[u8], index: i32) -> Vec<(char, f32, f32, f32, f32)> { +pub fn debug_glyphs(bytes: &[u8], index: i32) -> Vec<(char, f32, u64)> { let Ok(pdfium) = bind() else { return Vec::new(); }; @@ -256,8 +275,8 @@ pub fn debug_glyphs(bytes: &[u8], index: i32) -> Vec<(char, f32, f32, f32, f32)> let tp = b.FPDFText_LoadPage(page); let mut out = Vec::new(); if !tp.is_null() { - for g in glyphs(b, tp) { - out.push((g.ch, g.l, g.b, g.r, g.t)); + for g in glyphs(b, tp, true) { + out.push((g.ch, g.l, g.font)); } b.FPDFText_ClosePage(tp); } @@ -265,7 +284,29 @@ pub fn debug_glyphs(bytes: &[u8], index: i32) -> Vec<(char, f32, f32, f32, f32)> out } -fn glyphs(b: &dyn PdfiumLibraryBindings, tp: FPDF_TEXTPAGE) -> Vec { +/// Hash a glyph's PDF font name + flags, for `enforce_same_font`. 0 if unavailable. +fn font_hash(b: &dyn PdfiumLibraryBindings, tp: FPDF_TEXTPAGE, i: i32) -> u64 { + use std::hash::{Hash, Hasher}; + let mut flags: std::os::raw::c_int = 0; + let len = b.FPDFText_GetFontInfo(tp, i, std::ptr::null_mut(), 0, &mut flags); + if len == 0 { + return 0; + } + let mut buf = vec![0u8; len as usize]; + b.FPDFText_GetFontInfo( + tp, + i, + buf.as_mut_ptr() as *mut std::os::raw::c_void, + len, + &mut flags, + ); + let mut h = std::collections::hash_map::DefaultHasher::new(); + buf.hash(&mut h); + flags.hash(&mut h); + h.finish() +} + +fn glyphs(b: &dyn PdfiumLibraryBindings, tp: FPDF_TEXTPAGE, fetch_font: bool) -> Vec { let n = b.FPDFText_CountChars(tp); let mut out = Vec::with_capacity(n.max(0) as usize); for i in 0..n { @@ -276,18 +317,42 @@ fn glyphs(b: &dyn PdfiumLibraryBindings, tp: FPDF_TEXTPAGE) -> Vec { if ch == '\r' || ch == '\n' { continue; } + let font = if fetch_font { font_hash(b, tp, i) } else { 0 }; + let (mut l, mut r, mut bot, mut top) = (0f64, 0f64, 0f64, 0f64); + let has_box = b.FPDFText_GetCharBox(tp, i, &mut l, &mut r, &mut bot, &mut top) != 0; + // Loose box: font ascent/descent + glyph advance, uniform per font/size. + let mut lr = FS_RECTF { + left: 0.0, + top: 0.0, + right: 0.0, + bottom: 0.0, + }; + let (ll, lb, lrt, ltop) = if b.FPDFText_GetLooseCharBox(tp, i, &mut lr) != 0 { + (lr.left, lr.bottom, lr.right, lr.top) + } else if has_box { + (l as f32, bot as f32, r as f32, top as f32) + } else { + (f32::NAN, 0.0, 0.0, 0.0) + }; if ch.is_whitespace() { + // Keep the space *with its box* (the docling-parse-style line sanitizer + // needs literal space glyphs); NaN `l` if pdfium reports no box (the + // legacy `lines_from_glyphs` ignores the box and only flags a space). out.push(Glyph { ch: ' ', - l: f32::NAN, - b: 0.0, - r: 0.0, - t: 0.0, + l: if has_box { l as f32 } else { f32::NAN }, + b: if has_box { bot as f32 } else { 0.0 }, + r: if has_box { r as f32 } else { 0.0 }, + t: if has_box { top as f32 } else { 0.0 }, + ll, + lb, + lr: lrt, + lt: ltop, + font, }); continue; } - let (mut l, mut r, mut bot, mut top) = (0f64, 0f64, 0f64, 0f64); - if b.FPDFText_GetCharBox(tp, i, &mut l, &mut r, &mut bot, &mut top) == 0 { + if !has_box { continue; } out.push(Glyph { @@ -296,6 +361,11 @@ fn glyphs(b: &dyn PdfiumLibraryBindings, tp: FPDF_TEXTPAGE) -> Vec { b: bot as f32, r: r as f32, t: top as f32, + ll, + lb, + lr: lrt, + lt: ltop, + font, }); } // pdfium splits the Arabic lam-alef ligature into two chars at the *same* x From 09649929cb805c7b75a85f6569e534e2800946ca Mon Sep 17 00:00:00 2001 From: artiz Date: Mon, 29 Jun 2026 13:53:16 +0200 Subject: [PATCH 22/42] =?UTF-8?q?feat(pdf):=20docling-parse=20migration=20?= =?UTF-8?q?phase=203=20=E2=80=94=20wire=20sanitizer=20into=20assembly=20(f?= =?UTF-8?q?lag)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Under DOCLING_DP_LINES, join adjacent line cells with a single space (docling joins its line cells; the legacy gap-aware join omitted it) and have clean_text preserve the sanitizer's internal spacing instead of collapsing whitespace. Both gated on the flag, so the default path is byte-identical (still 3/14). With the flag, multi_page drops 54→22 — the run-boundary colons are now correct (`Undo/Redo : Introduced`, `Typewriter) : Introduced`). amt_handbook 16→14 (justified double spaces preserved). picture_classification/code_and_formula stay exact. Two regressions remain in the dp path (to resolve before default-on): - 2305-pg9 (0→6): text is interleaved word-by-word — region_text's band sort mis-orders the sanitizer's font-split fragments on a dense two-column page. - right_to_left_01 (2→4): RTL assembly interaction. multi_page's remaining 22 are a separate ordered-list rendering issue (`- 1. Undo/Redo` vs `1. Undo/Redo`), not a sanitizer problem. Co-Authored-By: Claude Opus 4.8 (1M context) --- crates/fleischwolf-pdf/src/assemble.rs | 30 ++++++++++++++++---------- 1 file changed, 19 insertions(+), 11 deletions(-) diff --git a/crates/fleischwolf-pdf/src/assemble.rs b/crates/fleischwolf-pdf/src/assemble.rs index f0a880cb..ca2f1c4f 100644 --- a/crates/fleischwolf-pdf/src/assemble.rs +++ b/crates/fleischwolf-pdf/src/assemble.rs @@ -101,17 +101,23 @@ fn order_regions(items: &mut [T], page_w: f32, reg: impl Fn(&T) -> &Region) { /// `{ ahn }`, `Name 1 .`, `[ 9 ]`), and a geometric gap heuristic diverges from /// it more than a plain single-space join does. fn clean_text(text: &str) -> String { - let out = text + let replaced = text .replace("\u{2} ", "") .replace("\u{ad} ", "") .replace(['\u{2}', '\u{ad}'], "") // any stray wrap hyphens not at a join .replace(['\u{2018}', '\u{2019}'], "'") // ‘ ’ → ' .replace(['\u{201c}', '\u{201d}'], "\"") // “ ” → " .replace(['\u{2013}', '\u{2014}'], "-") // – — → - - .replace('\u{2026}', "...") // … → ... - .split_whitespace() - .collect::>() - .join(" "); + .replace('\u{2026}', "..."); // … → ... + let out = if std::env::var("DOCLING_DP_LINES").is_ok() { + // The docling-parse sanitizer already placed the correct spacing (e.g. + // justified double spaces); preserve internal runs of spaces, only + // normalizing line breaks/tabs and trimming the ends. + replaced.replace(['\n', '\r', '\t'], " ").trim().to_string() + } else { + // Legacy: collapse all whitespace runs to single spaces. + replaced.split_whitespace().collect::>().join(" ") + }; fix_arabic_lam_alef(&out) } @@ -201,11 +207,13 @@ fn region_text(region: &Region, cells: &[TextCell]) -> String { let x = (c.l * 10.0) as i64; ((c.t / band).round() as i64, if rtl { -x } else { x }) }); - // Join cells in reading order. Cells on different line-bands join with a - // space (line break). Cells on the same band join with a space only if there - // is a real horizontal gap between them — an RTL line is split into adjacent - // segments mid-word (`الت`|`ي` → `التي`), so abutting same-band cells must not - // get a spurious space. + // Join cells in reading order. With the docling-parse sanitizer the cells are + // already correctly spaced words/lines, so adjacent cells join with a single + // space (docling joins its line cells with a space) — matching e.g. a bold + // label and its value, `LABEL` | `: value` → `LABEL : value`. The legacy + // reconstruction instead joins same-band cells with a space only across a real + // gap, because it can split a word into abutting segments (`الت`|`ي` → `التي`). + let dp = std::env::var("DOCLING_DP_LINES").is_ok(); let mut joined = String::new(); let mut prev: Option<&&TextCell> = None; for c in &inside { @@ -213,7 +221,7 @@ fn region_text(region: &Region, cells: &[TextCell]) -> String { let same_band = ((p.t / band).round() as i64) == ((c.t / band).round() as i64); let h = (c.b - c.t).abs().max((p.b - p.t).abs()).max(1.0); let gap = if rtl { p.l - c.r } else { c.l - p.r }; - if !same_band || gap > h * 0.25 { + if dp || !same_band || gap > h * 0.25 { joined.push(' '); } } From dd95c8331df095f0078e644700c6b10781d152a9 Mon Sep 17 00:00:00 2001 From: artiz Date: Mon, 29 Jun 2026 14:21:36 +0200 Subject: [PATCH 23/42] =?UTF-8?q?feat(pdf):=20docling-parse=20migration=20?= =?UTF-8?q?phase=203.1=20=E2=80=94=20space-box=20glyph=20fidelity?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Two pdfium glyph quirks broke the sanitizer's merging: - Generated space glyphs carry a default font that blocked every word↔space merge under enforce_same_font. Spaces are now font-neutral (0), and the font check treats 0 as a wildcard. - pdfium gives generated spaces a degenerate *zero-width* loose box at the wrong baseline, so corner-distance adjacency failed and lines fragmented into words. Drop only zero-width spaces (the inter-word gap then drives merge_with's space insertion); spaces with real width are kept so justified double-spaces survive. Net effect with DOCLING_DP_LINES on: the 2305-pg9 word-interleaving is gone, and general text improves — multi_page 54→22, normal_4pages 108→82, redp5110 300→256, amt_handbook 16→14. Default path (flag off) unchanged (EXACT 3 intact). Blocker before default-on: pdfium decomposes fi/ffi ligatures into separate glyphs whose gaps trip the inserted-space rule (`conf iguration`, `di f f i cult`), regressing 2305-pg9 to 4. docling-parse keeps the ligature as one cell — next fix is to recompose them. Co-Authored-By: Claude Opus 4.8 (1M context) --- crates/fleischwolf-pdf/src/dp_lines.rs | 18 ++++++++++++++++-- crates/fleischwolf-pdf/src/pdfium_backend.rs | 9 ++++++++- 2 files changed, 24 insertions(+), 3 deletions(-) diff --git a/crates/fleischwolf-pdf/src/dp_lines.rs b/crates/fleischwolf-pdf/src/dp_lines.rs index fe9428e3..03c7a2ff 100644 --- a/crates/fleischwolf-pdf/src/dp_lines.rs +++ b/crates/fleischwolf-pdf/src/dp_lines.rs @@ -101,7 +101,13 @@ fn applicable(a: &Cell, b: &Cell) -> bool { if !a.active || !b.active { return false; } - if a.font != b.font && !is_ligature(&a.text) && !is_ligature(&b.text) { + // font 0 = unknown/space (font-neutral); ligatures bridge fonts too. + if a.font != 0 + && b.font != 0 + && a.font != b.font + && !is_ligature(&a.text) + && !is_ligature(&b.text) + { return false; } a.same_orientation(b) @@ -186,7 +192,15 @@ pub(crate) fn line_cells(glyphs: &[Glyph], page_h: f32) -> Vec { // Use the loose box (uniform font ascent/descent + advance) so adjacent // glyphs share a top edge, matching docling-parse's `compute_rect`. if !g.ll.is_finite() { - return None; // a space pdfium gave no box for; can't place it + return None; + } + // Drop *degenerate* space glyphs (zero-width loose box): pdfium's + // generated spaces get a zero-width box at the wrong baseline that + // breaks corner-distance adjacency. Without them the inter-word gap + // drives `merge_with`'s space insertion. Spaces with a real width are + // kept (they carry justified double-space information). + if g.ch == ' ' && (g.lr - g.ll).abs() < 0.5 { + return None; } let text = g.ch.to_string(); let ltr = !is_right_to_left(&text); diff --git a/crates/fleischwolf-pdf/src/pdfium_backend.rs b/crates/fleischwolf-pdf/src/pdfium_backend.rs index 03cde8c7..46076769 100644 --- a/crates/fleischwolf-pdf/src/pdfium_backend.rs +++ b/crates/fleischwolf-pdf/src/pdfium_backend.rs @@ -317,7 +317,14 @@ fn glyphs(b: &dyn PdfiumLibraryBindings, tp: FPDF_TEXTPAGE, fetch_font: bool) -> if ch == '\r' || ch == '\n' { continue; } - let font = if fetch_font { font_hash(b, tp, i) } else { 0 }; + // Spaces are font-neutral (0): pdfium's generated spaces carry a default + // font that would otherwise block every word↔space merge under + // enforce_same_font; docling-parse's spaces inherit the run's font. + let font = if fetch_font && !ch.is_whitespace() { + font_hash(b, tp, i) + } else { + 0 + }; let (mut l, mut r, mut bot, mut top) = (0f64, 0f64, 0f64, 0f64); let has_box = b.FPDFText_GetCharBox(tp, i, &mut l, &mut r, &mut bot, &mut top) != 0; // Loose box: font ascent/descent + glyph advance, uniform per font/size. From b60a5c55c6bf1e3ed2948534d3dcbc93adbe4d4b Mon Sep 17 00:00:00 2001 From: artiz Date: Mon, 29 Jun 2026 14:29:18 +0200 Subject: [PATCH 24/42] =?UTF-8?q?feat(pdf):=20docling-parse=20migration=20?= =?UTF-8?q?phase=203.2=20=E2=80=94=20recompose=20decomposed=20ligatures?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit pdfium decomposes a single ligature glyph (Latin fi/ffi, Arabic lam-alef) into several chars at the *same* loose box. The contraction's gap is a Euclidean distance (always positive), so the zero-width overlap read as a real gap and inserted spurious intra-word spaces (`conf iguration`, `di f f i cult`). Recompose them: consecutive glyphs sharing a loose box are appended into one cell, exactly like docling-parse keeps the ligature whole. This recovers 2305-pg9 to EXACT (was 0→4) and RTL_01 to 2, making the dp path net-positive with no regression vs legacy: 2203 309→285, 2206 200→168, multi_page 54→22, normal_4pages 108→82, redp5110 300→252, amt 16→14, table_mislabeled 113→111; the 3 EXACT PDFs stay exact. Still behind the flag. Co-Authored-By: Claude Opus 4.8 (1M context) --- crates/fleischwolf-pdf/examples/dump_chars.rs | 28 ++++--- crates/fleischwolf-pdf/src/dp_lines.rs | 76 ++++++++++--------- crates/fleischwolf-pdf/src/pdfium_backend.rs | 4 +- 3 files changed, 63 insertions(+), 45 deletions(-) diff --git a/crates/fleischwolf-pdf/examples/dump_chars.rs b/crates/fleischwolf-pdf/examples/dump_chars.rs index 4c5e2634..ecc81b41 100644 --- a/crates/fleischwolf-pdf/examples/dump_chars.rs +++ b/crates/fleischwolf-pdf/examples/dump_chars.rs @@ -1,14 +1,24 @@ -//! Dump pdfium's raw char stream (codepoint + x + font hash) for a page. -//! Usage: `... --example dump_chars -- file.pdf` +//! Dump pdfium's raw char stream (codepoint + loose left/right) for a page, +//! optionally filtered to a substring window. Usage: +//! ... --example dump_chars -- file.pdf [needle] fn main() { - let path = std::env::args().nth(1).expect("usage: dump_chars "); + let path = std::env::args() + .nth(1) + .expect("usage: dump_chars [needle]"); + let needle = std::env::args().nth(2); let bytes = std::fs::read(&path).expect("read"); let glyphs = fleischwolf_pdf::pdfium_backend::debug_glyphs(&bytes, 0); - println!("pdfium CHAR order (ch / x / font-hash):"); - for (ch, l, font) in glyphs.iter().take(40) { - println!( - " {:?} U+{:04X} xl={:.1} font={}", - ch, *ch as u32, l, font - ); + let text: String = glyphs.iter().map(|(c, _, _)| *c).collect(); + let start = needle + .as_deref() + .and_then(|n| text.find(n)) + .map(|b| text[..b].chars().count()) + .unwrap_or(0); + println!("pdfium chars (ch / loose-left / loose-right / gap-to-prev):"); + let mut prev_r = f32::NAN; + for (ch, l, r) in glyphs.iter().skip(start).take(20) { + let gap = l - prev_r; + println!(" {:?} l={:7.2} r={:7.2} gap={:+.2}", ch, l, r, gap); + prev_r = *r; } } diff --git a/crates/fleischwolf-pdf/src/dp_lines.rs b/crates/fleischwolf-pdf/src/dp_lines.rs index 03c7a2ff..fb3cc953 100644 --- a/crates/fleischwolf-pdf/src/dp_lines.rs +++ b/crates/fleischwolf-pdf/src/dp_lines.rs @@ -186,41 +186,49 @@ fn contract(cells: &mut Vec) { /// Build line cells from a page's glyph stream via the docling-parse contraction. pub(crate) fn line_cells(glyphs: &[Glyph], page_h: f32) -> Vec { - let mut cells: Vec = glyphs - .iter() - .filter_map(|g| { - // Use the loose box (uniform font ascent/descent + advance) so adjacent - // glyphs share a top edge, matching docling-parse's `compute_rect`. - if !g.ll.is_finite() { - return None; - } - // Drop *degenerate* space glyphs (zero-width loose box): pdfium's - // generated spaces get a zero-width box at the wrong baseline that - // breaks corner-distance adjacency. Without them the inter-word gap - // drives `merge_with`'s space insertion. Spaces with a real width are - // kept (they carry justified double-space information). - if g.ch == ' ' && (g.lr - g.ll).abs() < 0.5 { - return None; + let mut cells: Vec = Vec::new(); + for g in glyphs { + // Use the loose box (uniform font ascent/descent + advance) so adjacent + // glyphs share a top edge, matching docling-parse's `compute_rect`. + if !g.ll.is_finite() { + continue; + } + // Drop *degenerate* space glyphs (zero-width loose box): pdfium's generated + // spaces get a zero-width box at the wrong baseline that breaks the + // corner-distance adjacency. Without them the inter-word gap drives + // `merge_with`'s space insertion. Spaces with a real width are kept (they + // carry justified double-space information). + if g.ch == ' ' && (g.lr - g.ll).abs() < 0.5 { + continue; + } + // Recompose a ligature: pdfium decomposes one font glyph (Latin fi/ffi, + // Arabic lam-alef) into several chars at the *same* loose box. Append them + // into one cell so the contraction never inserts a space inside it. + if let Some(last) = cells.last_mut() { + if (last.rx0 - g.ll as f64).abs() < 0.5 && (last.rx1 - g.lr as f64).abs() < 0.5 { + last.text.push(g.ch); + last.ltr = !is_right_to_left(&last.text); + continue; } - let text = g.ch.to_string(); - let ltr = !is_right_to_left(&text); - Some(Cell { - text, - rx0: g.ll as f64, - ry0: g.lb as f64, - rx1: g.lr as f64, - ry1: g.lb as f64, - rx2: g.lr as f64, - ry2: g.lt as f64, - rx3: g.ll as f64, - ry3: g.lt as f64, - ltr, - active: true, - lig_carry: false, - font: g.font, - }) - }) - .collect(); + } + let text = g.ch.to_string(); + let ltr = !is_right_to_left(&text); + cells.push(Cell { + text, + rx0: g.ll as f64, + ry0: g.lb as f64, + rx1: g.lr as f64, + ry1: g.lb as f64, + rx2: g.lr as f64, + ry2: g.lt as f64, + rx3: g.ll as f64, + ry3: g.lt as f64, + ltr, + active: true, + lig_carry: false, + font: g.font, + }); + } contract(&mut cells); cells .into_iter() diff --git a/crates/fleischwolf-pdf/src/pdfium_backend.rs b/crates/fleischwolf-pdf/src/pdfium_backend.rs index 46076769..cbccc3b9 100644 --- a/crates/fleischwolf-pdf/src/pdfium_backend.rs +++ b/crates/fleischwolf-pdf/src/pdfium_backend.rs @@ -259,7 +259,7 @@ impl Drop for FfiText<'_> { /// Debug helper: the raw pdfium glyph stream (codepoint + native bottom-left /// box) for a page, in pdfium's character order. For comparing against /// docling-parse's char cells. -pub fn debug_glyphs(bytes: &[u8], index: i32) -> Vec<(char, f32, u64)> { +pub fn debug_glyphs(bytes: &[u8], index: i32) -> Vec<(char, f32, f32)> { let Ok(pdfium) = bind() else { return Vec::new(); }; @@ -276,7 +276,7 @@ pub fn debug_glyphs(bytes: &[u8], index: i32) -> Vec<(char, f32, u64)> { let mut out = Vec::new(); if !tp.is_null() { for g in glyphs(b, tp, true) { - out.push((g.ch, g.l, g.font)); + out.push((g.ch, g.ll, g.lr)); } b.FPDFText_ClosePage(tp); } From 11f1fe0cfac6a4390a8628227080956c00635a0f Mon Sep 17 00:00:00 2001 From: artiz Date: Mon, 29 Jun 2026 14:51:58 +0200 Subject: [PATCH 25/42] =?UTF-8?q?feat(pdf):=20ordered=20lists=20+=20wrap?= =?UTF-8?q?=20dehyphenation=20=E2=80=94=20multi=5Fpage=20EXACT=20via=20dp?= =?UTF-8?q?=20path?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - Ordered list items: detect an `N.` enumeration marker at the start of a list item and render it as an ordered item (`N. text`) instead of `- N. text`. This is un-gated, helping the whole corpus (2305v1 114→64, 2203 285→263, table_mislabeled 111→102, redp5110 252→243). - Wrap dehyphenation (dp path): a line cell ending in a hyphen/dash followed by a lowercase continuation joins without the dash or a space (`platforms—` + `reflects` → `platformsreflects`). The dash is still raw at join time (clean_text normalizes em/en dashes later), so match -, ‐, –, — all. With DOCLING_DP_LINES: multi_page 54→**EXACT**, taking the dp path to **4/14** (vs legacy 3/14). Default path unchanged. Debug dumps now show loose box + font. Co-Authored-By: Claude Opus 4.8 (1M context) --- .../fleischwolf-pdf/examples/dump_regions.rs | 2 +- crates/fleischwolf-pdf/src/assemble.rs | 67 ++++++++++++++++--- 2 files changed, 57 insertions(+), 12 deletions(-) diff --git a/crates/fleischwolf-pdf/examples/dump_regions.rs b/crates/fleischwolf-pdf/examples/dump_regions.rs index 45853e85..b9160054 100644 --- a/crates/fleischwolf-pdf/examples/dump_regions.rs +++ b/crates/fleischwolf-pdf/examples/dump_regions.rs @@ -43,7 +43,7 @@ fn main() { // raw line cells in extraction order (to inspect RTL ordering) if std::env::var("DUMP_CELLS").is_ok() { for (ci, c) in page.cells.iter().enumerate() { - let snip: String = c.text.chars().take(50).collect(); + let snip: String = c.text.chars().take(300).collect(); println!( " CELL[{ci}] t={:6.1} l={:6.1} r={:6.1} | {}", c.t, c.l, c.r, snip diff --git a/crates/fleischwolf-pdf/src/assemble.rs b/crates/fleischwolf-pdf/src/assemble.rs index ca2f1c4f..807309da 100644 --- a/crates/fleischwolf-pdf/src/assemble.rs +++ b/crates/fleischwolf-pdf/src/assemble.rs @@ -100,6 +100,19 @@ fn order_regions(items: &mut [T], page_w: f32, reg: impl Fn(&T) -> &Region) { /// tighten punctuation spacing: docling preserves the PDF's own spaces (it keeps /// `{ ahn }`, `Name 1 .`, `[ 9 ]`), and a geometric gap heuristic diverges from /// it more than a plain single-space join does. +/// An ordered-list enumeration marker at the start of a list item: leading ASCII +/// digits followed by `.`, e.g. `1. Undo/Redo` → `(1, "Undo/Redo")`. Returns +/// `None` when the text doesn't start with `digits.`. +fn parse_ordered_marker(s: &str) -> Option<(u64, String)> { + let digits: String = s.chars().take_while(|c| c.is_ascii_digit()).collect(); + if digits.is_empty() { + return None; + } + let rest = s[digits.len()..].strip_prefix('.')?; + let number = digits.parse().ok()?; + Some((number, rest.trim_start().to_string())) +} + fn clean_text(text: &str) -> String { let replaced = text .replace("\u{2} ", "") @@ -217,15 +230,33 @@ fn region_text(region: &Region, cells: &[TextCell]) -> String { let mut joined = String::new(); let mut prev: Option<&&TextCell> = None; for c in &inside { + let t = c.text.trim(); if let Some(p) = prev { let same_band = ((p.t / band).round() as i64) == ((c.t / band).round() as i64); let h = (c.b - c.t).abs().max((p.b - p.t).abs()).max(1.0); let gap = if rtl { p.l - c.r } else { c.l - p.r }; - if dp || !same_band || gap > h * 0.25 { + // Dehyphenate a wrapped word: a line ending in a hyphen/dash followed + // by a lowercase continuation joins without the dash or a space + // (`platforms—` + `reflects` → `platformsreflects`). The dash is still + // raw here (clean_text normalizes em/en dashes later), so match them all. + let ends_dash = matches!( + joined.chars().last(), + Some('-' | '\u{2010}' | '\u{2013}' | '\u{2014}') + ); + let dehyph = dp + && ends_dash + && joined + .chars() + .nth_back(1) + .is_some_and(|c| c.is_alphabetic()) + && t.chars().next().is_some_and(|c| c.is_lowercase()); + if dehyph { + joined.pop(); + } else if dp || !same_band || gap > h * 0.25 { joined.push(' '); } } - joined.push_str(c.text.trim()); + joined.push_str(t); prev = Some(c); } clean_text(&joined) @@ -455,17 +486,31 @@ pub fn assemble_page( // `##` (it never emits a top-level `#` for PDFs), so match that. "title" | "section_header" => doc.push(Node::Heading { level: 2, text }), // docling drops the rendered bullet glyph; the Markdown serializer - // adds its own `- ` marker. - "list_item" => doc.push(Node::ListItem { - ordered: false, - number: 0, - first_in_list: false, - text: text + // adds its own `- ` marker. An item whose text opens with an `N.` + // enumeration marker is an ordered item (rendered `N. text`). + "list_item" => { + let stripped = text .trim_start_matches(['•', '◦', '▪', '·', '*', '-']) .trim_start() - .to_string(), - level: 0, - }), + .to_string(); + if let Some((number, rest)) = parse_ordered_marker(&stripped) { + doc.push(Node::ListItem { + ordered: true, + number, + first_in_list: false, + text: rest, + level: 0, + }); + } else { + doc.push(Node::ListItem { + ordered: false, + number: 0, + first_in_list: false, + text: stripped, + level: 0, + }); + } + } // TableFormer structure (cells + spans, text matched from word cells) // when available; otherwise geometric grid reconstruction; finally a // single cell. From 41be9a1017c35a2b60180b9170fdbf03a4266f90 Mon Sep 17 00:00:00 2001 From: artiz Date: Mon, 29 Jun 2026 14:58:47 +0200 Subject: [PATCH 26/42] feat(pdf): make the docling-parse line sanitizer the default MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Flip the prose reconstruction to dp_lines (the docling-parse sanitizer port) by default; `DOCLING_LEGACY_LINES` restores the old gap-heuristic path. Routed through a single `use_dp_lines()` helper (pdfium_backend), used by page_cells and by assemble's cell-join + clean_text branches. PDF conformance default 3/14 → **4/14** (multi_page now byte-exact), with the rest markedly closer: 2206 200→168, redp5110 300→243, 2305v1 110→64, normal_4pages 108→82, 2203 309→263, table_mislabeled 113→102. The 3 previously-exact PDFs stay exact. DOCX/HTML/etc. are unaffected (the sanitizer is PDF-only). Updated the clean_text unit test for the preserve-spacing default. Co-Authored-By: Claude Opus 4.8 (1M context) --- crates/fleischwolf-pdf/src/assemble.rs | 9 +++++---- crates/fleischwolf-pdf/src/pdfium_backend.rs | 9 ++++++++- 2 files changed, 13 insertions(+), 5 deletions(-) diff --git a/crates/fleischwolf-pdf/src/assemble.rs b/crates/fleischwolf-pdf/src/assemble.rs index 807309da..0a87a4ac 100644 --- a/crates/fleischwolf-pdf/src/assemble.rs +++ b/crates/fleischwolf-pdf/src/assemble.rs @@ -122,7 +122,7 @@ fn clean_text(text: &str) -> String { .replace(['\u{201c}', '\u{201d}'], "\"") // “ ” → " .replace(['\u{2013}', '\u{2014}'], "-") // – — → - .replace('\u{2026}', "..."); // … → ... - let out = if std::env::var("DOCLING_DP_LINES").is_ok() { + let out = if crate::pdfium_backend::use_dp_lines() { // The docling-parse sanitizer already placed the correct spacing (e.g. // justified double spaces); preserve internal runs of spaces, only // normalizing line breaks/tabs and trimming the ends. @@ -226,7 +226,7 @@ fn region_text(region: &Region, cells: &[TextCell]) -> String { // label and its value, `LABEL` | `: value` → `LABEL : value`. The legacy // reconstruction instead joins same-band cells with a space only across a real // gap, because it can split a word into abutting segments (`الت`|`ي` → `التي`). - let dp = std::env::var("DOCLING_DP_LINES").is_ok(); + let dp = crate::pdfium_backend::use_dp_lines(); let mut joined = String::new(); let mut prev: Option<&&TextCell> = None; for c in &inside { @@ -578,7 +578,8 @@ mod tests { "Graph's \"x\"" ); assert_eq!(clean_text("a\u{2026}"), "a..."); - // Whitespace collapses; a normal space-join is preserved. - assert_eq!(clean_text("a b\nc"), "a b c"); + // The dp default (the docling-parse sanitizer) preserves internal spacing + // it placed deliberately; line breaks/tabs normalize to a space, ends trim. + assert_eq!(clean_text("a b\nc"), "a b c"); } } diff --git a/crates/fleischwolf-pdf/src/pdfium_backend.rs b/crates/fleischwolf-pdf/src/pdfium_backend.rs index cbccc3b9..535301e5 100644 --- a/crates/fleischwolf-pdf/src/pdfium_backend.rs +++ b/crates/fleischwolf-pdf/src/pdfium_backend.rs @@ -55,6 +55,13 @@ pub struct PdfDocument { /// Bind to the pdfium dynamic library. Honors `PDFIUM_DYNAMIC_LIB_PATH` (a /// directory or file), else the directory of the current exe, else the system /// library — mirroring how a deployment ships `libpdfium` alongside the binary. +/// Whether to use the docling-parse line sanitizer ([`crate::dp_lines`]) for prose +/// reconstruction — the default. Set `DOCLING_LEGACY_LINES` to fall back to the +/// older gap-heuristic `lines_from_glyphs`. +pub(crate) fn use_dp_lines() -> bool { + std::env::var("DOCLING_LEGACY_LINES").is_err() +} + fn bind() -> Result { if let Ok(path) = std::env::var("PDFIUM_DYNAMIC_LIB_PATH") { let name = Pdfium::pdfium_platform_library_name_at_path(&path); @@ -222,7 +229,7 @@ impl<'a> FfiText<'a> { let out = if tp.is_null() { empty() } else { - let dp = std::env::var("DOCLING_DP_LINES").is_ok(); + let dp = use_dp_lines(); let g = glyphs(b, tp, dp); b.FPDFText_ClosePage(tp); // Prose line cells: the docling-parse-style sanitizer (behind a flag From bd4cb288100e0a9f21607cdb51fdb152c99cd39c Mon Sep 17 00:00:00 2001 From: artiz Date: Mon, 29 Jun 2026 15:00:48 +0200 Subject: [PATCH 27/42] docs(COMPARING): PDF 4/14 via the docling-parse line sanitizer multi_page is now byte-exact and the corpus is markedly closer. Update the PDF row (4/14, within-one 5/14) and describe the dp_lines sanitizer port that closed the text-run-boundary gap; note the remaining gaps (RTL justified double-spaces, table reading-order). Co-Authored-By: Claude Opus 4.8 (1M context) --- COMPARING.md | 21 +++++++++++---------- 1 file changed, 11 insertions(+), 10 deletions(-) diff --git a/COMPARING.md b/COMPARING.md index dccd6de1..0c064aea 100644 --- a/COMPARING.md +++ b/COMPARING.md @@ -177,7 +177,7 @@ case — see the divergence table below. | **PPTX** | **7 / 7** ✅ | 7 / 7 | | **DOCX** | **25 / 26** | 25 / 26 | | **HTML** | **28 / 33** | 28 / 33 | -| **PDF** | **3 / 14** † | 4 / 14 | +| **PDF** | **4 / 14** † | 5 / 14 | > † The pure-parse backends above are scored against **live** docling. **PDF** is > scored against the committed groundtruth corpus (`tests/data/pdf/groundtruth`) @@ -193,15 +193,16 @@ port: image encoder + autoregressive OTSL structure decoder + cell-bbox decoder, exported to ONNX — see `tableformer.rs`, with cv2-exact `INTER_AREA`/`INTER_LINEAR` preprocessing in `resample.rs`), and **PaddleOCR** recognition for scanned / image-only pages — and regions are assembled in reading order into a -`DoclingDocument`. Byte-exact today: `picture_classification`, `code_and_formula`, -and `2305.03393v1-pg9` (**including its TableFormer-reconstructed table, cell for -cell**). The rest are structurally correct but not byte-exact, and the ceiling is -the **text extractor**: pdfium differs from docling's own `docling-parse` C++ -parser at text-run boundaries (inter-run spacing such as `LABEL :`, ``, and -fractions) and on right-to-left scripts (bidi reordering + Arabic lam-alef ligature -decomposition) — differences a pdfium-based port cannot close without porting -docling-parse itself. Closest non-exact: `right_to_left_01` (within one line), -`amt_handbook_sample` (~8 lines). +`DoclingDocument`. Text reconstruction ports **docling-parse's line sanitizer** +(`dp_lines.rs`, from `cells.h`): the 3-pass corner-distance contraction with +`merge_with` space insertion, `enforce_same_font`, ligature recomposition, and +loose-box geometry — fed by pdfium glyphs. This closed the text-run-boundary gap +that previously capped conformance (inter-run spacing like `LABEL :`, justified +double-spacing, lam-alef ordering). Byte-exact today: `picture_classification`, +`code_and_formula`, `2305.03393v1-pg9` (**including its TableFormer-reconstructed +table, cell for cell**), and `multi_page`. The rest are structurally correct but +not yet byte-exact — the remaining gaps are justified RTL double-spaces +(`right_to_left_01`, within one line) and table reading-order on dense papers. **DOCX** (`*.docx`) is a core port of `MsWordDocumentBackend` (`roxmltree` over the `ooxml` helper): paragraphs, headings (by style, incl. Title), **numbered From dee04c5196657b8c185eb6b34b81af866afee6c1 Mon Sep 17 00:00:00 2001 From: artiz Date: Mon, 29 Jun 2026 15:38:28 +0200 Subject: [PATCH 28/42] fix(pdf): emit padded tables + align groundtruth to live docling MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit PDF tables were compact to match the stale committed groundtruth, but conformance.sh (and DOCX/HTML) score against live published docling, which uses padded GitHub tables — so the compact rows showed as cosmetic diffs (e.g. 2305-pg9 = 14 vs live). The PDF backend no longer opts into compact tables, so it emits the padded format like every other backend. Regenerated the committed PDF groundtruth from live docling (the conformance reference) via scripts/docling_convert.py, so the fixtures and conformance.sh agree. Result vs live docling: 2305-pg9 → EXACT (4/14), and the table papers drop sharply (2203 321→275, 2206 304→276, 2305v1 94→68) now that the table format matches. multi_page/code_and_formula/picture_classification stay exact. Co-Authored-By: Claude Opus 4.8 (1M context) --- crates/fleischwolf-pdf/src/lib.rs | 4 - tests/data/pdf/groundtruth/2203.01017v2.md | 102 ++++++----- tests/data/pdf/groundtruth/2206.01062.md | 150 ++++++++-------- .../data/pdf/groundtruth/2305.03393v1-pg9.md | 14 +- tests/data/pdf/groundtruth/2305.03393v1.md | 30 ++-- tests/data/pdf/groundtruth/normal_4pages.md | 12 +- .../data/pdf/groundtruth/redp5110_sampled.md | 164 +++++++++--------- .../table_mislabeled_as_picture.md | 18 +- 8 files changed, 255 insertions(+), 239 deletions(-) diff --git a/crates/fleischwolf-pdf/src/lib.rs b/crates/fleischwolf-pdf/src/lib.rs index 8305205a..1123d634 100644 --- a/crates/fleischwolf-pdf/src/lib.rs +++ b/crates/fleischwolf-pdf/src/lib.rs @@ -105,9 +105,6 @@ impl Pipeline { // is gigabytes for a multi-thousand-page document and drives the machine // into swap). let mut doc = DoclingDocument::new(name); - // The PDF groundtruth corpus predates docling-core's padded table - // serializer, so PDF output uses the compact `| a | b |` table form. - doc.compact_tables = true; pdfium_backend::for_each_page(bytes, password, |n, _total, mut page| { self.process_one_page(n, &mut page, &mut doc) })?; @@ -187,7 +184,6 @@ impl Pipeline { name: &str, ) -> Result { let mut doc = DoclingDocument::new(name); - doc.compact_tables = true; // compact table form for PDF/image/METS output for (n, page) in pages.iter_mut().enumerate() { self.process_one_page(n, page, &mut doc)?; } diff --git a/tests/data/pdf/groundtruth/2203.01017v2.md b/tests/data/pdf/groundtruth/2203.01017v2.md index 9853a499..f553ec90 100644 --- a/tests/data/pdf/groundtruth/2203.01017v2.md +++ b/tests/data/pdf/groundtruth/2203.01017v2.md @@ -14,17 +14,17 @@ The occurrence of tables in documents is ubiquitous. They often summarise quanti ## a. Picture of a table: -| 1 | -| - | +| 1 | +|-----| 7 -| 0 | 1 2 1 | 1 2 1 | 1 2 1 | -| - | - | - | - | -| 3 4 | 5 | 6 | 7 | -| 9 13 | 10 | 11 | 12 | -| 8 2 | 14 | 15 | 16 | -| 17 | 18 | 19 | 20 | +| 0 | 1 2 1 | 1 2 1 | 1 2 1 | +|------|---------|---------|---------| +| 3 4 | 5 | 6 | 7 | +| 9 13 | 10 | 11 | 12 | +| 8 2 | 14 | 15 | 16 | +| 17 | 18 | 19 | 20 | Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: 'PMC2944238 004 02'. @@ -85,14 +85,14 @@ In this regard, we have prepared four synthetic datasets, each one containing 15 Table 1: Both 'Combined-Tabnet' and 'CombinedTabnet' are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank. -| | Tags | Bbox | Size | Format | -| - | - | - | - | - | -| PubTabNet | 3 | 3 | 509k | PNG | -| FinTabNet | 3 | 3 | 112k | PDF | -| TableBank | 3 | 7 | 145k | JPEG | -| Combined-Tabnet(*) | 3 | 3 | 400k | PNG | -| Combined(**) | 3 | 3 | 500k | PNG | -| SynthTabNet | 3 | 3 | 600k | PNG | +| | Tags | Bbox | Size | Format | +|--------------------|--------|--------|--------|----------| +| PubTabNet | 3 | 3 | 509k | PNG | +| FinTabNet | 3 | 3 | 112k | PDF | +| TableBank | 3 | 7 | 145k | JPEG | +| Combined-Tabnet(*) | 3 | 3 | 400k | PNG | +| Combined(**) | 3 | 3 | 500k | PNG | +| SynthTabNet | 3 | 3 | 600k | PNG | Tab. 1 summarizes the various attributes of the datasets. @@ -166,18 +166,18 @@ where T a and T b represent tables in tree structure HTML format. EditDist denot Structure. As shown in Tab. 2, TableFormer outperforms all SOTA methods across different datasets by a large margin for predicting the table structure from an image. All the more, our model outperforms pre-trained methods. During the evaluation we do not apply any table filtering. We also provide our baseline results on the SynthTabNet dataset. It has been observed that large tables (e.g. tables that occupy half of the page or more) yield poor predictions. We attribute this issue to the image resizing during the preprocessing step, that produces downsampled images with indistinguishable features. This problem can be addressed by treating such big tables with a separate model which accepts a large input image size. -| Model | Dataset | Simple | TEDS Complex | All | -| - | - | - | - | - | -| EDD | PTN | 91.1 | 88.7 | 89.9 | -| GTE | PTN | - | - | 93.01 | -| TableFormer | PTN | 98.5 | 95.0 | 96.75 | -| EDD | FTN | 88.4 | 92.08 | 90.6 | -| GTE | FTN | - | - | 87.14 | -| GTE (FT) | FTN | - | - | 91.02 | -| TableFormer | FTN | 97.5 | 96.0 | 96.8 | -| EDD | TB | 86.0 | - | 86.0 | -| TableFormer | TB | 89.6 | - | 89.6 | -| TableFormer | STN | 96.9 | 95.7 | 96.7 | +| Model | Dataset | Simple | TEDS Complex | All | +|-------------|-----------|----------|----------------|-------| +| EDD | PTN | 91.1 | 88.7 | 89.9 | +| GTE | PTN | - | - | 93.01 | +| TableFormer | PTN | 98.5 | 95.0 | 96.75 | +| EDD | FTN | 88.4 | 92.08 | 90.6 | +| GTE | FTN | - | - | 87.14 | +| GTE (FT) | FTN | - | - | 91.02 | +| TableFormer | FTN | 97.5 | 96.0 | 96.8 | +| EDD | TB | 86.0 | - | 86.0 | +| TableFormer | TB | 89.6 | - | 89.6 | +| TableFormer | STN | 96.9 | 95.7 | 96.7 | Table 2: Structure results on PubTabNet (PTN), FinTabNet (FTN), TableBank (TB) and SynthTabNet (STN). @@ -187,24 +187,24 @@ Cell Detection. Like any object detector, our Cell BBox Detector provides boundi Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing. -| Model | Dataset | mAP | mAP (PP) | -| - | - | - | - | -| EDD+BBox | PubTabNet | 79.2 | 82.7 | -| TableFormer | PubTabNet | 82.1 | 86.8 | -| TableFormer | SynthTabNet | 87.7 | - | +| Model | Dataset | mAP | mAP (PP) | +|-------------|-------------|-------|------------| +| EDD+BBox | PubTabNet | 79.2 | 82.7 | +| TableFormer | PubTabNet | 82.1 | 86.8 | +| TableFormer | SynthTabNet | 87.7 | - | Cell Content. In this section, we evaluate the entire pipeline of recovering a table with content. Here we put our approach to test by capitalizing on extracting content from the PDF cells rather than decoding from images. Tab. 4 shows the TEDs score of HTML code representing the structure of the table along with the content inserted in the data cell and compared with the ground-truth. Our method achieved a 5.3% increase over the state-of-the-art, and commercial solutions. We believe our scores would be higher if the HTML ground-truth matched the extracted PDF cell content. Unfortunately, there are small discrepancies such as spacings around words or special characters with various unicode representations. Table 4: Results of structure with content retrieved using cell detection on PubTabNet. In all cases the input is PDF documents with cropped tables. -| Model | Simple | TEDS Complex | All | -| - | - | - | - | -| Tabula | 78.0 | 57.8 | 67.9 | -| Traprange | 60.8 | 49.9 | 55.4 | -| Camelot | 80.0 | 66.0 | 73.0 | -| Acrobat Pro | 68.9 | 61.8 | 65.3 | -| EDD | 91.2 | 85.4 | 88.3 | -| TableFormer | 95.4 | 90.1 | 93.6 | +| Model | Simple | TEDS Complex | All | +|-------------|----------|----------------|-------| +| Tabula | 78.0 | 57.8 | 67.9 | +| Traprange | 60.8 | 49.9 | 55.4 | +| Camelot | 80.0 | 66.0 | 73.0 | +| Acrobat Pro | 68.9 | 61.8 | 65.3 | +| EDD | 91.2 | 85.4 | 88.3 | +| TableFormer | 95.4 | 90.1 | 93.6 | - a. Red - PDF cells, Green - predicted bounding boxes, Blue - post-processed predictions matched to PDF cells @@ -349,24 +349,40 @@ Figure 9: Example of a table with big empty distance between cells. -Figure 10: Example of a complex table with empty cells. +| | | ANOVA | ANOVA | 2 | +|-----------|---------|---------|---------|--------------| +| 3 | Sum Sq | 5Df | FValue | Pr (>F) | +| | 5745.2 | | 266.75 | 4.64 × 10−9 | +| 2onc | 2191.39 | | 0.7 | 2.76 × 10 | +| R1× conc | 2648.33 | | 1.48 | 1.07 × 10 -6 | +| Residuals | 236.91 | | 6 | | - +Figure 10: Example of a complex table with empty cells. Figure 11: Simple table with different style and empty cells. + + Figure 13: Table predictions example on colorful table. +PDF Cells + Figure 12: Simple table predictions and post processing. Figure 14: Example with multi-line text. +4 [4.0] + +9 μ4.1 + +## PDF Cells + Figure 15: Example with triangular table. diff --git a/tests/data/pdf/groundtruth/2206.01062.md b/tests/data/pdf/groundtruth/2206.01062.md index 1875eb4c..6a81ca46 100644 --- a/tests/data/pdf/groundtruth/2206.01062.md +++ b/tests/data/pdf/groundtruth/2206.01062.md @@ -97,21 +97,21 @@ The annotation campaign was carried out in four phases. In phase one, we identif Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row 'Total') in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges. -| | | % of Total | % of Total | % of Total | triple inter-annotator mAP @0.5-0.95 (%) | triple inter-annotator mAP @0.5-0.95 (%) | triple inter-annotator mAP @0.5-0.95 (%) | triple inter-annotator mAP @0.5-0.95 (%) | triple inter-annotator mAP @0.5-0.95 (%) | triple inter-annotator mAP @0.5-0.95 (%) | triple inter-annotator mAP @0.5-0.95 (%) | -| - | - | - | - | - | - | - | - | - | - | - | - | -| class label | Count | Train | Test | Val | All | Fin | Man | Sci | Law | Pat | Ten | -| Caption | 22524 | 2.04 | 1.77 | 2.32 | 84-89 | 40-61 | 86-92 | 94-99 | 95-99 | 69-78 | n/a | -| Footnote | 6318 | 0.60 | 0.31 | 0.58 | 83-91 | n/a | 100 | 62-88 | 85-94 | n/a | 82-97 | -| Formula | 25027 | 2.25 | 1.90 | 2.96 | 83-85 | n/a | n/a | 84-87 | 86-96 | n/a | n/a | -| List-item | 185660 | 17.19 | 13.34 | 15.82 | 87-88 | 74-83 | 90-92 | 97-97 | 81-85 | 75-88 | 93-95 | -| Page-footer | 70878 | 6.51 | 5.58 | 6.00 | 93-94 | 88-90 | 95-96 | 100 | 92-97 | 100 | 96-98 | -| Page-header | 58022 | 5.10 | 6.70 | 5.06 | 85-89 | 66-76 | 90-94 | 98-100 | 91-92 | 97-99 | 81-86 | -| Picture | 45976 | 4.21 | 2.78 | 5.31 | 69-71 | 56-59 | 82-86 | 69-82 | 80-95 | 66-71 | 59-76 | -| Section-header | 142884 | 12.60 | 15.77 | 12.85 | 83-84 | 76-81 | 90-92 | 94-95 | 87-94 | 69-73 | 78-86 | -| Table | 34733 | 3.20 | 2.27 | 3.60 | 77-81 | 75-80 | 83-86 | 98-99 | 58-80 | 79-84 | 70-85 | -| Text | 510377 | 45.82 | 49.28 | 45.00 | 84-86 | 81-86 | 88-93 | 89-93 | 87-92 | 71-79 | 87-95 | -| Title | 5071 | 0.47 | 0.30 | 0.50 | 60-72 | 24-63 | 50-63 | 94-100 | 82-96 | 68-79 | 24-56 | -| Total | 1107470 | 941123 | 99816 | 66531 | 82-83 | 71-74 | 79-81 | 89-94 | 86-91 | 71-76 | 68-85 | +| | | % of Total | % of Total | % of Total | triple inter-annotator mAP @0.5-0.95 (%) | triple inter-annotator mAP @0.5-0.95 (%) | triple inter-annotator mAP @0.5-0.95 (%) | triple inter-annotator mAP @0.5-0.95 (%) | triple inter-annotator mAP @0.5-0.95 (%) | triple inter-annotator mAP @0.5-0.95 (%) | triple inter-annotator mAP @0.5-0.95 (%) | +|----------------|---------|--------------|--------------|--------------|--------------------------------------------|--------------------------------------------|--------------------------------------------|--------------------------------------------|--------------------------------------------|--------------------------------------------|--------------------------------------------| +| class label | Count | Train | Test | Val | All | Fin | Man | Sci | Law | Pat | Ten | +| Caption | 22524 | 2.04 | 1.77 | 2.32 | 84-89 | 40-61 | 86-92 | 94-99 | 95-99 | 69-78 | n/a | +| Footnote | 6318 | 0.60 | 0.31 | 0.58 | 83-91 | n/a | 100 | 62-88 | 85-94 | n/a | 82-97 | +| Formula | 25027 | 2.25 | 1.90 | 2.96 | 83-85 | n/a | n/a | 84-87 | 86-96 | n/a | n/a | +| List-item | 185660 | 17.19 | 13.34 | 15.82 | 87-88 | 74-83 | 90-92 | 97-97 | 81-85 | 75-88 | 93-95 | +| Page-footer | 70878 | 6.51 | 5.58 | 6.00 | 93-94 | 88-90 | 95-96 | 100 | 92-97 | 100 | 96-98 | +| Page-header | 58022 | 5.10 | 6.70 | 5.06 | 85-89 | 66-76 | 90-94 | 98-100 | 91-92 | 97-99 | 81-86 | +| Picture | 45976 | 4.21 | 2.78 | 5.31 | 69-71 | 56-59 | 82-86 | 69-82 | 80-95 | 66-71 | 59-76 | +| Section-header | 142884 | 12.60 | 15.77 | 12.85 | 83-84 | 76-81 | 90-92 | 94-95 | 87-94 | 69-73 | 78-86 | +| Table | 34733 | 3.20 | 2.27 | 3.60 | 77-81 | 75-80 | 83-86 | 98-99 | 58-80 | 79-84 | 70-85 | +| Text | 510377 | 45.82 | 49.28 | 45.00 | 84-86 | 81-86 | 88-93 | 89-93 | 87-92 | 71-79 | 87-95 | +| Title | 5071 | 0.47 | 0.30 | 0.50 | 60-72 | 24-63 | 50-63 | 94-100 | 82-96 | 68-79 | 24-56 | +| Total | 1107470 | 941123 | 99816 | 66531 | 82-83 | 71-74 | 79-81 | 89-94 | 86-91 | 71-76 | 68-85 | Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right. @@ -152,21 +152,21 @@ Phase 4: Production annotation. The previously selected 80K pages were annotated Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on DocLayNet test set. The MRCNN (Mask R-CNN) and FRCNN (Faster R-CNN) models with ResNet-50 or ResNet-101 backbone were trained based on the network architectures from the detectron2 model zoo (Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN 3x), with default configurations. The YOLO implementation utilized was YOLOv5x6 [13]. All models were initialised using pre-trained weights from the COCO 2017 dataset. -| | human | MRCNN | MRCNN | FRCNN | YOLO | -| - | - | - | - | - | - | -| | | R50 | R101 | R101 | v5x6 | -| Caption | 84-89 | 68.4 | 71.5 | 70.1 | 77.7 | -| Footnote | 83-91 | 70.9 | 71.8 | 73.7 | 77.2 | -| Formula | 83-85 | 60.1 | 63.4 | 63.5 | 66.2 | -| List-item | 87-88 | 81.2 | 80.8 | 81.0 | 86.2 | -| Page-footer | 93-94 | 61.6 | 59.3 | 58.9 | 61.1 | -| Page-header | 85-89 | 71.9 | 70.0 | 72.0 | 67.9 | -| Picture | 69-71 | 71.7 | 72.7 | 72.0 | 77.1 | -| Section-header | 83-84 | 67.6 | 69.3 | 68.4 | 74.6 | -| Table | 77-81 | 82.2 | 82.9 | 82.2 | 86.3 | -| Text | 84-86 | 84.6 | 85.8 | 85.4 | 88.1 | -| Title | 60-72 | 76.7 | 80.4 | 79.9 | 82.7 | -| All | 82-83 | 72.4 | 73.5 | 73.4 | 76.8 | +| | human | MRCNN | MRCNN | FRCNN | YOLO | +|----------------|---------|---------|---------|---------|--------| +| | | R50 | R101 | R101 | v5x6 | +| Caption | 84-89 | 68.4 | 71.5 | 70.1 | 77.7 | +| Footnote | 83-91 | 70.9 | 71.8 | 73.7 | 77.2 | +| Formula | 83-85 | 60.1 | 63.4 | 63.5 | 66.2 | +| List-item | 87-88 | 81.2 | 80.8 | 81.0 | 86.2 | +| Page-footer | 93-94 | 61.6 | 59.3 | 58.9 | 61.1 | +| Page-header | 85-89 | 71.9 | 70.0 | 72.0 | 67.9 | +| Picture | 69-71 | 71.7 | 72.7 | 72.0 | 77.1 | +| Section-header | 83-84 | 67.6 | 69.3 | 68.4 | 74.6 | +| Table | 77-81 | 82.2 | 82.9 | 82.2 | 86.3 | +| Text | 84-86 | 84.6 | 85.8 | 85.4 | 88.1 | +| Title | 60-72 | 76.7 | 80.4 | 79.9 | 82.7 | +| All | 82-83 | 72.4 | 73.5 | 73.4 | 76.8 | ## 5 EXPERIMENTS @@ -184,20 +184,20 @@ In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], F Table 3: Performance of a Mask R-CNN R50 network in mAP@0.5-0.95 scores trained on DocLayNet with different class label sets. The reduced label sets were obtained by either down-mapping or dropping labels. -| Class-count | 11 | 6 | 5 | 4 | -| - | - | - | - | - | -| Caption | 68 | Text | Text | Text | -| Footnote | 71 | Text | Text | Text | -| Formula | 60 | Text | Text | Text | -| List-item | 81 | Text | 82 | Text | -| Page-footer | 62 | 62 | - | - | -| Page-header | 72 | 68 | - | - | -| Picture | 72 | 72 | 72 | 72 | -| Section-header | 68 | 67 | 69 | 68 | -| Table | 82 | 83 | 82 | 82 | -| Text | 85 | 84 | 84 | 84 | -| Title | 77 | Sec.-h. | Sec.-h. | Sec.-h. | -| Overall | 72 | 73 | 78 | 77 | +| Class-count | 11 | 6 | 5 | 4 | +|----------------|------|---------|---------|---------| +| Caption | 68 | Text | Text | Text | +| Footnote | 71 | Text | Text | Text | +| Formula | 60 | Text | Text | Text | +| List-item | 81 | Text | 82 | Text | +| Page-footer | 62 | 62 | - | - | +| Page-header | 72 | 68 | - | - | +| Picture | 72 | 72 | 72 | 72 | +| Section-header | 68 | 67 | 69 | 68 | +| Table | 82 | 83 | 82 | 82 | +| Text | 85 | 84 | 84 | 84 | +| Title | 77 | Sec.-h. | Sec.-h. | Sec.-h. | +| Overall | 72 | 73 | 78 | 77 | ## Learning Curve @@ -209,21 +209,21 @@ The choice and number of labels can have a significant effect on the overall mod Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. Naive page-wise split will result in /tildelow 10% point improvement. -| Class-count Split | 11 | 11 | 5 | 5 | -| - | - | - | - | - | -| | Doc | Page | Doc | Page | -| Caption | 68 | 83 | | | -| Footnote | 71 | 84 | | | -| Formula | 60 | 66 | | | -| List-item | 81 | 88 | 82 | 88 | -| Page-footer | 62 | 89 | | | -| Page-header | 72 | 90 | | | -| Picture | 72 | 82 | 72 | 82 | -| Section-header | 68 | 83 | 69 | 83 | -| Table | 82 | 89 | 82 | 90 | -| Text | 85 | 91 | 84 | 90 | -| Title | 77 | 81 | | | -| All | 72 | 84 | 78 | 87 | +| Class-count Split | 11 | 11 | 5 | 5 | +|---------------------|------|------|-----|------| +| | Doc | Page | Doc | Page | +| Caption | 68 | 83 | | | +| Footnote | 71 | 84 | | | +| Formula | 60 | 66 | | | +| List-item | 81 | 88 | 82 | 88 | +| Page-footer | 62 | 89 | | | +| Page-header | 72 | 90 | | | +| Picture | 72 | 82 | 72 | 82 | +| Section-header | 68 | 83 | 69 | 83 | +| Table | 82 | 89 | 82 | 90 | +| Text | 85 | 91 | 84 | 90 | +| Title | 77 | 81 | | | +| All | 72 | 84 | 78 | 87 | ## Impact of Document Split in Train and Test Set @@ -233,22 +233,22 @@ Many documents in DocLayNet have a unique styling. In order to avoid overfitting Throughout this paper, we claim that DocLayNet's wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture , KDD '22, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network across the PubLayNet, DocBank & DocLayNet data-sets. By evaluating on common label classes of each dataset, we observe that the DocLayNet-trained model has much less pronounced variations in performance across all datasets. -| | | Testing on | Testing on | Testing on | -| - | - | - | - | - | -| Training on | labels | PLN | DB | DLN | -| PubLayNet (PLN) | Figure | 96 | 43 | 23 | -| PubLayNet (PLN) | Sec-header | 87 | - | 32 | -| | Table | 95 | 24 | 49 | -| | Text | 96 | - | 42 | -| | total | 93 | 34 | 30 | -| DocBank (DB) | Figure | 77 | 71 | 31 | -| DocBank (DB) | Table | 19 | 65 | 22 | -| DocBank (DB) | total | 48 | 68 | 27 | -| DocLayNet (DLN) | Figure | 67 | 51 | 72 | -| DocLayNet (DLN) | Sec-header | 53 | - | 68 | -| | Table | 87 | 43 | 82 | -| | Text | 77 | - | 84 | -| | total | 59 | 47 | 78 | +| | | Testing on | Testing on | Testing on | +|-----------------|------------|--------------|--------------|--------------| +| Training on | labels | PLN | DB | DLN | +| PubLayNet (PLN) | Figure | 96 | 43 | 23 | +| PubLayNet (PLN) | Sec-header | 87 | - | 32 | +| | Table | 95 | 24 | 49 | +| | Text | 96 | - | 42 | +| | total | 93 | 34 | 30 | +| DocBank (DB) | Figure | 77 | 71 | 31 | +| DocBank (DB) | Table | 19 | 65 | 22 | +| DocBank (DB) | total | 48 | 68 | 27 | +| DocLayNet (DLN) | Figure | 67 | 51 | 72 | +| DocLayNet (DLN) | Sec-header | 53 | - | 68 | +| | Table | 87 | 43 | 82 | +| | Text | 77 | - | 84 | +| | total | 59 | 47 | 78 | Section-header , Table and Text . Before training, we either mapped or excluded DocLayNet's other labels as specified in table 3, and also PubLayNet's List to Text . Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text . diff --git a/tests/data/pdf/groundtruth/2305.03393v1-pg9.md b/tests/data/pdf/groundtruth/2305.03393v1-pg9.md index 52576c63..82ec7912 100644 --- a/tests/data/pdf/groundtruth/2305.03393v1-pg9.md +++ b/tests/data/pdf/groundtruth/2305.03393v1-pg9.md @@ -6,13 +6,13 @@ We have chosen the PubTabNet data set to perform HPO, since it includes a highly Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart. -| # enc-layers | # dec-layers | Language | TEDs | TEDs | TEDs | mAP (0.75) | Inference time (secs) | -| - | - | - | - | - | - | - | - | -| # enc-layers | # dec-layers | Language | simple | complex | all | mAP (0.75) | Inference time (secs) | -| 6 | 6 | OTSL HTML | 0.965 0.969 | 0.934 0.927 | 0.955 0.955 | 0.88 0.857 | 2.73 5.39 | -| 4 | 4 | OTSL HTML | 0.938 0.952 | 0.904 0.909 | 0.927 0.938 | 0.853 0.843 | 1.97 3.77 | -| 2 | 4 | OTSL HTML | 0.923 0.945 | 0.897 0.901 | 0.915 0.931 | 0.859 0.834 | 1.91 3.81 | -| 4 | 2 | OTSL HTML | 0.952 0.944 | 0.92 0.903 | 0.942 0.931 | 0.857 0.824 | 1.22 2 | +| # enc-layers | # dec-layers | Language | TEDs | TEDs | TEDs | mAP (0.75) | Inference time (secs) | +|----------------|----------------|------------|-------------|-------------|-------------|--------------|-------------------------| +| # enc-layers | # dec-layers | Language | simple | complex | all | mAP (0.75) | Inference time (secs) | +| 6 | 6 | OTSL HTML | 0.965 0.969 | 0.934 0.927 | 0.955 0.955 | 0.88 0.857 | 2.73 5.39 | +| 4 | 4 | OTSL HTML | 0.938 0.952 | 0.904 0.909 | 0.927 0.938 | 0.853 0.843 | 1.97 3.77 | +| 2 | 4 | OTSL HTML | 0.923 0.945 | 0.897 0.901 | 0.915 0.931 | 0.859 0.834 | 1.91 3.81 | +| 4 | 2 | OTSL HTML | 0.952 0.944 | 0.92 0.903 | 0.942 0.931 | 0.857 0.824 | 1.22 2 | ## 5.2 Quantitative Results diff --git a/tests/data/pdf/groundtruth/2305.03393v1.md b/tests/data/pdf/groundtruth/2305.03393v1.md index a89a5710..f9422ad5 100644 --- a/tests/data/pdf/groundtruth/2305.03393v1.md +++ b/tests/data/pdf/groundtruth/2305.03393v1.md @@ -46,6 +46,10 @@ All known Im2Seq based models for TSR fundamentally work in similar ways. Given Fig. 2. Frequency of tokens in HTML and OTSL as they appear in PubTabNet. +HTML + +OTSL + Obviously, HTML and other general-purpose markup languages were not designed for Im2Seq models. As such, they have some serious drawbacks. First, the token vocabulary needs to be artificially large in order to describe all plausible tabular structures. Since most Im2Seq models use an autoregressive approach, they generate the sequence token by token. Therefore, to reduce inference time, a shorter sequence length is critical. Every table-cell is represented by at least two tokens ( <td> and </td> ). Furthermore, when tokenizing the HTML structure, one needs to explicitly enumerate possible column-spans and row-spans as words. In practice, this ends up requiring 28 different HTML tokens (when including column- and row-spans up to 10 cells) just to describe every table in the PubTabNet dataset. Clearly, not every token is equally represented, as is depicted in Figure 2. This skewed distribution of tokens in combination with variable token row-length makes it challenging for models to learn the HTML structure. @@ -116,13 +120,13 @@ We have chosen the PubTabNet data set to perform HPO, since it includes a highly Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart. -| # enc-layers | # dec-layers | Language | TEDs | TEDs | TEDs | mAP (0.75) | Inference time (secs) | -| - | - | - | - | - | - | - | - | -| # enc-layers | # dec-layers | Language | simple | complex | all | mAP (0.75) | Inference time (secs) | -| 6 | 6 | OTSL HTML | 0.965 0.969 | 0.934 0.927 | 0.955 0.955 | 0.88 0.857 | 2.73 5.39 | -| 4 | 4 | OTSL HTML | 0.938 0.952 | 0.904 0.909 | 0.927 0.938 | 0.853 0.843 | 1.97 3.77 | -| 2 | 4 | OTSL HTML | 0.923 0.945 | 0.897 0.901 | 0.915 0.931 | 0.859 0.834 | 1.91 3.81 | -| 4 | 2 | OTSL HTML | 0.952 0.944 | 0.92 0.903 | 0.942 0.931 | 0.857 0.824 | 1.22 2 | +| # enc-layers | # dec-layers | Language | TEDs | TEDs | TEDs | mAP (0.75) | Inference time (secs) | +|----------------|----------------|------------|-------------|-------------|-------------|--------------|-------------------------| +| # enc-layers | # dec-layers | Language | simple | complex | all | mAP (0.75) | Inference time (secs) | +| 6 | 6 | OTSL HTML | 0.965 0.969 | 0.934 0.927 | 0.955 0.955 | 0.88 0.857 | 2.73 5.39 | +| 4 | 4 | OTSL HTML | 0.938 0.952 | 0.904 0.909 | 0.927 0.938 | 0.853 0.843 | 1.97 3.77 | +| 2 | 4 | OTSL HTML | 0.923 0.945 | 0.897 0.901 | 0.915 0.931 | 0.859 0.834 | 1.91 3.81 | +| 4 | 2 | OTSL HTML | 0.952 0.944 | 0.92 0.903 | 0.942 0.931 | 0.857 0.824 | 1.22 2 | ## 5.2 Quantitative Results @@ -132,12 +136,12 @@ Additionally, the results show that OTSL has an advantage over HTML when applied Table 2. TSR and cell detection results compared between OTSL and HTML on the PubTabNet [22], FinTabNet [21] and PubTables-1M [14] data sets using TableFormer [9] (with enc=6, dec=6, heads=8). -| Data set | Language | TEDs | TEDs | TEDs | mAP(0.75) | Inference time (secs) | -| - | - | - | - | - | - | - | -| Data set | Language | simple | complex | all | mAP(0.75) | Inference time (secs) | -| PubTabNet | OTSL HTML | 0.965 0.969 | 0.934 0.927 | 0.955 0.955 | 0.88 0.857 | 2.73 5.39 | -| FinTabNet | OTSL HTML | 0.955 0.917 | 0.961 0.922 | 0.959 0.92 | 0.862 0.722 | 1.85 3.26 | -| PubTables-1M | OTSL HTML | 0.987 0.983 | 0.964 0.944 | 0.977 0.966 | 0.896 0.889 | 1.79 3.26 | +| Data set | Language | TEDs | TEDs | TEDs | mAP(0.75) | Inference time (secs) | +|--------------|------------|-------------|-------------|-------------|-------------|-------------------------| +| Data set | Language | simple | complex | all | mAP(0.75) | Inference time (secs) | +| PubTabNet | OTSL HTML | 0.965 0.969 | 0.934 0.927 | 0.955 0.955 | 0.88 0.857 | 2.73 5.39 | +| FinTabNet | OTSL HTML | 0.955 0.917 | 0.961 0.922 | 0.959 0.92 | 0.862 0.722 | 1.85 3.26 | +| PubTables-1M | OTSL HTML | 0.987 0.983 | 0.964 0.944 | 0.977 0.966 | 0.896 0.889 | 1.79 3.26 | ## 5.3 Qualitative Results diff --git a/tests/data/pdf/groundtruth/normal_4pages.md b/tests/data/pdf/groundtruth/normal_4pages.md index 821914e8..7610770f 100644 --- a/tests/data/pdf/groundtruth/normal_4pages.md +++ b/tests/data/pdf/groundtruth/normal_4pages.md @@ -48,12 +48,12 @@ [표 1] 감염병예방법 제2조제2호 개정전·후 비교 -| 구분 | 개정전 | 개정후 | -| - | - | - | -| 분류 | 1군감염병 | 1급감염병 | -| 분류 기준 | ◦ 마시는물또는식품을매개로 발생하고집단발생의우려가 큰감염병 | ◦ 생물테러감염병또는치명률이 높거나집단발생의우려가커높 은수준의격리가필요한감염병 | -| 대상 질병 | ◦ (6종)콜레라,장티푸스,파라 티푸스, 세균성이질, 장출혈 성대장균감염증,A형간염 | ◦ (17종)에볼라,페스트등 *좌측6종(2급감염병분류)은 미포함 | -| U코드 | ◦ 대상질병에U코드없음 | ◦ 대상질병에U코드일부(3종) 포함 ①신종감염병증후군→코로나19(U) ②중증급성호흡기증후군(SARS)(U) ③중동호흡기증후군(MERS)(U) | +| 구분 | 개정전 | 개정후 | +|-------|------------------------------------------------|--------------------------------------------------------------------------------| +| 분류 | 1군감염병 | 1급감염병 | +| 분류 기준 | ◦ 마시는물또는식품을매개로 발생하고집단발생의우려가 큰감염병 | ◦ 생물테러감염병또는치명률이 높거나집단발생의우려가커높 은수준의격리가필요한감염병 | +| 대상 질병 | ◦ (6종)콜레라,장티푸스,파라 티푸스, 세균성이질, 장출혈 성대장균감염증,A형간염 | ◦ (17종)에볼라,페스트등 *좌측6종(2급감염병분류)은 미포함 | +| U코드 | ◦ 대상질병에U코드없음 | ◦ 대상질병에U코드일부(3종) 포함 ①신종감염병증후군→코로나19(U) ②중증급성호흡기증후군(SARS)(U) ③중동호흡기증후군(MERS)(U) | 기존 생명보험에서 재해로 보장하는 법정1군 전염 병은 '콜레라, 장티푸스, 파라티푸스, 세균성이질, 장 출혈성대장균감염증, A형간염'이었으나 이번에 개 diff --git a/tests/data/pdf/groundtruth/redp5110_sampled.md b/tests/data/pdf/groundtruth/redp5110_sampled.md index b789d39f..ed7e8c33 100644 --- a/tests/data/pdf/groundtruth/redp5110_sampled.md +++ b/tests/data/pdf/groundtruth/redp5110_sampled.md @@ -8,49 +8,49 @@ Front cover ## Contents -| Notices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | . vii | -| - | - | -| Trademarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | viii | -| DB2 for i Center of Excellence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | . ix | -| Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | . xi | -| Authors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | . xi | -| Now you can become a published author, too! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | xiii | -| Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | xiii | -| Stay connected to IBM Redbooks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | xiv | -| Chapter 1. Securing and protecting IBM DB2 data . . . . . . . . . . . . . . . . . . . . . . . | . 1 | -| 1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | . 2 | -| 1.2 Current state of IBM i security. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | . 2 | -| 1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | . 3 | -| 1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | . 4 | -| 1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . | . 5 | -| Chapter 2. Roles and separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | . 7 | -| 2.1 Roles. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | . 8 | -| 2.1.1 DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . | . 8 | -| 2.1.2 Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . | . 8 | -| 2.1.3 Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . | . 9 | -| 2.1.4 Database Information function: QIBM_DB_SYSMON . . . . . . . . . . . . . . . . | . 9 | -| 2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . | . 9 | -| 2.1.6 Change Function Usage CL command. . . . . . . . . . . . . . . . . . . . . . . . . . . . | 10 | -| 2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view | 10 | -| 2.2 Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | 10 | -| Chapter 3. Row and Column Access Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . | 13 | -| 3.1 Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . | 14 | -| 3.1.1 Row permission and column mask definitions . . . . . . . . . . . . . . . . . . . . . . 3.1.2 Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | 14 | -| 3.2 Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . | 16 18 | -| 3.2.1 Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | 18 | -| 3.2.2 Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | 19 | -| 3.3 VERIFY_GROUP_FOR_USER function. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | 20 | -| 3.4 Establishing and controlling accessibility by using the RCAC rule text. . . . . . . . | 21 | -| 3.5 SELECT, INSERT, and UPDATE behavior with RCAC . . . . . . . . . . . . . . . . . . . | 22 | -| Human resources example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | 22 | -| 3.6 3.6.1 Assigning the QIBM_DB_SECADM function ID to the consultants. . . . . . . | 23 | -| 3.6.2 Creating group profiles for the users and their roles. . . . . . . . . . . . . . . . . . | 23 | -| 3.6.3 Demonstrating data access without RCAC. . . . . . . . . . . . . . . . . . . . . . . . . | 24 | -| 3.6.4 Defining and creating row permissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . | 25 | -| masks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | 26 | -| 3.6.5 Defining and creating column | 28 | -| 3.6.6 Activating RCAC. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6.7 Demonstrating data access with RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . | 29 | -| 3.6.8 Demonstrating data access with a view and RCAC . . . . . . . . . . . . . . . . . . | 32 | +| Notices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | . vii | +|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------| +| Trademarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | viii | +| DB2 for i Center of Excellence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | . ix | +| Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | . xi | +| Authors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | . xi | +| Now you can become a published author, too! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | xiii | +| Comments welcome. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | xiii | +| Stay connected to IBM Redbooks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | xiv | +| Chapter 1. Securing and protecting IBM DB2 data . . . . . . . . . . . . . . . . . . . . . . . | . 1 | +| 1.1 Security fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | . 2 | +| 1.2 Current state of IBM i security. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | . 2 | +| 1.3 DB2 for i security controls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | . 3 | +| 1.3.1 Existing row and column control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | . 4 | +| 1.3.2 New controls: Row and Column Access Control. . . . . . . . . . . . . . . . . . . . . | . 5 | +| Chapter 2. Roles and separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | . 7 | +| 2.1 Roles. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | . 8 | +| 2.1.1 DDM and DRDA application server access: QIBM_DB_DDMDRDA . . . . . | . 8 | +| 2.1.2 Toolbox application server access: QIBM_DB_ZDA. . . . . . . . . . . . . . . . . . | . 8 | +| 2.1.3 Database Administrator function: QIBM_DB_SQLADM . . . . . . . . . . . . . . . | . 9 | +| 2.1.4 Database Information function: QIBM_DB_SYSMON . . . . . . . . . . . . . . . . | . 9 | +| 2.1.5 Security Administrator function: QIBM_DB_SECADM . . . . . . . . . . . . . . . . | . 9 | +| 2.1.6 Change Function Usage CL command. . . . . . . . . . . . . . . . . . . . . . . . . . . . | 10 | +| 2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view | 10 | +| 2.2 Separation of duties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | 10 | +| Chapter 3. Row and Column Access Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . | 13 | +| 3.1 Explanation of RCAC and the concept of access control . . . . . . . . . . . . . . . . . . | 14 | +| 3.1.1 Row permission and column mask definitions . . . . . . . . . . . . . . . . . . . . . . 3.1.2 Enabling and activating RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | 14 | +| 3.2 Special registers and built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . | 16 18 | +| 3.2.1 Special registers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | 18 | +| 3.2.2 Built-in global variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | 19 | +| 3.3 VERIFY_GROUP_FOR_USER function. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | 20 | +| 3.4 Establishing and controlling accessibility by using the RCAC rule text. . . . . . . . | 21 | +| 3.5 SELECT, INSERT, and UPDATE behavior with RCAC . . . . . . . . . . . . . . . . . . . | 22 | +| Human resources example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | 22 | +| 3.6 3.6.1 Assigning the QIBM_DB_SECADM function ID to the consultants. . . . . . . | 23 | +| 3.6.2 Creating group profiles for the users and their roles. . . . . . . . . . . . . . . . . . | 23 | +| 3.6.3 Demonstrating data access without RCAC. . . . . . . . . . . . . . . . . . . . . . . . . | 24 | +| 3.6.4 Defining and creating row permissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . | 25 | +| masks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . | 26 | +| 3.6.5 Defining and creating column | 28 | +| 3.6.6 Activating RCAC. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6.7 Demonstrating data access with RCAC . . . . . . . . . . . . . . . . . . . . . . . . . . . | 29 | +| 3.6.8 Demonstrating data access with a view and RCAC . . . . . . . . . . . . . . . . . . | 32 | DB2 for i Center of Excellence @@ -189,21 +189,21 @@ The FUNCTION\_USAGE view contains function usage configuration details. Table 2- Table 2-1 FUNCTION\_USAGE view -| Column name | Data type | Description | -| - | - | - | -| FUNCTION_ID | VARCHAR(30) | ID of the function. | -| USER_NAME | VARCHAR(10) | Name of the user profile that has a usage setting for this function. | -| USAGE | VARCHAR(7) | Usage setting: /SM590000 ALLOWED: The user profile is allowed to use the function. /SM590000 DENIED: The user profile is not allowed to use the function. | -| USER_TYPE | VARCHAR(5) | Type of user profile: /SM590000 USER: The user profile is a user. /SM590000 GROUP: The user profile is a group. | +| Column name | Data type | Description | +|---------------|-------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------| +| FUNCTION_ID | VARCHAR(30) | ID of the function. | +| USER_NAME | VARCHAR(10) | Name of the user profile that has a usage setting for this function. | +| USAGE | VARCHAR(7) | Usage setting: /SM590000 ALLOWED: The user profile is allowed to use the function. /SM590000 DENIED: The user profile is not allowed to use the function. | +| USER_TYPE | VARCHAR(5) | Type of user profile: /SM590000 USER: The user profile is a user. /SM590000 GROUP: The user profile is a group. | To discover who has authorization to define and manage RCAC, you can use the query that is shown in Example 2-1. Example 2-1 Query to determine who has authority to define and manage RCAC -| SELECT | function_id, user_name, usage, user_type | -| - | - | +| SELECT | function_id, user_name, usage, user_type | +|------------|--------------------------------------------------------| | FROM ORDER | function_usage function_id='QIBM_DB_SECADM' user_name; | -| WHERE | | +| WHERE | | ## 2.2 Separation of duties @@ -223,20 +223,20 @@ Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL aut Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority -| User action | *JOBCTL | QIBM_DB_SECADM | QIBM_DB_SQLADM | QIBM_DB_SYSMON | No Authority | -| - | - | - | - | - | - | -| SET CURRENT DEGREE (SQL statement) | X | | X | | | -| CHGQRYA command targeting a different user's job | X | | X | | | -| STRDBMON or ENDDBMON commands targeting a different user's job | X | | X | | | -| STRDBMON or ENDDBMON commands targeting a job that matches the current user | X | | X | X | X | -| QUSRJOBI() API format 900 or System i Navigator's SQL Details for Job | X | | X | X | | -| Visual Explain within Run SQL scripts | X | | X | X | X | -| Visual Explain outside of Run SQL scripts | X | | X | | | -| ANALYZE PLAN CACHE procedure | X | | X | | | -| DUMP PLAN CACHE procedure | X | | X | | | -| MODIFY PLAN CACHE procedure | X | | X | | | -| MODIFY PLAN CACHE PROPERTIES procedure (currently does not check authority) | X | | X | | | -| CHANGE PLAN CACHE SIZE procedure (currently does not check authority) | X | | X | | | +| User action | *JOBCTL | QIBM_DB_SECADM | QIBM_DB_SQLADM | QIBM_DB_SYSMON | No Authority | +|-----------------------------------------------------------------------------|-----------|------------------|------------------|------------------|----------------| +| SET CURRENT DEGREE (SQL statement) | X | | X | | | +| CHGQRYA command targeting a different user's job | X | | X | | | +| STRDBMON or ENDDBMON commands targeting a different user's job | X | | X | | | +| STRDBMON or ENDDBMON commands targeting a job that matches the current user | X | | X | X | X | +| QUSRJOBI() API format 900 or System i Navigator's SQL Details for Job | X | | X | X | | +| Visual Explain within Run SQL scripts | X | | X | X | X | +| Visual Explain outside of Run SQL scripts | X | | X | | | +| ANALYZE PLAN CACHE procedure | X | | X | | | +| DUMP PLAN CACHE procedure | X | | X | | | +| MODIFY PLAN CACHE procedure | X | | X | | | +| MODIFY PLAN CACHE PROPERTIES procedure (currently does not check authority) | X | | X | | | +| CHANGE PLAN CACHE SIZE procedure (currently does not check authority) | X | | X | | | The SQL CREATE PERMISSION statement that is shown in Figure 3-1 is used to define and initially enable or disable the row access rules. @@ -252,11 +252,11 @@ Table 3-1 summarizes these special registers and their values. Table 3-1 Special registers and their corresponding values -| Special register | Corresponding value | -| - | - | -| USER or SESSION_USER | The effective user of the thread excluding adopted authority. | -| CURRENT_USER | The effective user of the thread including adopted authority. When no adopted authority is present, this has the same value as USER. | -| SYSTEM_USER | The authorization ID that initiated the connection. | +| Special register | Corresponding value | +|----------------------|--------------------------------------------------------------------------------------------------------------------------------------| +| USER or SESSION_USER | The effective user of the thread excluding adopted authority. | +| CURRENT_USER | The effective user of the thread including adopted authority. When no adopted authority is present, this has the same value as USER. | +| SYSTEM_USER | The authorization ID that initiated the connection. | Figure 3-5 shows the difference in the special register values when an adopted authority is used: @@ -280,17 +280,17 @@ Table 3-2 lists the nine built-in global variables. Table 3-2 Built-in global variables -| Global variable | Type | Description | -| - | - | - | -| CLIENT_HOST | VARCHAR(255) | Host name of the current client as returned by the system | -| CLIENT_IPADDR | VARCHAR(128) | IP address of the current client as returned by the system | -| CLIENT_PORT | INTEGER | Port used by the current client to communicate with the server | -| PACKAGE_NAME | VARCHAR(128) | Name of the currently running package | -| PACKAGE_SCHEMA | VARCHAR(128) | Schema name of the currently running package | -| PACKAGE_VERSION | VARCHAR(64) | Version identifier of the currently running package | -| ROUTINE_SCHEMA | VARCHAR(128) | Schema name of the currently running routine | -| ROUTINE_SPECIFIC_NAME | VARCHAR(128) | Name of the currently running routine | -| ROUTINE_TYPE | CHAR(1) | Type of the currently running routine | +| Global variable | Type | Description | +|-----------------------|--------------|----------------------------------------------------------------| +| CLIENT_HOST | VARCHAR(255) | Host name of the current client as returned by the system | +| CLIENT_IPADDR | VARCHAR(128) | IP address of the current client as returned by the system | +| CLIENT_PORT | INTEGER | Port used by the current client to communicate with the server | +| PACKAGE_NAME | VARCHAR(128) | Name of the currently running package | +| PACKAGE_SCHEMA | VARCHAR(128) | Schema name of the currently running package | +| PACKAGE_VERSION | VARCHAR(64) | Version identifier of the currently running package | +| ROUTINE_SCHEMA | VARCHAR(128) | Schema name of the currently running routine | +| ROUTINE_SPECIFIC_NAME | VARCHAR(128) | Name of the currently running routine | +| ROUTINE_TYPE | CHAR(1) | Type of the currently running routine | ## 3.3 VERIFY\_GROUP\_FOR\_USER function diff --git a/tests/data/pdf/groundtruth/table_mislabeled_as_picture.md b/tests/data/pdf/groundtruth/table_mislabeled_as_picture.md index 3405aa69..2d667a55 100644 --- a/tests/data/pdf/groundtruth/table_mislabeled_as_picture.md +++ b/tests/data/pdf/groundtruth/table_mislabeled_as_picture.md @@ -69,12 +69,12 @@ Other (Please select all that apply) -| | | State funded legal aid | CSOs | -| - | - | - | - | -| y | Persons with disabilities | * | * | -| y | Children | * | * | -| y | Women | * | * | -| y | The elderly | * | * | -| y | Migrants | * | * | -| y | Refugees,asylum seekers,or stateless persons | * | * | -| y | Internally displaced persons | * | * | \ No newline at end of file +| | | State funded legal aid | CSOs | +|----|----------------------------------------------|--------------------------|--------| +| y | Persons with disabilities | * | * | +| y | Children | * | * | +| y | Women | * | * | +| y | The elderly | * | * | +| y | Migrants | * | * | +| y | Refugees,asylum seekers,or stateless persons | * | * | +| y | Internally displaced persons | * | * | \ No newline at end of file From f88efbfae20a2ce3941e3e14998fc3af3f66054b Mon Sep 17 00:00:00 2001 From: artiz Date: Mon, 29 Jun 2026 15:54:26 +0200 Subject: [PATCH 29/42] feat(pdf): band-aware reading order for two-column pages MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Full-width regions (title, wide figures/tables spanning most of the page) now break the two-column flow into horizontal bands: within a band the left column reads fully then the right, and a full-width region reads after the band above it and before the band below. Previously a full-width title was assigned to a column by its center and emitted in the middle of the page (the DocLayNet title landed 12th instead of first). Band break requires spanning >70% of the width (a merely wide column region doesn't count), which keeps it from mis-ordering dense layouts. Net −12 diff-lines (normal_4pages 86→78, right_to_left_03 70→66, 2206 276→274); the 4 exact PDFs are unchanged. The table papers remain dominated by finer reading-order + table-content differences. Co-Authored-By: Claude Opus 4.8 (1M context) --- crates/fleischwolf-pdf/src/assemble.rs | 32 +++++++++++++++++++++++--- 1 file changed, 29 insertions(+), 3 deletions(-) diff --git a/crates/fleischwolf-pdf/src/assemble.rs b/crates/fleischwolf-pdf/src/assemble.rs index 0a87a4ac..e9cfebd0 100644 --- a/crates/fleischwolf-pdf/src/assemble.rs +++ b/crates/fleischwolf-pdf/src/assemble.rs @@ -73,11 +73,37 @@ fn order_regions(items: &mut [T], page_w: f32, reg: impl Fn(&T) -> &Region) { && items.iter().any(|t| reg(t).r <= cx) && items.iter().any(|t| reg(t).l >= cx); if two_col { + // Full-width regions (title, figures, wide tables spanning both columns) + // break the two-column flow into horizontal bands: within a band the left + // column reads fully then the right, and a full-width region reads after + // the band above it and before the band below. Band index = number of + // full-width regions above a region's top; column 1=left, 2=right, + // 3=full-width (so it sorts after that band's columns). + // Only a region spanning *most* of the page width is a band break (a + // title, a full-width figure/table) — a merely wide column region is not. + let full_band = page_w * 0.2; + let is_full = |r: &Region| r.l < cx - full_band && r.r > cx + full_band; + let full_tops: Vec = items + .iter() + .map(®) + .filter(|r| is_full(r)) + .map(|r| r.t) + .collect(); + let key = |r: &Region| -> (usize, u8) { + let bnd = full_tops.iter().filter(|&&ft| ft < r.t - 1.0).count(); + let col = if is_full(r) { + 3 + } else if (r.l + r.r) / 2.0 >= cx { + 2 + } else { + 1 + }; + (bnd, col) + }; items.sort_by(|a, b| { let (a, b) = (reg(a), reg(b)); - let ca = ((a.l + a.r) / 2.0) >= cx; - let cb = ((b.l + b.r) / 2.0) >= cx; - ca.cmp(&cb) + key(a) + .cmp(&key(b)) .then(a.t.total_cmp(&b.t)) .then(a.l.total_cmp(&b.l)) }); From a9be45c6ce6178e4b8fe29f348db63805a8a7a6a Mon Sep 17 00:00:00 2001 From: artiz Date: Mon, 29 Jun 2026 16:22:44 +0200 Subject: [PATCH 30/42] feat(pdf): merge paragraph fragments split across column/page breaks MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit docling joins a paragraph whose previous fragment ends mid-sentence (a letter, not sentence punctuation) with a lowercase continuation (`…definition of` + `lists in…` → `…definition of lists in…`); my pipeline emitted two paragraphs. Add a post-assembly pass that merges consecutive top-level paragraph nodes on that rule. A heading/table/figure between fragments ends the paragraph. 2203 277→255, 2305v1 68→56, with smaller gains on 2206/redp5110/normal_4pages; the 4 exact PDFs are unchanged (their paragraphs end with sentence punctuation, so nothing false-merges). Co-Authored-By: Claude Opus 4.8 (1M context) --- crates/fleischwolf-pdf/src/assemble.rs | 33 ++++++++++++++++++++++++++ crates/fleischwolf-pdf/src/lib.rs | 2 ++ 2 files changed, 35 insertions(+) diff --git a/crates/fleischwolf-pdf/src/assemble.rs b/crates/fleischwolf-pdf/src/assemble.rs index e9cfebd0..cfab50c5 100644 --- a/crates/fleischwolf-pdf/src/assemble.rs +++ b/crates/fleischwolf-pdf/src/assemble.rs @@ -587,6 +587,39 @@ pub fn assemble_page( } } +/// Merge paragraph fragments split across a column or page break. docling joins a +/// paragraph whose previous fragment ends mid-sentence (a letter, not sentence +/// punctuation) with a lowercase continuation: `…definition of` + `lists in…` → +/// `…definition of lists in…`. Only consecutive top-level paragraphs merge — a +/// heading/table/figure between them ends the paragraph. +pub(crate) fn merge_continuations(nodes: &mut Vec) { + let mut i = 0; + while i + 1 < nodes.len() { + let merged = match (&nodes[i], &nodes[i + 1]) { + (Node::Paragraph { text: a }, Node::Paragraph { text: b }) => { + let a_open = a + .trim_end() + .chars() + .next_back() + .is_some_and(|c| c.is_alphabetic()); + let b_cont = b + .trim_start() + .chars() + .next() + .is_some_and(|c| c.is_lowercase()); + (a_open && b_cont).then(|| format!("{} {}", a.trim_end(), b.trim_start())) + } + _ => None, + }; + if let Some(text) = merged { + nodes[i] = Node::Paragraph { text }; + nodes.remove(i + 1); + } else { + i += 1; + } + } +} + #[cfg(test)] mod tests { use super::clean_text; diff --git a/crates/fleischwolf-pdf/src/lib.rs b/crates/fleischwolf-pdf/src/lib.rs index 1123d634..233e4e1a 100644 --- a/crates/fleischwolf-pdf/src/lib.rs +++ b/crates/fleischwolf-pdf/src/lib.rs @@ -108,6 +108,7 @@ impl Pipeline { pdfium_backend::for_each_page(bytes, password, |n, _total, mut page| { self.process_one_page(n, &mut page, &mut doc) })?; + assemble::merge_continuations(&mut doc.nodes); Ok(doc) } @@ -187,6 +188,7 @@ impl Pipeline { for (n, page) in pages.iter_mut().enumerate() { self.process_one_page(n, page, &mut doc)?; } + assemble::merge_continuations(&mut doc.nodes); Ok(doc) } } From 2c3745c2f538e29fb2e55171da2fbb210542f02a Mon Sep 17 00:00:00 2001 From: artiz Date: Mon, 29 Jun 2026 16:31:44 +0200 Subject: [PATCH 31/42] fix(pdf): reconstruct zero-width space boxes as word separators MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit pdfium gives generated spaces a zero-width loose box at a wrong baseline. Dropping them (to fix the 2305-pg9 jumble) also dropped real word-separator spaces, merging words (`Information systems` → `Informationsystems`, `high-quality` → `highquality`). Now reconstruct a degenerate space by spanning the gap to the next glyph on the same line, so the sanitizer keeps it as a separator. A wrap (different baseline) or a touching gap is left alone. 2206 272→264, 2203 255→253, 2305v1 56→54, normal_4pages 76→74; the 4 exact PDFs unchanged. Co-Authored-By: Claude Opus 4.8 (1M context) --- crates/fleischwolf-pdf/src/pdfium_backend.rs | 28 ++++++++++++++++++++ 1 file changed, 28 insertions(+) diff --git a/crates/fleischwolf-pdf/src/pdfium_backend.rs b/crates/fleischwolf-pdf/src/pdfium_backend.rs index 535301e5..3036e8d9 100644 --- a/crates/fleischwolf-pdf/src/pdfium_backend.rs +++ b/crates/fleischwolf-pdf/src/pdfium_backend.rs @@ -399,6 +399,34 @@ fn glyphs(b: &dyn PdfiumLibraryBindings, tp: FPDF_TEXTPAGE, fetch_font: bool) -> out.swap(i, i + 1); } } + // Reconstruct degenerate (zero-width) loose space boxes by spanning the gap to + // the next glyph on the same line, so the sanitizer keeps them as word + // separators rather than dropping them (which would merge `Information systems` + // → `Informationsystems`). pdfium gives generated spaces a zero-width box at a + // wrong baseline; a wrap (different baseline) or a touching gap is left alone. + for i in 0..out.len() { + if out[i].ch != ' ' || (out[i].lr - out[i].ll).abs() >= 0.5 { + continue; + } + let prev = out[..i] + .iter() + .rev() + .find(|g| g.ch != ' ' && g.ll.is_finite()) + .map(|g| (g.lr, g.lb, g.lt)); + let next = out[i + 1..] + .iter() + .find(|g| g.ch != ' ' && g.ll.is_finite()) + .map(|g| (g.ll, g.lb)); + if let (Some((plr, plb, plt)), Some((nll, nlb))) = (prev, next) { + let line_h = (plt - plb).abs().max(1.0); + if (plb - nlb).abs() < line_h * 0.5 && nll > plr + 0.5 { + out[i].ll = plr; + out[i].lr = nll; + out[i].lb = plb; + out[i].lt = plt; + } + } + } out } From e2624396434de62bf8d8b8fb0c4961d781478203 Mon Sep 17 00:00:00 2001 From: artiz Date: Mon, 29 Jun 2026 16:51:33 +0200 Subject: [PATCH 32/42] feat(pdf): escape markdown special chars in prose (match docling serializer) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit docling-core's markdown serializer escapes `_` → `\_` then HTML-escapes `&`/`<`/`>` (post_process). The other backends escape at the backend level; the PDF backend didn't. Add md_escape to PDF prose nodes (headings, list items, paragraphs) — code blocks, the formula placeholder, and table cells stay raw, and strict mode still unescapes for the cleaner variant. redp5110 299→271, 2305v1 54→44, 2203 253→247, table_mislabeled 116→110 (net ~−50); the 4 exact PDFs are unchanged (no special chars in their prose). Co-Authored-By: Claude Opus 4.8 (1M context) --- crates/fleischwolf-pdf/src/assemble.rs | 24 ++++++++++++++++++++---- 1 file changed, 20 insertions(+), 4 deletions(-) diff --git a/crates/fleischwolf-pdf/src/assemble.rs b/crates/fleischwolf-pdf/src/assemble.rs index cfab50c5..d5c257e3 100644 --- a/crates/fleischwolf-pdf/src/assemble.rs +++ b/crates/fleischwolf-pdf/src/assemble.rs @@ -139,6 +139,17 @@ fn parse_ordered_marker(s: &str) -> Option<(u64, String)> { Some((number, rest.trim_start().to_string())) } +/// Escape markdown special characters the way docling-core's markdown serializer +/// does (`markdown.py` post_process): `_` → `\_`, then HTML-escape `&`, `<`, `>` +/// (quote=False, so quotes are left). Applied to prose (headings, list items, +/// paragraphs); code blocks, the formula placeholder, and table cells are left raw. +fn md_escape(text: &str) -> String { + text.replace('_', "\\_") + .replace('&', "&") + .replace('<', "<") + .replace('>', ">") +} + fn clean_text(text: &str) -> String { let replaced = text .replace("\u{2} ", "") @@ -510,7 +521,10 @@ pub fn assemble_page( match region.label { // docling renders both the document title and section headers as // `##` (it never emits a top-level `#` for PDFs), so match that. - "title" | "section_header" => doc.push(Node::Heading { level: 2, text }), + "title" | "section_header" => doc.push(Node::Heading { + level: 2, + text: md_escape(&text), + }), // docling drops the rendered bullet glyph; the Markdown serializer // adds its own `- ` marker. An item whose text opens with an `N.` // enumeration marker is an ordered item (rendered `N. text`). @@ -524,7 +538,7 @@ pub fn assemble_page( ordered: true, number, first_in_list: false, - text: rest, + text: md_escape(&rest), level: 0, }); } else { @@ -532,7 +546,7 @@ pub fn assemble_page( ordered: false, number: 0, first_in_list: false, - text: stripped, + text: md_escape(&stripped), level: 0, }); } @@ -582,7 +596,9 @@ pub fn assemble_page( } } // text, caption, footnote → paragraph - _ => doc.push(Node::Paragraph { text }), + _ => doc.push(Node::Paragraph { + text: md_escape(&text), + }), } } } From ea78a2ee85a74eef01550e33d6a418c4788f34d5 Mon Sep 17 00:00:00 2001 From: artiz Date: Mon, 29 Jun 2026 16:58:41 +0200 Subject: [PATCH 33/42] feat(pdf): normalize U+2212 minus + merge hyphen-ending paragraph fragments MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - Map U+2212 (minus sign) to ASCII `-` in clean_text alongside the en/em dashes (author-ID `[0000 −0002]` → `[0000 -0002]`). - merge_continuations now also joins a fragment ending in a wrap hyphen/dash with a lowercase continuation (`vocab-` + `ulary` → `vocab- ulary`), matching docling. 2305v1 44→40; the 4 exact PDFs unchanged. Co-Authored-By: Claude Opus 4.8 (1M context) --- crates/fleischwolf-pdf/src/assemble.rs | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/crates/fleischwolf-pdf/src/assemble.rs b/crates/fleischwolf-pdf/src/assemble.rs index d5c257e3..c35767d5 100644 --- a/crates/fleischwolf-pdf/src/assemble.rs +++ b/crates/fleischwolf-pdf/src/assemble.rs @@ -157,7 +157,7 @@ fn clean_text(text: &str) -> String { .replace(['\u{2}', '\u{ad}'], "") // any stray wrap hyphens not at a join .replace(['\u{2018}', '\u{2019}'], "'") // ‘ ’ → ' .replace(['\u{201c}', '\u{201d}'], "\"") // “ ” → " - .replace(['\u{2013}', '\u{2014}'], "-") // – — → - + .replace(['\u{2013}', '\u{2014}', '\u{2212}'], "-") // – — − → - .replace('\u{2026}', "..."); // … → ... let out = if crate::pdfium_backend::use_dp_lines() { // The docling-parse sanitizer already placed the correct spacing (e.g. @@ -613,11 +613,11 @@ pub(crate) fn merge_continuations(nodes: &mut Vec) { while i + 1 < nodes.len() { let merged = match (&nodes[i], &nodes[i + 1]) { (Node::Paragraph { text: a }, Node::Paragraph { text: b }) => { - let a_open = a - .trim_end() - .chars() - .next_back() - .is_some_and(|c| c.is_alphabetic()); + // Open if the fragment ends mid-word (a letter) or with a wrap + // hyphen/dash — docling joins `vocab-` + `ulary` → `vocab- ulary`. + let a_open = a.trim_end().chars().next_back().is_some_and(|c| { + c.is_alphabetic() || matches!(c, '-' | '\u{2010}' | '\u{2013}' | '\u{2014}') + }); let b_cont = b .trim_start() .chars() From 57a63ed226f36214cf5f6485d8a0ca9c7579fa69 Mon Sep 17 00:00:00 2001 From: artiz Date: Mon, 29 Jun 2026 17:06:30 +0200 Subject: [PATCH 34/42] feat(pdf): glue @ to the following token (table mAP headers, emails, decorators) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit `@` now binds to what follows in the word/line grouping, so a table cell reads `mAP @0.5-0.95` instead of `mAP @ 0.5-0.95`. The mismatched cell width cascaded through the padded columns, so fixing it dropped 2206 264→234. Emails and decorators benefit too. The 4 exact PDFs (incl. code_and_formula) are unchanged. Co-Authored-By: Claude Opus 4.8 (1M context) --- crates/fleischwolf-pdf/src/pdfium_backend.rs | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/crates/fleischwolf-pdf/src/pdfium_backend.rs b/crates/fleischwolf-pdf/src/pdfium_backend.rs index 3036e8d9..fa022975 100644 --- a/crates/fleischwolf-pdf/src/pdfium_backend.rs +++ b/crates/fleischwolf-pdf/src/pdfium_backend.rs @@ -627,7 +627,8 @@ fn is_close_punct(c: char) -> bool { } fn is_open_punct(c: char) -> bool { - matches!(c, '(' | '[' | '{') + // `@` glues to what follows (`mAP @0.5`, `bpf@zurich`, `@decorator`). + matches!(c, '(' | '[' | '{' | '@') } fn push_word(word: &mut String, words: &mut Vec) { From 16b126f32b39e072b525058a1b8b7f7ac0dcc88f Mon Sep 17 00:00:00 2001 From: artiz Date: Mon, 29 Jun 2026 17:16:01 +0200 Subject: [PATCH 35/42] =?UTF-8?q?docs:=20update=20PDF=20status=20=E2=80=94?= =?UTF-8?q?=20docling-parse=20migration=20done,=204/14=20vs=20live=20docli?= =?UTF-8?q?ng?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Rewrite PDF_CONFORMANCE.md (was 1/14, listing text-extraction + TableFormer as not-done — both shipped): current 4/14 byte-exact vs live docling, the docling-parse line-sanitizer port, the text/serializer fixes, and the remaining model-level blockers (TableFormer structure, layout classification, reading order, RTL doubles). Fix MIGRATION.md's stale claims (geometric tables → TableFormer; segment-join → dp_lines sanitizer; compact → padded tables; snapshot-only → byte-for-byte vs live docling). Co-Authored-By: Claude Opus 4.8 (1M context) --- MIGRATION.md | 66 ++++++------ PDF_CONFORMANCE.md | 245 ++++++++++++++------------------------------- 2 files changed, 112 insertions(+), 199 deletions(-) diff --git a/MIGRATION.md b/MIGRATION.md index 2030ea2b..c726dc09 100644 --- a/MIGRATION.md +++ b/MIGRATION.md @@ -10,8 +10,11 @@ phased plan is kept at the end as history.) > (legacy + a Rust-only *strict* mode), docling-native **JSON** output, and > **image extraction**. The declarative formats are pure-Rust and checked > byte-for-byte against *live* docling; the PDF/image/METS ML path lives in -> `fleischwolf-pdf` and is checked against a deterministic snapshot baseline. -> `cargo test` is green (unit tests + a 131-source output-regression suite). +> `fleischwolf-pdf` (pdfium + ONNX layout/TableFormer/OCR + a port of +> docling-parse's line sanitizer) and is also measured byte-for-byte against live +> docling — **4 / 14 PDF fixtures exact** (see `PDF_CONFORMANCE.md`), with a +> snapshot baseline guarding against regressions. `cargo test` is green (unit +> tests + a 131-source output-regression suite). --- @@ -79,15 +82,17 @@ PyPI; run via `scripts/conformance.sh `), not the committed groundtruth Shared OOXML infrastructure (`ooxml.rs`): a `zip` reader, `.rels` parsing, part content-type resolution, and image extraction — reused by DOCX/PPTX/XLSX/EPUB. -### ML formats — `fleischwolf-pdf`, snapshot baseline +### ML formats — `fleischwolf-pdf` -These run docling's *discriminative* PDF pipeline ported to ONNX. Output is **not -byte-for-byte** with docling (different OCR/table models — §4); it is pinned by a -deterministic snapshot (`scripts/pdf_conformance.sh`, **76/76 exact**). +These run docling's *discriminative* PDF pipeline ported to ONNX. They are now +measured **byte-for-byte against live docling** (the committed PDF groundtruth is +regenerated from it): **4 / 14 exact**, the rest close — see `PDF_CONFORMANCE.md`. +A deterministic snapshot baseline (`scripts/pdf_conformance.sh`) still guards +against regressions. | Format | How | |---|---| -| PDF | pdfium text cells + page render → RT-DETR layout (ONNX) → PP-OCRv3 OCR for scanned pages → geometric table reconstruction → reading-order assembly | +| PDF | pdfium glyph cells + page render → RT-DETR layout (ONNX) → **TableFormer** table structure (ONNX) → PP-OCRv3 OCR for scanned pages → **docling-parse line sanitizer** (`dp_lines.rs`) + reading-order assembly | | Images (tiff/webp/png/jpeg) | the same pipeline, image as a single page | | METS / Google Books | `.tar.gz` of per-page hOCR + TIFF → cells from hOCR → the same layout+assembly path (no OCR needed) | @@ -135,31 +140,33 @@ These are deliberate or unavoidable divergences, not bugs. entity re-escaping); `strict` produces cleaner Markdown. docling has no such switch. All conformance numbers are measured in **legacy** mode. -4. **Tables use the committed groundtruth's compact `| - |` format.** The whole - committed groundtruth corpus (HTML, CSV, LaTeX, PDF, …) uses docling-core's - older minimal serializer (`| a | b |` cells, single-dash `| - | - |` - separators, no width padding). `fleischwolf` now emits that exact format so it - matches the conformance reference byte-for-byte, which is required for the PDF - table work (TableFormer) to land conformant output. (Current docling-core emits - *padded* GitHub tables; we deliberately target the committed fixtures instead.) +4. **Tables use docling-core's padded GitHub format.** All backends emit the + width-padded `tabulate(tablefmt="github")` tables that current published + docling produces (columns padded to header-width+2 or the widest data cell, + numeric columns right-aligned). The PDF groundtruth was regenerated from live + docling to match. (An earlier compact `| - |` variant — to match a stale + committed corpus — was reverted; the `compact_tables` option still exists but + no backend sets it.) -5. **The PDF pipeline is discriminative and partial.** Ported from docling's - standard pipeline, with substitutions: +5. **The PDF pipeline is discriminative and byte-measured.** Ported from + docling's standard pipeline: - **Layout** — RT-DETR (`docling-layout-heron`) exported to ONNX, run via `ort`. Same model family as docling. - **OCR** — PP-OCRv3 recognition (RapidOCR) via ONNX, *not* docling's default EasyOCR; different recognizer → different scanned text. - - **Tables** — *geometric* grid reconstruction (cluster cells into rows/cols), - **not TableFormer** (docling's autoregressive table-structure model). Table - structure is approximate; complex spans are not recovered. - - **Text assembly** — line cells are joined in reading order; soft-hyphen line - wraps (pdfium emits the wrap hyphen as the U+0002 control char) are undone so - `com-`/`pact` rejoins as `compact`, and curly quotes/ellipsis are mapped to - ASCII — both matching docling. Token spacing is a plain single-space join: - docling's per-glyph spacing comes from the PDF *text stream*, which the - segment-based path can't reproduce, so citation/footnote spacing (e.g. - `[ 37 , 36 ]` vs `[37, 36]`) can still differ. - - Output is therefore a **snapshot baseline**, never byte-for-byte with docling. + - **Tables** — **TableFormer** (image encoder + autoregressive OTSL structure + decoder + cell-bbox decoder, ported to ONNX), on a cv2-exact preprocessed + crop. Reproduces docling's padded GitHub tables — `2305-pg9` is cell-for-cell + exact; multi-row headers / spans on the dense papers still differ. + - **Text assembly** — a port of docling-parse's line sanitizer (`dp_lines.rs`): + 3-pass corner-distance contraction with gap-proportional space insertion, + `enforce_same_font`, ligature recomposition, loose-box geometry. Plus + docling's markdown escaping, wrap dehyphenation, paragraph-continuation + merging, and band-aware two-column reading order. Default; set + `DOCLING_LEGACY_LINES` for the old gap-heuristic join. + - Output is measured **byte-for-byte against live docling** (PDF_CONFORMANCE.md): + **4 / 14 exact**, the rest close. The remaining gaps are model-level + (TableFormer structure on complex tables, layout classification, RTL). 6. **Extracted image bytes are real but not byte-identical.** Cropped/embedded pixels are correct, but the PNG re-encoding differs from docling's, so the @@ -184,7 +191,10 @@ Explicitly **not done**, with the reason: - **VLM pipelines** (SmolDocling / remote VLM) and **enrichment models** (picture classification, formula understanding, code understanding). Model-bound; out of scope for the discriminative port. -- **TableFormer.** Replaced by geometric table reconstruction (§4.5). + +(**TableFormer is now done** — ported to ONNX and run on every table region; see +§5 and `PDF_CONFORMANCE.md`. Geometric reconstruction remains only as the fallback +when the TableFormer graphs aren't present.) - **XML DocLang / DocTags** input backend — no `.dclg` sources in the corpus to verify against, and not in the requested scope. - **Older patent schemas.** USPTO covers the modern `v4x` XML only; the diff --git a/PDF_CONFORMANCE.md b/PDF_CONFORMANCE.md index db887372..dde82e90 100644 --- a/PDF_CONFORMANCE.md +++ b/PDF_CONFORMANCE.md @@ -1,180 +1,83 @@ -# PDF conformance roadmap +# PDF conformance How close the Rust PDF pipeline gets to docling's **default** Markdown, measured -byte-for-byte against the committed groundtruth (`tests/data/pdf/groundtruth/*.md`), -and what it would take to close the remaining gap. +byte-for-byte against the committed groundtruth (`tests/data/pdf/groundtruth/*.md`). +The groundtruth is regenerated from **live published docling**, so it agrees with +`scripts/conformance.sh pdf`. -> Measure locally with `scripts/pdf_groundtruth.sh` (no docling install needed — -> it diffs against the checked-in reference). The numbers below are the current -> state. +> Measure locally with `scripts/pdf_groundtruth.sh` (diffs the checked-in +> reference; no docling install needed) or `scripts/conformance.sh pdf` (installs +> docling and diffs against it). Diff = changed lines vs the groundtruth (one +> changed line counts as 2). ## Current state -**1 / 14 groundtruth PDFs are byte-for-byte exact** (`picture_classification`); -the rest are blocked on one of the categories below. Diff = changed lines vs the -groundtruth (one changed line counts as 2). +**4 / 14 groundtruth PDFs are byte-for-byte exact.** -| PDF | diff | dominant blocker | +| PDF | diff | dominant remaining blocker | |---|---:|---| | picture_classification | **exact** | — | -| right_to_left_01 | 4 | RTL/bidi | -| code_and_formula | 6 | inter-run spacing + code fencing | -| right_to_left_02 | 8 | RTL/bidi | -| amt_handbook_sample | 12 | double-spaces, duplicate glyphs, fractions | -| 2305.03393v1-pg9 | 25 | table structure | -| right_to_left_03 | 74 | RTL/bidi | -| multi_page | 76 | inter-run spacing + line-wrap hyphens | -| normal_4pages | 108 | reading order (CJK) | -| 2305.03393v1 | 152 | table structure | -| table_mislabeled_as_picture | 151 | table structure | -| 2203.01017v2 | 346 | table structure (+ inter-run spacing) | -| 2206.01062 | 321 | table structure | -| redp5110_sampled | 342 | table structure | - -Shipped in this PR (no regressions; `pdf_conformance` stays 76/76): -de-hyphenation + typography normalization, ``, -caption-before-image pairing, and (strict-mode only) punctuation tightening. - -Reaching ~50% exact requires the two big items below: **text-stream extraction** -(unlocks the spacing-bound PDFs) and **TableFormer** (unlocks the six -table-bound PDFs). - ---- - -## Blocker 1 — inter-run text spacing (a.k.a. "text-stream extraction") - -**Symptom.** pdfium splits a visual line into multiple style *segments* (a -citation's superscripts, a code line's tokens, mixed fonts). We emit one cell -per segment and join them with single spaces, so the real inter-run spacing is -lost: `[ 37 , 36 ]` instead of `[37, 36]`, `function add ( a , b )` instead of -`function add(a, b)`. docling reads text via pypdfium2's `get_text_range` -(`FPDFText_GetText`), which inserts spaces from each glyph's *advance* and so -reproduces the PDF's real spacing. - -**Eight approaches were tried in this PR; all regressed the aggregate and were -reverted.** The headline finding: real `FPDFText_GetText` **is reachable and -works in isolation**, but no whole-line reconstruction built on top of it beats -pdfium's native style segments. - -1. **Raw char API** (`PdfPageText::chars()` → `unicode_char()` + `loose_bounds()`, - concatenated per line). pdfium's per-char list is *unreliable*: some lines come - back with no space characters at all (`Thiscontentisextremelyvaluablefor`) and - the char order is occasionally scrambled. -2. **Raw char API + gap-based spacing** (drop pdfium's spaces, re-insert from - glyph gaps). Fixes code perfectly but garbles prose (band mis-sort merges - glyphs from adjacent lines: `valuablefor`, stray glyphs). -3. **`inside_rect()`** (`FPDFText_GetBoundedText`) over a whole line's bbox. - `GetBoundedText` ≠ `GetText`: it *drops* inter-run spaces on multi-segment - lines (`{ahn,nli,mly,taa}@zurich` vs docling's `{ ahn,nli,mly,taa } @zurich`) - and *bleeds* glyphs from vertically adjacent lines. -4. **`inside_rect` hybrid** (segment text for single-segment lines only). Same - `GetBoundedText` divergence on the lines that need fixing. -5. **Real `FPDFText_GetText`, char-detected lines.** Reached the raw call via the - public `PdfiumLibraryBindings` trait — `bindings()` on the `Pdfium`/`PdfPage` - exposes `FPDF_LoadMemDocument`/`FPDF_LoadPage`/`FPDFText_LoadPage`/`CountChars`/ - `GetCharBox`/`GetText`, so a *second raw-FFI handle on the same bytes* drives - `GetText` directly (no fork, stays publishable). **Citations read correctly in - isolation** (`[37, 36, 18, 20]`). But my char-box line detection diverges from - pdfium's line structure, and `GetText` inserts letter-tracking spaces into - display text (`Fi gures` for a tracked title) — net regression. -6. **+ U+FFFE de-hyphenation.** `GetText` encodes the wrap hyphen as **U+FFFE** - (not the segment path's U+0002); handling it recovered most prose, but the - title-tracking and line-detection issues remained. -7. **+ single-segment override** (replace a GetText line with segment text when - one segment covers it). Helped marginally; line boundaries still diverged. -8. **Segment-defined lines + `GetText` per multi-segment range** (group *segments* - into lines so the structure matches docling, `GetText` only the multi-segment - lines via a bbox→char-index range). Preserved the exact PDFs and improved - `multi_page`, but the bbox→range mapping mis-selects characters on dense - two-column pages, so citation lines on `2203`/`2206` came back wrong and - `normal_4pages` regressed (108→152). - -**Conclusion.** The blocker is *not* the missing binding — `GetText` is callable -and correct. It is that **reconstructing docling's exact line + character ranges -from glyph/segment geometry is itself the hard problem** (docling uses -`docling-parse`, a purpose-built PDF text reconstructor, not raw `GetText`). -pdfium's own style segments are a better-structured unit than anything rebuilt on -top of them here, so the segment path stays in production. A real fix needs a -faithful line/cell reconstructor (port `docling-parse`, or use pdfium's -`FPDFText_GetTextObject`/structured APIs to get true line boundaries before -`GetText`), not just the call this PR proved reachable. - -**Stopgap shipped:** `--strict` tightens the citation/parenthetical spacing at -serialization time, so strict Markdown reads cleanly even though default mode -mirrors the segment spacing. - -Also needed alongside it: -- **Line-wrap de-hyphenation for real hyphens.** We already drop the U+0002 soft - hyphen; `multi_page` wraps words with a real `-` (`professi-`/`onal`), which - needs line-end-hyphen detection during the line join. -- **Double-space preservation.** docling keeps the PDF's wide justified spacing - (`the stainless steel nuts`); `clean_text` currently collapses runs of - whitespace. With `GetText` per line, stop collapsing intra-line spacing. - -## Blocker 2 — table structure (TableFormer) - -**Symptom.** Six PDFs (`2206.01062`, `2305.03393v1[-pg9]`, `redp5110_sampled`, -`table_mislabeled_as_picture`, and the table on `2203.01017v2`) are dominated by -table differences. We reconstruct grids *geometrically* (cluster cells into -rows/columns); docling runs **TableFormer**, an autoregressive transformer that -predicts the table structure as an OTSL/HTML tag sequence plus per-cell bounding -boxes, which recovers spanning headers and merged cells we cannot. - -**Status — ONNX export + decode VERIFIED byte-exact ✅.** -`scripts/export_tableformer.py` loads `TableModel04_rs` (`accurate`, resnet18 + -6-layer encoder + 6-layer decoder) and exports two graphs, both verified against -PyTorch (max abs diff < 1e-5): - -- `encoder.onnx` — `image[1,3,448,448] → memory[784,1,512]` -- `decoder.onnx` — `tags[seq,1] + memory → logits[1,13], hidden[1,512]` - -The Rust loop (`crates/fleischwolf-pdf/src/tableformer.rs`) feeds the growing -token list back in and applies docling's two corrections (`xcel→lcel` on *every* -row — docling's `line_num` is never incremented; `ucel`-then-`lcel → fcel`). -**Verified: this reproduces docling's OTSL token sequence byte-exact** on -docling's own preprocessed table tensor (54-token sequence on `2305v1-pg9`). - -Two findings that cost real debugging, recorded so they aren't re-hit: -- The model's decoder layer keeps only `tgt[-1:]` per layer and relies on a - non-standard per-layer cache; re-running it cache-less loses deep context. - Equivalent stateless form: apply each layer to the whole prefix under a causal - mask. (Export uses the dynamo exporter so the `seq` axis stays symbolic — the - legacy tracer bakes it into `nn.MultiheadAttention`'s reshape.) -- **Export from `docling-project/docling-models`, not `ds4sd/docling-models`** — - both are cached, weights differ, and the old ones give a different OTSL. - -**Remaining (the Rust integration, staged):** - -1. **Preprocessing parity.** The decode is exact on docling's input tensor, but - the pipeline's own crop differs (docling resizes the *page* to 1024px height, - crops the table bbox, then resizes to 448²; we crop the 2× render directly). - Match this so the live OTSL matches (currently 88 vs 54 tokens on `2305v1-pg9` - purely from the crop). -2. **Bbox decoder** (`bbox.onnx`, not yet exported): per-cell hidden → box, for - matching. Interim: skip it and match by grid geometry. -3. **OTSL → grid.** `docling_ibm_models/tableformer/otsl.py` is the reference - (`fcel/ched/rhed/srow/ecel/lcel/ucel/xcel/nl`); port it to a `Table` with - row/col spans (the Markdown serializer needs span support added). -4. **Cell content.** Map the PDF text cells we already extract onto the grid - cells (docling does not OCR programmatic tables). - -A cheaper interim improvement (not docling-exact, but closes some diff): better -geometric reconstruction — detect header rows, merge obvious spanning cells, and -handle the multi-line header cells that currently shatter into many columns. - -## Blocker 3 — RTL / bidi (Arabic) - -`right_to_left_01/02/03`. Two compounding issues: (a) reading order — Latin runs -embedded in RTL text and the overall right-to-left flow are emitted left-to-right -(`Python و ة R` vs `R و Python`); we'd need Unicode bidi reordering of each line. -(b) Arabic shaping — pdfium returns presentation-form / decomposed sequences that -differ from docling's (`اإل` vs `الإ`), needing NFC-ish normalization of the -Arabic block. Both are self-contained but specialized; lower priority than 1–2. - -## Smaller items - -- **Duplicate glyphs** (`amt_handbook`: `T he`, `F Figure 7-26 6`). pdfium emits - doubled glyphs for some bold/overlapping text; needs de-duplication of - overlapping cells. -- **Code regions** → fenced ```` ``` ```` blocks with the caption *after* (code - captions trail; figure captions lead). Pairs with Blocker 1 for the code text. +| code_and_formula | **exact** | — | +| multi_page | **exact** | — | +| 2305.03393v1-pg9 | **exact** | — (TableFormer table, cell-for-cell) | +| right_to_left_01 | 2 | RTL justified double-space | +| right_to_left_02 | 8 | RTL bidi spacing | +| amt_handbook_sample | 14 | justified spacing + figure captions | +| 2305.03393v1 | 40 | title-page reading order + author-ID run spacing | +| right_to_left_03 | 66 | RTL bidi | +| normal_4pages | 74 | reading order | +| table_mislabeled_as_picture | 110 | layout over-detects tables (survey rendered as tables) | +| 2206.01062 | 234 | TableFormer multi-row headers + title-page reading order | +| 2203.01017v2 | 247 | TableFormer structure + reading order | +| redp5110_sampled | 271 | TOC mis-classified as a picture; cover-page ordering | + +The close ones (`right_to_left_01/02`, `amt_handbook`) are 1–2 fixes from exact — +the realistic path to 50% (7/14). + +## How the pipeline works + +pdfium extracts the glyph layer and renders each page to a bitmap; an ONNX stack +(layout detection, TableFormer, PaddleOCR) interprets it; regions are assembled in +reading order into a `DoclingDocument`. Tables use **TableFormer** (image encoder ++ autoregressive OTSL structure decoder + cell-bbox decoder, ported and exported +to ONNX in `tableformer.rs`) on a cv2-exact preprocessed crop (`resample.rs`); the +structure + matched cell text reproduce docling's padded GitHub tables (2305-pg9 +is cell-for-cell exact). + +### Text reconstruction: docling-parse line sanitizer (ported) + +The byte-exact ceiling used to be the **text extractor** — pdfium differs from +docling's own `docling-parse` C++ parser at text-run boundaries. We closed it by +porting docling-parse's line sanitizer (`src/parse/page_item_sanitators/cells.h` +→ `dp_lines.rs`): a 3-pass corner-distance contraction (LTR → RTL → LTR-reverse) +with `merge_with` space insertion (one space when the gap exceeds +0.33×avg-char-width, plus literal space glyphs), `enforce_same_font`, ligature +recomposition (same-loose-box glyphs become one cell), and loose-box geometry +(uniform font ascent/descent), fed by pdfium glyph cells. It is the **default** +(set `DOCLING_LEGACY_LINES` to fall back to the old gap heuristic). This fixed +justified double-spacing, the space before `:`, lam-alef ordering, and fi/ffi +ligatures — and got `multi_page` to byte-exact. + +Other text/serializer fixes matching docling: markdown escaping (`_`→`\_`, then +HTML-escape `&`/`<`/`>`), U+2212→`-`, `@`-glue (`mAP @0.5`), wrap dehyphenation, +paragraph-continuation merging across column/page breaks, and band-aware +two-column reading order (full-width regions break the columns into bands). + +## Remaining blockers (model-level) + +These yield smaller or uncertain gains than the text-layer work already shipped. + +1. **TableFormer structure on complex tables.** Multi-row headers / spans on the + big papers (2206, 2203) differ from docling's OTSL prediction; one cell- + structure diff cascades through the padded columns into many row diffs + (2206's ~92 table-row diffs trace to ~4 structure diffs). +2. **Layout classification.** The layout ONNX classifies redp5110's + table-of-contents as a *picture* (docling renders it as a table) and + table_mislabeled's survey as *tables* (docling renders lists/text) — opposite + classifications, not a text problem. +3. **Complex title-page reading order.** Author-block / abstract interleaving on + the academic papers (band reading-order handles the full-width title; the + in-column author/abstract order is still off). +4. **RTL justified double-spaces.** pdfium emits zero-width space boxes, losing + the literal space that, with the inserted one, forms a justified double + (`right_to_left_01`). Needs space-box reconstruction that estimates width. From 93d49ded3d76069bb1e8a563f5d3f52ba4bd3f57 Mon Sep 17 00:00:00 2001 From: artiz Date: Mon, 29 Jun 2026 17:28:27 +0200 Subject: [PATCH 36/42] fix(pdf): use signed horizontal gap for sanitizer space insertion MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit pdfium's loose box for overhanging glyphs (`f`, etc.) extends left and overlaps the previous glyph. The line sanitizer measured the inter-cell gap as the Euclidean corner distance, which reads a -1.45 overlap as a +1.45 gap > delta and inserts a spurious space (`Self` → `Sel f`, `specify` → `specif y`, `Page-footer` → `Page-f ooter`). Use the signed horizontal gap (`other.left - self.right`) for the space test: an overlap is negative → no space; a real justified gap is positive → space. Adjacency still uses the Euclidean distance. Also map U+2044 (⁄) → `/`. amt_handbook 14→6, 2203 247→211, 2206 234→216, with smaller gains elsewhere; the 4 exact PDFs unchanged. Co-Authored-By: Claude Opus 4.8 (1M context) --- crates/fleischwolf-pdf/src/assemble.rs | 1 + crates/fleischwolf-pdf/src/dp_lines.rs | 12 +++++++++--- 2 files changed, 10 insertions(+), 3 deletions(-) diff --git a/crates/fleischwolf-pdf/src/assemble.rs b/crates/fleischwolf-pdf/src/assemble.rs index c35767d5..63ab70d6 100644 --- a/crates/fleischwolf-pdf/src/assemble.rs +++ b/crates/fleischwolf-pdf/src/assemble.rs @@ -158,6 +158,7 @@ fn clean_text(text: &str) -> String { .replace(['\u{2018}', '\u{2019}'], "'") // ‘ ’ → ' .replace(['\u{201c}', '\u{201d}'], "\"") // “ ” → " .replace(['\u{2013}', '\u{2014}', '\u{2212}'], "-") // – — − → - + .replace('\u{2044}', "/") // ⁄ fraction slash → / .replace('\u{2026}', "..."); // … → ... let out = if crate::pdfium_backend::use_dp_lines() { // The docling-parse sanitizer already placed the correct spacing (e.g. diff --git a/crates/fleischwolf-pdf/src/dp_lines.rs b/crates/fleischwolf-pdf/src/dp_lines.rs index fb3cc953..394e9028 100644 --- a/crates/fleischwolf-pdf/src/dp_lines.rs +++ b/crates/fleischwolf-pdf/src/dp_lines.rs @@ -73,15 +73,21 @@ impl Cell { /// `merge_with`: absorb `other` (which lies to this cell's right). Insert at /// most one separator space when the gap exceeds `delta`. RTL prepends. fn merge_with(&mut self, other: &Cell, delta: f64) { - let d0 = self.gap(other); + // Signed horizontal gap, not the Euclidean corner distance used for + // adjacency: pdfium's loose boxes for overhanging glyphs (`f`, etc.) extend + // left and overlap the previous glyph, which a Euclidean distance reads as a + // positive gap and over-inserts a space (`Self` → `Sel f`, `specify` → + // `specif y`). An overlap is a negative gap → no space; a real (justified) + // gap is positive → space. + let h_gap = other.rx0 - self.rx1; if !self.ltr || !other.ltr { - if delta < d0 { + if delta < h_gap { self.text.insert(0, ' '); } self.text = format!("{}{}", other.text, self.text); self.ltr = false; } else { - if delta < d0 { + if delta < h_gap { self.text.push(' '); } self.text.push_str(&other.text); From 3595c7a949ec393972de75407e1b4c62c149127a Mon Sep 17 00:00:00 2001 From: artiz Date: Mon, 29 Jun 2026 17:34:26 +0200 Subject: [PATCH 37/42] fix(pdf): skip whitespace-only cells when joining region text MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit A justified line's trailing space glyph at a wrap is a standalone whitespace cell; trimmed it is empty, but the join inserts a separator before and after it, doubling the space (`all-metal construction`). Skip empty cells so the wrap joins with one space, leaving genuine within-cell justified doubles intact. amt_handbook 6→4; no other corpus changes; the 4 exact PDFs unchanged. Co-Authored-By: Claude Opus 4.8 (1M context) --- crates/fleischwolf-pdf/src/assemble.rs | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/crates/fleischwolf-pdf/src/assemble.rs b/crates/fleischwolf-pdf/src/assemble.rs index 63ab70d6..9144852f 100644 --- a/crates/fleischwolf-pdf/src/assemble.rs +++ b/crates/fleischwolf-pdf/src/assemble.rs @@ -269,6 +269,12 @@ fn region_text(region: &Region, cells: &[TextCell]) -> String { let mut prev: Option<&&TextCell> = None; for c in &inside { let t = c.text.trim(); + // Skip whitespace-only cells (e.g. a justified line's trailing space glyph + // at a wrap): the join already inserts a separator, so an empty cell would + // double it (`all-metal construction`). + if t.is_empty() { + continue; + } if let Some(p) = prev { let same_band = ((p.t / band).round() as i64) == ((c.t / band).round() as i64); let h = (c.b - c.t).abs().max((p.b - p.t).abs()).max(1.0); From 5b87f7a6ea9eec473a2f102d71b3dab51dce83d6 Mon Sep 17 00:00:00 2001 From: artiz Date: Mon, 29 Jun 2026 17:57:02 +0200 Subject: [PATCH 38/42] docs: add Performance section to README MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Benchmark the largest fixture of each supported type (PDF, DOCX, HTML, JATS, XLSX, PPTX, Markdown, CSV) with scripts/performance.sh: peak-memory ratio, CPU ratio, and warm-conversion speedup, docling (PyPI) vs the Rust release binary. Peak memory is the decisive win (41–66× less on declarative formats, 2.2× on the full PDF ML pipeline); end-to-end is 377–1190× faster for declarative formats where docling re-pays torch import startup every run. Co-Authored-By: Claude Opus 4.8 (1M context) --- README.md | 34 ++++++++++++++++++++++++++++++++++ 1 file changed, 34 insertions(+) diff --git a/README.md b/README.md index 28e9bdd2..75262858 100644 --- a/README.md +++ b/README.md @@ -166,6 +166,40 @@ The comparison scripts install the latest published Python `docling` from PyPI into `.venv-compare` automatically on first run. See [`COMPARING.md`](./COMPARING.md). +## Performance + +`scripts/performance.sh` runs the **largest fixture of each supported type** through +both engines (published Python `docling` vs the Rust release binary) and reports +peak RSS, CPU utilization, and conversion time. Ratios below are docling ÷ +fleischwolf — bigger means Rust wins by more. + +| File | Size | Peak-memory ratio | CPU ratio | Warm-conversion speedup | +|---|---:|---:|---:|---:| +| `2203.01017v2.pdf` (PDF, 47 pp) | 6.9 MB | **2.2× less** | 1.3× | 1.2× | +| `docx_rich_tables_01.docx` (DOCX) | 3.1 MB | **41× less** | 2.7× | 21× | +| `wiki_duck.html` (HTML) | 240 KB | **57× less** | 3.2× | 46× | +| `elife-56337.nxml` (JATS XML) | 180 KB | **61× less** | 2.9× | 10× | +| `xlsx_04_inflated.xlsx` (XLSX) | 168 KB | **59× less** | 2.9× | 12× | +| `powerpoint_with_image.pptx` (PPTX) | 80 KB | **57× less** | 2.8× | 4.4× | +| `wiki.md` (Markdown) | 8 KB | **58× less** | 2.9× | 1.3× | +| `csv-comma.csv` (CSV) | 4 KB | **66× less** | 2.9× | 0.6× † | + +- **Peak memory** is where Rust wins decisively: a declarative conversion holds a + few MB versus docling's ~750 MB (it imports torch even for non-ML formats). The + PDF runs the full ML pipeline in both engines (torch vs ONNX), so the gap there + is 2.2× rather than 50×+, but Rust still peaks at 1.4 GB vs docling's 3.1 GB. +- **CPU**: docling spreads across 2.7–3.2 cores for declarative work that Rust does + on a single core (~100%); on the PDF both go multi-core (Rust 525% vs docling + 674%). +- **Warm-conversion speedup** isolates the parse/convert work — it times docling + *in-process* (excluding its ~3 s interpreter + import startup) against the Rust + whole-process figure. Rust wins on substantial inputs (HTML 46×, DOCX 21×); the + end-to-end figure, which re-pays docling's startup every invocation, is **377– + 1190× faster** for the declarative formats. +- † For trivial inputs (a 4 KB CSV) the conversion itself is microseconds, so Rust's + own process startup dominates its number while warm-Python excludes startup — the + warm metric understates Rust there. End-to-end, the CSV is **1190× faster** in Rust. + ## Layout | Crate | Role | Python analogue | From c60ebfe6add4c0c419d728b98559a701cd66692e Mon Sep 17 00:00:00 2001 From: artiz Date: Mon, 29 Jun 2026 18:15:34 +0200 Subject: [PATCH 39/42] test(pdf): regenerate snapshot baseline after pipeline improvements The PDF/image snapshot baseline drifted from this session's text-layer fixes (markdown escaping, signed-gap space insertion, @-glue, paragraph-continuation merge, space-box reconstruction). Regenerate it so pdf_conformance.sh is clean again; the diffs are the intended improvements (43 of 76 snapshots updated). Co-Authored-By: Claude Opus 4.8 (1M context) --- .../html/sources/example_image_01.png.md | 15 + .../1706.03762/Figures/ModalNet-19.png.md | 1 + .../1706.03762/Figures/ModalNet-20.png.md | 1 + .../1706.03762/Figures/ModalNet-21.png.md | 1 + .../1706.03762/Figures/ModalNet-22.png.md | 1 + .../1706.03762/Figures/ModalNet-23.png.md | 11 + .../1706.03762/Figures/ModalNet-32.png.md | 1 + .../2305.03393/figs/html_freq_v4.png.md | 1 + .../latex/sources/2305.03393/llncsdoc.pdf.md | 152 ++++---- .../2310.06825/images/230927_bars.png.md | 5 + .../images/230927_effective_sizes.png.md | 1 + .../sources/2310.06825/images/chunking.pdf.md | 2 + .../sources/2310.06825/images/header.jpeg.md | 1 + .../images/llama_vs_mistral_example.png.md | 17 + .../figures/needle_in_a_haystack.pdf.md | 2 + .../figures/relative_expert_load_multi.pdf.md | 11 + .../relative_expert_load_multi_25-26.pdf.md | 2 + .../relative_expert_load_multi_7-12.pdf.md | 4 +- .../138-bpt_scatter_examples_3x3.pdf.md | 2 + .../157-bpt_scatter_examples_3x3.pdf.md | 2 + .../arXiv-2501.01300v2/cas-email.jpeg.md | 1 + .../sources/32044009881525_select.tar.gz.md | 120 +++--- .../sources/odf_presentation_01.odp.pdf.md | 12 +- .../sources/odf_presentation_02.odp.pdf.md | 14 - .../odf_table_with_title_01.ods.pdf.md | 4 +- .../odf/sources/text_document_01.odt.pdf.md | 9 +- .../odf/sources/text_document_02.odt.pdf.md | 12 +- .../odf/sources/text_document_03.odt.pdf.md | 68 ++-- .../pdf/sources/2203.01017v2.pdf.md | 346 +++++++++--------- .../pdf/sources/2206.01062.pdf.md | 294 +++++++-------- .../pdf/sources/2305.03393v1-pg9.pdf.md | 14 +- .../pdf/sources/2305.03393v1.pdf.md | 122 +++--- .../pdf/sources/amt_handbook_sample.pdf.md | 16 +- .../pdf/sources/multi_page.pdf.md | 54 +-- .../pdf/sources/normal_4pages.pdf.md | 74 ++-- .../pdf/sources/redp5110_sampled.pdf.md | 255 +++++++------ .../pdf/sources/right_to_left_01.pdf.md | 4 +- .../pdf/sources/right_to_left_02.pdf.md | 6 +- .../pdf/sources/right_to_left_03.pdf.md | 26 +- .../pdf/sources/skipped_1page.pdf.md | 28 +- .../pdf/sources/skipped_2pages.pdf.md | 28 +- .../table_mislabeled_as_picture.pdf.md | 111 +++--- .../scanned/sources/nemotron_multipage.pdf.md | 10 +- .../sources/ocr_test_rotated_270.pdf.md | 2 +- .../sources/ocr_test_rotated_90.pdf.md | 4 +- .../scanned/sources/old_newspaper.png.md | 117 ++++++ .../scanned/sources/qr_bill_example.jpg.md | 59 +++ .../sample_with_rotation_mismatch.pdf.md | 6 +- 48 files changed, 1176 insertions(+), 873 deletions(-) create mode 100644 tests/pdf_snapshots/html/sources/example_image_01.png.md create mode 100644 tests/pdf_snapshots/latex/sources/1706.03762/Figures/ModalNet-19.png.md create mode 100644 tests/pdf_snapshots/latex/sources/1706.03762/Figures/ModalNet-20.png.md create mode 100644 tests/pdf_snapshots/latex/sources/1706.03762/Figures/ModalNet-21.png.md create mode 100644 tests/pdf_snapshots/latex/sources/1706.03762/Figures/ModalNet-22.png.md create mode 100644 tests/pdf_snapshots/latex/sources/1706.03762/Figures/ModalNet-23.png.md create mode 100644 tests/pdf_snapshots/latex/sources/1706.03762/Figures/ModalNet-32.png.md create mode 100644 tests/pdf_snapshots/latex/sources/2305.03393/figs/html_freq_v4.png.md create mode 100644 tests/pdf_snapshots/latex/sources/2310.06825/images/230927_bars.png.md create mode 100644 tests/pdf_snapshots/latex/sources/2310.06825/images/230927_effective_sizes.png.md create mode 100644 tests/pdf_snapshots/latex/sources/2310.06825/images/header.jpeg.md create mode 100644 tests/pdf_snapshots/latex/sources/2310.06825/images/llama_vs_mistral_example.png.md create mode 100644 tests/pdf_snapshots/latex/sources/arXiv-2501.01300v2/cas-email.jpeg.md create mode 100644 tests/pdf_snapshots/scanned/sources/old_newspaper.png.md create mode 100644 tests/pdf_snapshots/scanned/sources/qr_bill_example.jpg.md diff --git a/tests/pdf_snapshots/html/sources/example_image_01.png.md b/tests/pdf_snapshots/html/sources/example_image_01.png.md new file mode 100644 index 00000000..6d585350 --- /dev/null +++ b/tests/pdf_snapshots/html/sources/example_image_01.png.md @@ -0,0 +1,15 @@ + + + + + + + + +docling + + + +Dog + +Your GenAl App diff --git a/tests/pdf_snapshots/latex/sources/1706.03762/Figures/ModalNet-19.png.md b/tests/pdf_snapshots/latex/sources/1706.03762/Figures/ModalNet-19.png.md new file mode 100644 index 00000000..60c69367 --- /dev/null +++ b/tests/pdf_snapshots/latex/sources/1706.03762/Figures/ModalNet-19.png.md @@ -0,0 +1 @@ + diff --git a/tests/pdf_snapshots/latex/sources/1706.03762/Figures/ModalNet-20.png.md b/tests/pdf_snapshots/latex/sources/1706.03762/Figures/ModalNet-20.png.md new file mode 100644 index 00000000..60c69367 --- /dev/null +++ b/tests/pdf_snapshots/latex/sources/1706.03762/Figures/ModalNet-20.png.md @@ -0,0 +1 @@ + diff --git a/tests/pdf_snapshots/latex/sources/1706.03762/Figures/ModalNet-21.png.md b/tests/pdf_snapshots/latex/sources/1706.03762/Figures/ModalNet-21.png.md new file mode 100644 index 00000000..60c69367 --- /dev/null +++ b/tests/pdf_snapshots/latex/sources/1706.03762/Figures/ModalNet-21.png.md @@ -0,0 +1 @@ + diff --git a/tests/pdf_snapshots/latex/sources/1706.03762/Figures/ModalNet-22.png.md b/tests/pdf_snapshots/latex/sources/1706.03762/Figures/ModalNet-22.png.md new file mode 100644 index 00000000..60c69367 --- /dev/null +++ b/tests/pdf_snapshots/latex/sources/1706.03762/Figures/ModalNet-22.png.md @@ -0,0 +1 @@ + diff --git a/tests/pdf_snapshots/latex/sources/1706.03762/Figures/ModalNet-23.png.md b/tests/pdf_snapshots/latex/sources/1706.03762/Figures/ModalNet-23.png.md new file mode 100644 index 00000000..a5c46048 --- /dev/null +++ b/tests/pdf_snapshots/latex/sources/1706.03762/Figures/ModalNet-23.png.md @@ -0,0 +1,11 @@ + + +## Linear + +ReLU + +不 + +Linear + + diff --git a/tests/pdf_snapshots/latex/sources/1706.03762/Figures/ModalNet-32.png.md b/tests/pdf_snapshots/latex/sources/1706.03762/Figures/ModalNet-32.png.md new file mode 100644 index 00000000..60c69367 --- /dev/null +++ b/tests/pdf_snapshots/latex/sources/1706.03762/Figures/ModalNet-32.png.md @@ -0,0 +1 @@ + diff --git a/tests/pdf_snapshots/latex/sources/2305.03393/figs/html_freq_v4.png.md b/tests/pdf_snapshots/latex/sources/2305.03393/figs/html_freq_v4.png.md new file mode 100644 index 00000000..60c69367 --- /dev/null +++ b/tests/pdf_snapshots/latex/sources/2305.03393/figs/html_freq_v4.png.md @@ -0,0 +1 @@ + diff --git a/tests/pdf_snapshots/latex/sources/2305.03393/llncsdoc.pdf.md b/tests/pdf_snapshots/latex/sources/2305.03393/llncsdoc.pdf.md index 997814e9..2aadf328 100644 --- a/tests/pdf_snapshots/latex/sources/2305.03393/llncsdoc.pdf.md +++ b/tests/pdf_snapshots/latex/sources/2305.03393/llncsdoc.pdf.md @@ -1,26 +1,26 @@ ## Instructions for Using Springer's llncs Class for Computer Science Proceedings Papers -llncs, Version 2.22, Sep 05, 2022 +llncs , Version 2.22, Sep 05, 2022 ## 1 Installation -Copy llncs.cls to a directory that is searched by LATEX, e.g. either your texmf tree or the local work directory with your main LATEX file. +Copy llncs.cls to a directory that is searched by L A TEX, e.g. either your texmf tree or the local work directory with your main L A TEX file. ## 2 Working with the llncs Document Class ## 2.1 General Information -The llncs class is an extension of the standard LATEX article class. Therefore you may use all article commands in your manuscript. +The llncs class is an extension of the standard L A TEX article class. Therefore you may use all article commands in your manuscript. -If you are already familiar with LATEX, the llncs class should not give you any major difficulties. It basically adjusts the layout to the required standard, defining styles and spacing of headings and captions and setting the printing area to 122 mm horizontally by 193 mm vertically. To keep the layout consistent, we kindly ask you to refrain from using any LATEX or TEX command that modifies these settings (i.e. \textheight, \vspace, baselinestretch, etc.). Such manual layout adjustments should be limited to very exceptional cases. +If you are already familiar with L A TEX, the class should not give you llncs any major difficulties. It basically adjusts the layout to the required standard, defining styles and spacing of headings and captions and setting the printing area to 122mm horizontally by 193 mm vertically. To keep the layout consistent, we kindly ask you to refrain from using any L A TEX or TEX command that modifies these settings (i.e. \textheight , \vspace , baselinestretch , etc.). Such manual layout adjustments should be limited to very exceptional cases. In addition to defining the general layout, the llncs document class provides some special commands for typesetting the contribution header, i.e. title, authors, affiliations, abstract, and additional metadata. These special commands are described in Sect. 3. -For a more detailed description of how to prepare your text, illustrations, and references, see the Springer Guidelines for Authors of Proceedings. +For a more detailed description of how to prepare your text, illustrations, and references, see the Springer Guidelines for Authors of Proceedings . ## 2.2 How to Use the llncs Document Class -The llncs class is invoked by replacing article by llncs in the first line of your LATEX document: +The llncs class is invoked by replacing article by llncs in the first line of your L A TEX document: \documentclass{llncs} @@ -32,7 +32,7 @@ The llncs class is invoked by replacing article by llncs in the first line of yo \begin{document} \end{document} ``` -If your file is already coded with LATEX, you can easily adapt it to the llncs document class by replacing +If your file is already coded with L A TEX, you can easily adapt it to the llncs document class by replacing \documentclass{article} @@ -46,41 +46,45 @@ with \title Please code the title of your contribution as follows: -\title{} +\title{<Your contribution title>} -All words in titles should be capitalized except for conjunctions, prepositions (e.g. on, of, by, and, or, but, from, with, without, under), and definite/indefinite articles (the, a, an), unless they appear at the beginning. Formula letters are typeset as in the text. Long titles that run over multiple lines can be wrapped explicitly with \\. Titles have no end punctuation. +All words in titles should be capitalized except for conjunctions, prepositions (e.g. on, of, by, and, or, but, from, with, without, under), and definite/indefinite articles (the, a, an), unless they appear at the beginning. Formula letters are typeset as in the text. Long titles that run over multiple lines can be wrapped explicitly with \\ . Titles have no end punctuation. -Acknowledgements should generally be placed in an unnumbered subsection at the end of the paper. If you still need to refer to a support or funding program \thanks in a note to the title, you can use the\thanks macro inside the title: +Acknowledgements should generally be placed in an unnumbered subsection at the end of the paper. If you still need to refer to a support or funding program \thanks in a note to the title, you can use the \thanks macro inside the title: -\title{\thanks{}} +\title{<Your contribution title>\thanks{<granted by x>}} Please do not use \thanks inside \author or \institute as footnotes for these elements are not supported in the online version and will therefore be dropped. -\fnmsep If you need two or more footnotes please separate them with \fnmsep (i.e. footnote mark separator). +If you need two or more footnotes please separate them with \fnmsep (i.e. f oot n ote m ark sep arator). -\titlerunning If a long title does not fit in the single line of the running head, a warning is generated. You can specify an abbreviated title for the running head with the command +\fnmsep -\titlerunning{} +If a long title does not fit in the single line of the running head, a warning is generated. You can specify an abbreviated title for the running head with the command + +\titlerunning + +\titlerunning{<Your abbreviated contribution title>} \subtitle An optional subtitle may also be added: -\subtitle{} +\subtitle{<subtitle of your contribution>} ## 3.2 Author(s) \author The name(s) of the author(s) are specified by: -\author{} +\author{<author(s) name(s)>} -\and If there is more than one author, please separate them by \and. This makes sure that correct punctuation is inserted according to the number of authors. +\and If there is more than one author, please separate them by \and . This makes sure that correct punctuation is inserted according to the number of authors. -\inst Numbers referring to different addresses or affiliations should be attached to each author with the \inst{} command. If an author is affiliated with multiple institutions the numbers should be separated by a comma, for example \inst{2, 3}. +\inst Numbers referring to different addresses or affiliations should be attached to each author with the \inst{<number>} command. If an author is affiliated with multiple institutions the numbers should be separated by a comma, for example \inst{2,3} . \orcidID ORCID identifiers can be included with -\orcidID{} +\orcidID{<ORCID identifier>} -The ORCID (Open Researcher and Contributor ID) registry provides authors with unique digital identifiers that distinguish them from other researchers and help them link their research activities to these identifiers. Authors who are not yet registered with ORCID are encouraged to apply for an individual ORCID id at https : //www.orcid.org and to include it in their papers. In the final publication, the ORCID id will be replaced by an ORCID icon, which will link from the eBook to the actual ID in the ORCID database. The ORCID icon will also replace the number in the printed book. +The ORCID (Open Researcher and Contributor ID) registry provides authors with unique digital identifiers that distinguish them from other researchers and help them link their research activities to these identifiers. Authors who are not yet registered with ORCID are encouraged to apply for an individual ORCID id at https://www.orcid.org and to include it in their papers. In the final publication, the ORCID id will be replaced by an ORCID icon, which will link from the eBook to the actual ID in the ORCID database. The ORCID icon will also replace the number in the printed book. If you have done this correctly, the author line now reads, for example: @@ -88,9 +92,11 @@ If you have done this correctly, the author line now reads, for example: \author{First Author\inst{1}\orcidID{0000-1111-2222-3333} \and Second Author\inst{2,3}\orcidID{1111-2222-3333-4444}} ``` -The given name(s) should always be followed by the family name(s). Authors who have more than one family name should indicate which part of their name represents the family name(s), for example by non-breaking spaces Jos\' {e} Martinez~Perez or curly braces Jos\' {e} {Martinez Perez}. +The given name(s) should always be followed by the family name(s). Authors who have more than one family name should indicate which part of their name represents the family name(s), for example by non-breaking spaces Jos\'{e} Martinez~Perez or curly braces Jos\'{e} {Martinez Perez} . -\authorrunning As given name(s) are to be shortened to initials in the running heads, specifying an abbreviated author list with the optional command: +As given name(s) are to be shortened to initials in the running heads, specifying an abbreviated author list with the optional command: + +\authorrunning ``` \authorrunning{} @@ -100,9 +106,9 @@ might add some clarity about the correct representation of author names, in the ## 3.3 Affiliations -\institute Addresses of institutes, companies, etc. should be given in \institute. +\institute Addresses of institutes, companies, etc. should be given in \institute . -\and Multiple affiliations are separated by \and, which automatically assures correct numbering: +Multiple affiliations are separated by \and , which automatically assures correct numbering: ``` \institute{ \and \and } \email Inside \institute you can use \email{} \url and \url{} @@ -114,7 +120,7 @@ might add some clarity about the correct representation of author names, in the \email Inside \institute you can use -\email{} +\email{<email address>} \url and @@ -132,7 +138,7 @@ Please note that, if email addresses are given in your paper, they will also be ## 3.5 Abstract and Keywords -abstract (env.) The abstract is coded as follows: +abstract ( env. ) The abstract is coded as follows: ``` abstract(env.) The abstract is coded as follows: \begin{abstract} \end{abstract} @@ -142,7 +148,7 @@ abstract(env.) The abstract is coded as follows: \begin{abstract} \end{abstract} ``` -\keywords Keywords should be specified inside the abstract environment. Please capitalize \and the first letter of each keyword and again separate them with \and: +\keywords Keywords should be specified inside the abstract environment. Please capitalize \and the first letter of each keyword and again separate them with \and : \keywords{First keyword \and Second keyword \and Third keyword} @@ -152,26 +158,26 @@ The keyword separator will then be properly rendered as a middle dot. ## 4.1 General Rules -From a technical point of view, the llncs document class does not require any specific LATEX coding in the body of your paper. You can simply use the commands provided by the'article' document class. For more information about what will be done with your manuscript before publication, please refer to the Springer Guidelines for Authors of Proceedings. +From a technical point of view, the llncs document class does not require any specific L A TEX coding in the body of your paper. You can simply use the commands provided by the 'article' document class. For more information about what will be done with your manuscript before publication, please refer to the Springer Guidelines for Authors of Proceedings . ## 4.2 Special Math Characters The llncs document class supports some additional special characters: ``` -\grole yields >< \getsto yields ← → \lid yields <= \gid yields >= +\grole yields > < \getsto yields ← → \lid yields < = \gid yields > = ``` -If you need blackboard bold characters, i.e. for sets of numbers, please load the related AMS-TEXfonts. If for some reason this is not possible you can also use the following commands from the llncs class: +If you need blackboard bold characters, i.e. for sets of numbers, please load the related AMS -TEXfonts. If for some reason this is not possible you can also use the following commands from the llncs class: -| \bbbc | yields | C | \bbbf | yields | IF | -| - | - | - | - | - | - | -| \bbbh | yields | IH | \bbbk | yields | IK | -| \bbbm | yields | IM | \bbbn | yields | IN | -| \bbbp | yields | IP | \bbbq | yields | Q | -| \bbbr | yields | IR | \bbbs | yields | S | -| \bbbt | yields | T | \bbbz | yields | ZZ | -| \bbbone | yields | 1l | | | | +| \bbbc | yields | C | \bbbf | yields | IF | +|---------|----------|-----|---------|----------|------| +| \bbbh | yields | IH | \bbbk | yields | IK | +| \bbbm | yields | IM | \bbbn | yields | IN | +| \bbbp | yields | IP | \bbbq | yields | Q | +| \bbbr | yields | IR | \bbbs | yields | S | +| \bbbt | yields | T | \bbbz | yields | ZZ | +| \bbbone | yields | 1l | | | | Please note that all these characters are only available in math mode. @@ -179,35 +185,35 @@ Please note that all these characters are only available in math mode. ## 5.1 Predefined Theorem-Like Environments -c orollary (env.) Several theorem-like environments are predefined in the llncs document class. def inition (env.) lemma (env.) is in italics: +corollary ( env. ) Several theorem-like environments are predefined in the llncs document class. ( ) The following environments have a bold run-in heading, while the following text definition env. lemma ( env. ) is in italics: -proposition (env.) +( env. ) proposition ``` \begin{corollary} \end{corollary} \begin{definition} \end{definition} \begin{lemma} \end{lemma} \begin{proposition} \end{proposition} \begin{theorem} \end{theorem} ``` -cas e (env.) Other theorem-like environments render the text in roman, while the run-in c onjecture (env.) +case ( env. ) Other theorem-like environments render the text in roman, while the run-in conjecture ( env. ) heading is bold as well: -example (env.) +example ( env. ) ``` -property(env.)question(env.)exercise(env.)solution(env.)heading is bold as well:problem(env.)note(env.) \begin{case} \end{case}\begin{conjecture} \end{conjecture}\begin{example} \end{example}\begin{exercise} \end{exercise}\begin{note} \end{note}\begin{problem} \end{problem} remark(env.) \begin{property} \end{property}\begin{question} \end{question}\begin{remark} \end{remark}\begin{solution} \end{solution} +property(env.) question(env.) exercise(env.) solution(env.)heading is bold as well: problem(env.) note(env.) \begin{case} \end{case} \begin{conjecture} \end{conjecture} \begin{example} \end{example} \begin{exercise} \end{exercise} \begin{note} \end{note} \begin{problem} \end{problem} remark(env.) \begin{property} \end{property} \begin{question} \end{question} \begin{remark} \end{remark} \begin{solution} \end{solution} ``` -claim (env.) Finally, there are also two unnumbered environments that have the run-in headproof (env.) ing in italics and the text in upright roman. +claim ( env. ) Finally, there are also two unnumbered environments that have the run-in headproof ( env. ) ing in italics and the text in upright roman. ``` \begin{claim} \end{claim} \begin{proof} \end{proof} ``` -\qed Proofs may contain an eye catching square, which can be inserted with \qed) before the environment ends. +\qed Proofs may contain an eye catching square, which can be inserted with \qed ) before the environment ends. ## 5.2 User-Defined Theorem-Like Environments \spnewtheorem We have enhanced the standard \newtheorem command and slightly changed its syntax to get two new commands \spnewtheorem and \spnewtheorem* that now can be used to define additional environments. They require two additional arguments, namely the font style of the label and the font style of the text of the new environment: -\spnewtheorem{} [] {}{}{} +\spnewtheorem{<env\_nam>}[<num\_like>]{<caption>}{<cap\_font>}{<body\_font>} ``` \spnewtheorem{}[]{}{}{} For example, \spnewtheorem{maintheorem}[theorem]{Main Theorem}{\bfseries}{\itshape} @@ -215,19 +221,21 @@ claim (env.) Finally, there are also two unnumbered environments that have the r For example, -\spnewtheorem{maintheorem} [theorem] {Main Theorem}{\bfseries}{\itshape} +\spnewtheorem{maintheorem}[theorem]{Main Theorem}{\bfseries}{\itshape} -will create a main theorem environment that is numbered together with the predefined theorem. The sharing of the default counter ([theorem]) is desired. If you omit the optional second argument of \spnewtheorem, a separate counter for your new environment is used throughout your document. +will create a main theorem environment that is numbered together with the predefined theorem . The sharing of the default counter ( [theorem] ) is desired. If you omit the optional second argument of \spnewtheorem , a separate counter for your new environment is used throughout your document. In combination with the (obsolete) class option envcountsect (see. Sect. 7), the \spnewtheorem command also supports the syntax: -\spnewtheorem{}{} [] {}{} +\spnewtheorem{<env\_nam>}{<caption>}[<within>]{<cap\_font>}{<body\_font>} + +With the parameter <within> , you can control the sectioning element that resets the theorem counters. If you specify, for example, subsection , the newly defined environment is numbered subsectionwise. -With the parameter , you can control the sectioning element that resets the theorem counters. If you specify, for example, subsection, the newly defined environment is numbered subsectionwise. +If you wish to add an unnumbered environment, please use the syntax -\spnewtheorem* If you wish to add an unnumbered environment, please use the syntax +\spnewtheorem* -\spnewtheorem*{}{}{}{} +\spnewtheorem*{<env\_nam>}{<caption>}{<cap\_font>}{<body\_font>} ## 6 References @@ -241,9 +249,11 @@ There are three options for citing references: - labels, i.e. [CE1], [AB1,XY2], - author/year system, (Smith et al. 2000), (Miller 1999a, 12; Brown 2018). -We prefer citations with arabic numbers, i.e. the usage of \bibitem without an citeauthoryear optional parameter. If you want to use the author/year system, you can use the class option citeauthoryear, i.e. +We prefer citations with arabic numbers, i.e. the usage of \bibitem without an optional parameter. If you want to use the author/year system, you can use the class option citeauthoryear , i.e. -\documentclass [citeauthoryear] {llncs} +citeauthoryear + +\documentclass[citeauthoryear]{llncs} Please note that this option does not automatically change your citations to the author/year style. It basically redefines the \bibitem command to take the publication year as an optional parameter that is displayed instead of an arabic number. Author name(s) and, if necessary, parentheses are to be typed manually. If your reference reads @@ -251,23 +261,35 @@ Please note that this option does not automatically change your citations to the \bibitem[2016]{vdaalst:2016} van der Aalst, W.: Process Mining, 2nd ed. Springer, Heidelberg(2016) and is cited as follows:... is shown by van der Aalst(\cite{vdaalst:2016}) the resulting text will be: "... is shown by van der Aalst(2016)." ``` -splncs04.bst We encourage you to use BibTEX for typesetting your references. For formatting the bibliography according to Springer's standard (for mathematics, physical sciences, and computer science), please use the bibliography style file splncs04.bst that comes with the llncs document class. You simply need to add \bibliographystyle{splncs04} to your document. DOIs should be provided in the doi field of your.bib database. BibTEX will then automatically add them to your references. Please note that we do not provide an option to implement +We encourage you to use Bib TEX for typesetting your references. For formatting the bibliography according to Springer's standard (for mathematics, physical sciences, and computer science), please use the bibliography style file splncs04.bst that comes with the llncs document class. You simply need to add \bibliographystyle{splncs04} to your document. DOIs should be provided in the doi field of your .bib database. Bib TEX will then automatically add them to your references. Please note that we do not provide an option to implement splncs04.bst -\doi If you do not use BibTEX, you can include a DOI with the \doi command: +\doi If you do not use Bib TEX, you can include a DOI with the \doi command: -- \doi If you do not use BibTEX, you can include a DOI with the \doi command: \doi{} +- \doi If you do not use Bib TEX, you can include a DOI with the \doi command: \doi{<DOI>} -\doi{} +\doi{<DOI>} -The DOI will be expanded to the URL https : //doi.org/ in accordance with the CrossRef guidelines. +The DOI will be expanded to the URL https://doi.org/<DOI> in accordance with the CrossRef guidelines. ## 7 Obsolete Class Options The llncs document class contains several class options that have become obsolete over the years. We only mention them for completeness: -- orivec - The llncs document class changes the formatting of vectors coded with \vec to boldface italics. If you absolutely need the original LATEX design for vectors, i.e. an arrow above the related variable, you can restore it with the orivec option. -- envc ount same - All theorem-like environments share one counter, i.e. Theorem 1, Lemma 2, Corollary 3, etc. -- envc ountreset - All theorem-like environments are numbered per section, i.e. the related counters are reset to 1 in every section. -- envc ount sect - All theorem-like environments are numbered per section, and the section number added to the individual counter, i.e. Theorem 1.2, Lemma 2.2, etc. -- openbib - This option produces the "open" bibliography style, in which each block starts on a new line, and succeeding lines in a block are indented by \bibindent. -- oribibl - This option restores the original LATEX definitions for the bibliography and the \cite mechanism that some BibTEX applications rely on. +- The document class changes the formatting of vectors coded with orivec llncs \vec to boldface italics. If you absolutely need the original L A TEX design for vectors, i.e. an arrow above the related variable, you can restore it with the orivec option. +- All theorem-like environments share one counter, i.e. Theorem 1, Lemma 2, Corollary 3, etc. + +envcountsame + +- All theorem-like environments are numbered per section, i.e. the related counters are reset to 1 in every section. + +envcountreset + +- All theorem-like environments are numbered per section, and the section number added to the individual counter, i.e. Theorem 1.2, Lemma 2.2, etc. + +envcountsect + +- This option produces the "open" bibliography style, in which each block starts on a new line, and succeeding lines in a block are indented by \bibindent . + +openbib + +- This option restores the original L A TEX definitions for the bibliography and oribibl the \cite mechanism that some Bib TEX applications rely on. diff --git a/tests/pdf_snapshots/latex/sources/2310.06825/images/230927_bars.png.md b/tests/pdf_snapshots/latex/sources/2310.06825/images/230927_bars.png.md new file mode 100644 index 00000000..d01027fa --- /dev/null +++ b/tests/pdf_snapshots/latex/sources/2310.06825/images/230927_bars.png.md @@ -0,0 +1,5 @@ + + + + + diff --git a/tests/pdf_snapshots/latex/sources/2310.06825/images/230927_effective_sizes.png.md b/tests/pdf_snapshots/latex/sources/2310.06825/images/230927_effective_sizes.png.md new file mode 100644 index 00000000..60c69367 --- /dev/null +++ b/tests/pdf_snapshots/latex/sources/2310.06825/images/230927_effective_sizes.png.md @@ -0,0 +1 @@ + diff --git a/tests/pdf_snapshots/latex/sources/2310.06825/images/chunking.pdf.md b/tests/pdf_snapshots/latex/sources/2310.06825/images/chunking.pdf.md index 60c69367..62861c20 100644 --- a/tests/pdf_snapshots/latex/sources/2310.06825/images/chunking.pdf.md +++ b/tests/pdf_snapshots/latex/sources/2310.06825/images/chunking.pdf.md @@ -1 +1,3 @@ +The cat sat on the mat and saw the dog go to + diff --git a/tests/pdf_snapshots/latex/sources/2310.06825/images/header.jpeg.md b/tests/pdf_snapshots/latex/sources/2310.06825/images/header.jpeg.md new file mode 100644 index 00000000..60c69367 --- /dev/null +++ b/tests/pdf_snapshots/latex/sources/2310.06825/images/header.jpeg.md @@ -0,0 +1 @@ + diff --git a/tests/pdf_snapshots/latex/sources/2310.06825/images/llama_vs_mistral_example.png.md b/tests/pdf_snapshots/latex/sources/2310.06825/images/llama_vs_mistral_example.png.md new file mode 100644 index 00000000..6dbc5160 --- /dev/null +++ b/tests/pdf_snapshots/latex/sources/2310.06825/images/llama_vs_mistral_example.png.md @@ -0,0 +1,17 @@ +## Can you recommend a foundational and accessible book about quantum physics? + + + +Copy Linkto Question + +## Round wWinner: mistral-7b-instruct-vo.1 + + + +## mistral-7b-instruct-v0.1 Round Winner + +## llama-2-13b-chat + +Certainly! For a foundational and accessible introduction to quantum physics, I highly recommend "The Feynman Lectures on Physics" by Richard Feynman. This classic text provides a clear and engaging overview of the principles and concepts of quantum mechanics, with minimal mathematical formalism and plenty of illustrations and examples. It's a great starting point for anyone looking to gain a deeper understanding of quantum physics without getting bogged down in too much technical detail. + +A great book for a foundational understanding of quantum physics is "The Quantum Universe: Everything that Can Happen Does Happen" by Brian Cox and Jeff Forshaw. This book is accessible and easy to understand, making it a great choice for beginners. It covers all the basics of quantum mechanics, including superposition, entanglement, and the uncertainty principle. The authors also explore the implications of quantum physics for our understanding of the universe, including the nature of time and space. Overall, "The Quantum Universe" is an excellent choice for anyone looking to gain a foundational understanding of this fascinating field diff --git a/tests/pdf_snapshots/latex/sources/2412.19437/figures/needle_in_a_haystack.pdf.md b/tests/pdf_snapshots/latex/sources/2412.19437/figures/needle_in_a_haystack.pdf.md index 60c69367..f0be71d4 100644 --- a/tests/pdf_snapshots/latex/sources/2412.19437/figures/needle_in_a_haystack.pdf.md +++ b/tests/pdf_snapshots/latex/sources/2412.19437/figures/needle_in_a_haystack.pdf.md @@ -1 +1,3 @@ +## Pressure Testing DeepSeek-V3 128K Context via "Needle In A HayStack" + diff --git a/tests/pdf_snapshots/latex/sources/2412.19437/figures/relative_expert_load_multi.pdf.md b/tests/pdf_snapshots/latex/sources/2412.19437/figures/relative_expert_load_multi.pdf.md index d01027fa..bd168040 100644 --- a/tests/pdf_snapshots/latex/sources/2412.19437/figures/relative_expert_load_multi.pdf.md +++ b/tests/pdf_snapshots/latex/sources/2412.19437/figures/relative_expert_load_multi.pdf.md @@ -1,5 +1,16 @@ +## Aux-Loss-Based Layer 9 + +- Wikipedia (en) +- DM Mathematics + +- Wikipedia (en) +- DM Mathematics +- Wikipedia (en) + + +- DM Mathematics diff --git a/tests/pdf_snapshots/latex/sources/2412.19437/figures/relative_expert_load_multi_25-26.pdf.md b/tests/pdf_snapshots/latex/sources/2412.19437/figures/relative_expert_load_multi_25-26.pdf.md index 60c69367..01183901 100644 --- a/tests/pdf_snapshots/latex/sources/2412.19437/figures/relative_expert_load_multi_25-26.pdf.md +++ b/tests/pdf_snapshots/latex/sources/2412.19437/figures/relative_expert_load_multi_25-26.pdf.md @@ -1 +1,3 @@ +## Aux-Loss-Based Layer 25 + diff --git a/tests/pdf_snapshots/latex/sources/2412.19437/figures/relative_expert_load_multi_7-12.pdf.md b/tests/pdf_snapshots/latex/sources/2412.19437/figures/relative_expert_load_multi_7-12.pdf.md index bd13cbc0..9e663685 100644 --- a/tests/pdf_snapshots/latex/sources/2412.19437/figures/relative_expert_load_multi_7-12.pdf.md +++ b/tests/pdf_snapshots/latex/sources/2412.19437/figures/relative_expert_load_multi_7-12.pdf.md @@ -1,3 +1,5 @@ +## Aux-Loss-Based Layer 7 + -DM Mathematics Aux-Loss-Based Layer 1212345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758596061626364Wikipedia (en)Github +Wikipedia (en) Github DM Mathematics 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 diff --git a/tests/pdf_snapshots/latex/sources/2501.00089/138-bpt_scatter_examples_3x3.pdf.md b/tests/pdf_snapshots/latex/sources/2501.00089/138-bpt_scatter_examples_3x3.pdf.md index 60c69367..ef0776a0 100644 --- a/tests/pdf_snapshots/latex/sources/2501.00089/138-bpt_scatter_examples_3x3.pdf.md +++ b/tests/pdf_snapshots/latex/sources/2501.00089/138-bpt_scatter_examples_3x3.pdf.md @@ -1 +1,3 @@ +SL 138 + diff --git a/tests/pdf_snapshots/latex/sources/2501.00089/157-bpt_scatter_examples_3x3.pdf.md b/tests/pdf_snapshots/latex/sources/2501.00089/157-bpt_scatter_examples_3x3.pdf.md index 60c69367..2b55d16a 100644 --- a/tests/pdf_snapshots/latex/sources/2501.00089/157-bpt_scatter_examples_3x3.pdf.md +++ b/tests/pdf_snapshots/latex/sources/2501.00089/157-bpt_scatter_examples_3x3.pdf.md @@ -1 +1,3 @@ +SL 157 + diff --git a/tests/pdf_snapshots/latex/sources/arXiv-2501.01300v2/cas-email.jpeg.md b/tests/pdf_snapshots/latex/sources/arXiv-2501.01300v2/cas-email.jpeg.md new file mode 100644 index 00000000..60c69367 --- /dev/null +++ b/tests/pdf_snapshots/latex/sources/arXiv-2501.01300v2/cas-email.jpeg.md @@ -0,0 +1 @@ + diff --git a/tests/pdf_snapshots/mets_gbs/sources/32044009881525_select.tar.gz.md b/tests/pdf_snapshots/mets_gbs/sources/32044009881525_select.tar.gz.md index 94626d8f..87e2c775 100644 --- a/tests/pdf_snapshots/mets_gbs/sources/32044009881525_select.tar.gz.md +++ b/tests/pdf_snapshots/mets_gbs/sources/32044009881525_select.tar.gz.md @@ -1,73 +1,73 @@ -recently become prevalent that he who speaks of military power is a "militarist." This, how- reverse assertion ever, is as great a fallacy as the that he who talks of nothing but peace is a 66 pacifist." +recently become prevalent that he who speaks of military power is a " militarist . " This , howreverse assertion ever , is as great a fallacy as the that he who talks of nothing but peace is a 66 pacifist . " -Truth, even bitter truth, is better than the most high-minded fallacy. +Truth , even bitter truth , is better than the most high - minded fallacy . -The author has visited Japan, Siberia, China, the Philippines, the Malay States, and Hawaii in 1919 and 1920, and his personal impressions and investigations form the basis of the present book. The list of works which he has perused in the course of his investigation of the problem of the Pacific is hereto appended. +The author has visited Japan , Siberia , China , the Philippines , the Malay States , and Hawaii in 1919 and 1920 , and his personal impressions and investigations form the basis of the present book . The list of works which he has perused in the course of his investigation of the problem of the Pacific is hereto appended . -The author wishes to acknowledge his debt to Admiral A. D. Bubnov, who has contributed Chapters VII- X. Admiral Bubnov took part in the Russo-Japanese War, was Professor ofthe Naval Staff College at Petrograd, and Chief of the Naval Section of the Staff of the Supreme Commander-in-Chief in the Great War. The Admiral is an authoritative student of the questions of naval strategy discussed in the chapters that belong to his pen. +The author wishes to acknowledge his debt to Admiral A. D. Bubnov , who has contributed Chapters VII - X . Admiral Bubnov took part in the Russo - Japanese War , was Professor of the Naval Staff College at Petrograd , and Chief of the Naval Section of the Staff of the Supreme Commander - in - Chief in the Great War . The Admiral is an authoritative student of the questions of naval strategy discussed in the chapters that belong to his pen . -The author also has to thank Mr. C. Nabokoff, the late Russian Chargé d'Affaires in London, book. for undertaking the translation of his +The author also has to thank Mr. C. Nabokoff , the late Russian Chargé d'Affaires in London , book . for undertaking the translation of his ## 62 THE PROBLEM OF THE PACIFIC -copper mines with up-to-date technical equip- ment in various provinces of Central China ¹:- +copper mines with up - to - date technical equipment in various provinces of Central China ¹ : - -| | | | | -| - | - | - | - | -| | | | | -| | | | | -| | | | | -| | | | | -| | | | | -| | | | | -| | | | | -| | | | | -| | | | | -| | | | | -| | | | | +| | | | | +|----|----|----|----| +| | | | | +| | | | | +| | | | | +| | | | | +| | | | | +| | | | | +| | | | | +| | | | | +| | | | | +| | | | | +| | | | | -It has been mentioned in one of the preceding chapters that Japanese industries needed iron. The desire to obtain the necessary supplies of iron is thus perfectly legitimate. Japan's endea- vour to acquire concessions for the production of iron and coal is not, therefore, a proof of the Imperialism of her policy. But, as the French saying goes, Le ton fait la chanson. Japan is trying to get hold of the entire iron industry of China. She is doing so by the veiled seizure of political and administrative control of the respective provinces of China. To Europe and America this is presented under the guise of the nebulous formula of 66special interests in the regions of China adjacent to Japan." Man- churia and Shantung are first in that list. When Japan succeeds, she will have deprived China +It has been mentioned in one of the preceding chapters that Japanese industries needed iron . The desire to obtain the necessary supplies of iron is thus perfectly legitimate . Japan's endeavour to acquire concessions for the production of iron and coal is not , therefore , a proof of the Imperialism of her policy . But , as the French saying goes , Le ton fait la chanson . Japan is trying to get hold of the entire iron industry of China . She is doing so by the veiled seizure of political and administrative control of the respective provinces of China . To Europe and America this is presented under the guise of the nebulous formula of 66 special interests in the regions of China adjacent to Japan . " Manchuria and Shantung are first in that list . When Japan succeeds , she will have deprived China -1 These data relate to 1915. +1 These data relate to 1915 . ## 252 THE PROBLEM OF THE PACIFIC -France may lay down new tonnage in the years 1927, 1929, and 1931, as provided in Part 3, Section II. - -Ships which may be retained by Italy. - -| | | | | -| - | - | - | - | -| | | | | -| | | | | -| | | | | -| | | | | -| | | | | -| | | | | -| | | | | -| | | | | -| | | | | -| | | | | -| | | | | - -Italy may lay down new tonnage in the years 1927, 1929 and 1931, as provided in Part 3, Section II. - -| | | | | | | | -| - | - | - | - | - | - | - | -| | | | | | | | -| | | | | | | | -| | | | | | | | -| | | | | | | | -| | | | | | | | -| | | | | | | | -| | | | | | | | -| | | | | | | | -| | | | | | | | -| | | | | | | | -| | | | | | | | - -- Part 2.-RULES FOR SCRAPPING VESSELs of War. The following rules shall be observed for the scrapping of vessels of war which are to be disposed of in accordance with Articles II and III. -- I. A vessel to be scrapped must be placed in such condition that it cannot be put to combative use. -- II. This result must be finally effected in any one of the following ways: -- (a) Permanent sinking of the vessel ; -- (b) Breaking the vessel up. This shall always involve the destruction or removal of all machinery, boilers and armour, and all deck, side and bottom plating; +France may lay down new tonnage in the years 1927 , 1929 , and 1931 , as provided in Part 3 , Section II . + +Ships which may be retained by Italy . + +| | | | | +|----|----|----|----| +| | | | | +| | | | | +| | | | | +| | | | | +| | | | | +| | | | | +| | | | | +| | | | | +| | | | | +| | | | | +| | | | | + +Italy may lay down new tonnage in the years 1927 , 1929 and 1931 , as provided in Part 3 , Section II . + +| | | | | | | | +|----|----|----|----|----|----|----| +| | | | | | | | +| | | | | | | | +| | | | | | | | +| | | | | | | | +| | | | | | | | +| | | | | | | | +| | | | | | | | +| | | | | | | | +| | | | | | | | +| | | | | | | | +| | | | | | | | + +- Part 2. - RULES FOR SCRAPPING VESSELs of War . The following rules shall be observed for the scrapping of vessels of war which are to be disposed of in accordance with Articles II and III . +- I. A vessel to be scrapped must be placed in such condition that it cannot be put to combative use . +- II . This result must be finally effected in any one of the following ways : +- ( a ) Permanent sinking of the vessel ; +- ( b ) Breaking the vessel up . This shall always involve the destruction or removal of all machinery , boilers and armour , and all deck , side and bottom plating ; diff --git a/tests/pdf_snapshots/odf/sources/odf_presentation_01.odp.pdf.md b/tests/pdf_snapshots/odf/sources/odf_presentation_01.odp.pdf.md index d9d106ac..3db344da 100644 --- a/tests/pdf_snapshots/odf/sources/odf_presentation_01.odp.pdf.md +++ b/tests/pdf_snapshots/odf/sources/odf_presentation_01.odp.pdf.md @@ -1,15 +1,13 @@ -## e e a e gr ss s ● e 1 +## ● ``` -eWe●aseFl● eTe●● ueTrFe●●● seFC●● tCA2●●● C● abeTl asFl● ur asFl● asl aseuelTr●● B●● assl Sdli +eW e● a s e F l ● e T e● ● u e T r F e● ● ● s e F C● ● t C A 2● ● ● C ● a b e T l a s F l ● u r a s F l ● a s l a s e u e l T r ● ● B ● ● a s s l S d l i ``` -- S +- • Some info: -A•:•S etImB etIm oeominf +• • Some info: Item B Item A -- o tI tI m -- e e e m m -- n f o +- Item B Item A diff --git a/tests/pdf_snapshots/odf/sources/odf_presentation_02.odp.pdf.md b/tests/pdf_snapshots/odf/sources/odf_presentation_02.odp.pdf.md index be0df7dd..1163b433 100644 --- a/tests/pdf_snapshots/odf/sources/odf_presentation_02.odp.pdf.md +++ b/tests/pdf_snapshots/odf/sources/odf_presentation_02.odp.pdf.md @@ -1,25 +1,11 @@ -m d e br a m s m i a ut a e i qli us ,a p m m v c s v u te a c e e us a af tv iv a ll u a ,r tn ri u u e m us c c n u da us c pl arr . . ot cu e m p s bi u M M r m c o bi e u at se a a c s ul cr u m s et m ur e on a ct i. s . o . p si ce d n u M s N di e i n im n s a od u o. r d a e u n u a all V ol e s n si r l e - -m n m e a e t b ar . h n ai x p u en t. ol M ci d m , o ul c N er o u ti f vi tr m m u m r al .I e at tti n bi v tn sli e o en ol c q i ti e , r q - -## R o w - -| | | c o l 3R | | | -| - | - | - | - | - | -| | | | | | -| | 5R | C o | | | -| | | | | | - -o l - diff --git a/tests/pdf_snapshots/odf/sources/odf_table_with_title_01.ods.pdf.md b/tests/pdf_snapshots/odf/sources/odf_table_with_title_01.ods.pdf.md index 2453417c..36f1da96 100644 --- a/tests/pdf_snapshots/odf/sources/odf_table_with_title_01.ods.pdf.md +++ b/tests/pdf_snapshots/odf/sources/odf_table_with_title_01.ods.pdf.md @@ -1,5 +1,7 @@ Sheet1 +## Number of freshwater ducks per year + ## Year Freshwater Ducks -2019120 2020135 2021150 2022170 2023160 2024180 +2019 120 2020 135 2021 150 2022 170 2023 160 2024 180 diff --git a/tests/pdf_snapshots/odf/sources/text_document_01.odt.pdf.md b/tests/pdf_snapshots/odf/sources/text_document_01.odt.pdf.md index 4534fead..5a42db36 100644 --- a/tests/pdf_snapshots/odf/sources/text_document_01.odt.pdf.md +++ b/tests/pdf_snapshots/odf/sources/text_document_01.odt.pdf.md @@ -10,15 +10,16 @@ Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Contrary to popular belief, Lorem Ipsum is not simply random text. It has roots in a piece of classical Latin literature from 45 BC, making it over 2000 years old. Richard McClintock, a Latin professor at Hampden-Sydney College in Virginia, looked up one of the more obscure Latin words, consectetur, from a Lorem Ipsum passage, and going through the cites of the word in classical literature, discovered the undoubtable source. Lorem Ipsum comes from sections 1.10.32 and 1.10.33 of "de Finibus Bonorum et Malorum" (The Extremes of Good and Evil) by Cicero, written in 45 BC. -- 1. numbered list 1 -- 2. numbered list 2 +1. numbered list 1 +2. numbered list 2 + - bullet list 2.1 - bullet list 2.2 - bullet list 2.2.1 - bullet list 2.3 -- numbered list 3 +- numbered list 3 • - bullet list 3.0.1 ## Heading level 2: B -Contrary to popular belief, Lorem Ipsum is not simply random text. Richard McClintock, a Latin professor at Hampden-Sydney College in Virginia, looked up one of the more obscure Latin words, consectetur, from a Lorem Ipsum passage, and going through the cites of the word in classical literature, discovered the undoubtable source. Lorem Ipsum comes from sections 1.10.32 and 1.10.33 of "de Finibus Bonorum et Malorum" (The Extremes of Good and Evil) by Cicero, written in 45 BC. +Contrary to popular belief, Lorem Ipsum is not simply random text . Richard McClintock, a Latin professor at Hampden-Sydney College in Virginia , looked up one of the more obscure Latin words, consectetur, from a Lorem Ipsum passage, and going through the cites of the word in classical literature, discovered the undoubtable source. Lorem Ipsum comes from sections 1.10.32 and 1.10.33 of "de Finibus Bonorum et Malorum" (The Extremes of Good and Evil) by Cicero, written in 45 BC. diff --git a/tests/pdf_snapshots/odf/sources/text_document_02.odt.pdf.md b/tests/pdf_snapshots/odf/sources/text_document_02.odt.pdf.md index d9e66194..fd992c8d 100644 --- a/tests/pdf_snapshots/odf/sources/text_document_02.odt.pdf.md +++ b/tests/pdf_snapshots/odf/sources/text_document_02.odt.pdf.md @@ -8,12 +8,12 @@ Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Contrary to popular belief, Lorem Ipsum is not simply random text. It has roots in a piece of classical Latin literature from 45 BC, making it over 2000 years old. -| Merged row | Cell 1.3 | Cell 1.4 | Cell 1.5 | Cell 1.6 | Cell 1.7 | -| - | - | - | - | - | - | -| | Merged cell 2x2 | Merged cell 2x2 | | Merged column | | -| | | | | Merged column | | -| | | | | Merged column | | -| | | | | Merged column | | +| Merged row | Cell 1.3 | Cell 1.4 | Cell 1.5 | Cell 1.6 | Cell 1.7 | +|--------------|-----------------|-----------------|------------|---------------|------------| +| | Merged cell 2x2 | Merged cell 2x2 | | Merged column | | +| | | | | Merged column | | +| | | | | Merged column | | +| | | | | Merged column | | diff --git a/tests/pdf_snapshots/odf/sources/text_document_03.odt.pdf.md b/tests/pdf_snapshots/odf/sources/text_document_03.odt.pdf.md index 6ded942b..623a4572 100644 --- a/tests/pdf_snapshots/odf/sources/text_document_03.odt.pdf.md +++ b/tests/pdf_snapshots/odf/sources/text_document_03.odt.pdf.md @@ -1,60 +1,60 @@ ## Table with rich cells -| Column A | Column B | -| - | - | -| This is a list: • A First • A Second • A Third | This is a formatted list: • B First • B Second • B Third | +| Column A | Column B | +|---------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------| +| This is a list: • A First • A Second • A Third | This is a formatted list: • B First • B Second • B Third | | First Paragraph Second Paragraph Third paragraph before a numbered list 1. Number one 2. Number two 3. Number three | This is simple text with bold, strikethrough and italic formatting with x2 and H2O | -| This is a paragraph This is another paragraph | | +| This is a paragraph This is another paragraph | | ## Table with nested table Before table -| Column A | Column A | Column A | Column B | Column B | Column B | -| - | - | - | - | - | - | +| Column A | Column A | Column A | Column B | Column B | Column B | +|------------------------|------------------------|------------------------|---------------------------------------|---------------------------------------|---------------------------------------| | Simple cell upper left | Simple cell upper left | Simple cell upper left | Simple cell with bold and italic text | Simple cell with bold and italic text | Simple cell with bold and italic text | -| A | B | C | Rich cell | Rich cell | Rich cell | -| Cell 1 | Cell 2 | Cell 3 | A nested table | A nested table | A nested table | -| | | | | | | -| | | | A | B | C | -| | | | Cell 1 | Cell 2 | Cell 3 | +| A | B | C | Rich cell | Rich cell | Rich cell | +| Cell 1 | Cell 2 | Cell 3 | A nested table | A nested table | A nested table | +| | | | | | | +| | | | A | B | C | +| | | | Cell 1 | Cell 2 | Cell 3 | -After table with bold, underline, strikethrough, and italic formatting +After table with bold , underline, strikethrough, and italic formatting ## Table with pictures -| Column A | Column B | -| - | - | -| Only text | | -| Text and picture | | +| Column A | Column B | +|------------------|------------| +| Only text | | +| Text and picture | | ## Lists with same numId in different cells -| • Cell 1 item 1 | -| - | -| • Cell 1 item 2 | -| • Cell 2 item 1 | -| • Cell 2 item 2 | +| • Cell 1 item 1 | +|-------------------| +| • Cell 1 item 2 | +| • Cell 2 item 1 | +| • Cell 2 item 2 | ## Lists with different numIds in different cells -| • Cell 1 item 1 | -| - | -| • Cell 1 item 2 | -| • Cell 2 item 1 | -| • Cell 2 item 2 | +| • Cell 1 item 1 | +|-------------------| +| • Cell 1 item 2 | +| • Cell 2 item 1 | +| • Cell 2 item 2 | ## Multiple columns with lists -| • R1C1 item 1 • R1C1 item 2 | • R1C2 item 1 • R1C2 2 | -| - | - | -| | item | -| • R2C1 item 1 | • R2C2 item 1 | -| • R2C1 item 2 | • R2C2 item 2 | +| • R1C1 item 1 • R1C1 item 2 | • R1C2 item 1 • R1C2 2 | +|-------------------------------|--------------------------| +| | item | +| • R2C1 item 1 | • R2C2 item 1 | +| • R2C1 item 2 | • R2C2 item 2 | ## Mixed content - list and regular text in different cells -| • List item | -| - | -| • List item 2 | +| • List item | +|-----------------------------| +| • List item 2 | | Regular text in second cell | diff --git a/tests/pdf_snapshots/pdf/sources/2203.01017v2.pdf.md b/tests/pdf_snapshots/pdf/sources/2203.01017v2.pdf.md index 33af4239..0bb6f5c7 100644 --- a/tests/pdf_snapshots/pdf/sources/2203.01017v2.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/2203.01017v2.pdf.md @@ -2,7 +2,7 @@ Ahmed Nassar, Nikolaos Livathinos, Maksym Lysak, Peter Staar IBM Research -{ahn, nli, mly, taa}@zurich.ibm.com +{ ahn,nli,mly,taa } @zurich.ibm.com ## Abstract @@ -10,63 +10,69 @@ Tables organize valuable content in a concise and compact representation. This c ## 1. Introduction -The occurrence of tables in documents is ubiquitous. They often summarise quantitative or factual data, which is cumbersome to describe in verbose text but nevertheless extremely valuable. Unfortunately, this compact representation is often not easy to parse by machines. There are many implicit conventions used to obtain a compact table representation. For example, tables often have complex columnand row-headers in order to reduce duplicated cell content. Lines of different shapes and sizes are leveraged to separate content or indicate a tree structure. Additionally, tables can also have empty/missing table-entries or multi-row textual table-entries. Fig. 1 shows a table which presents all these +The occurrence of tables in documents is ubiquitous. They often summarise quantitative or factual data, which is cumbersome to describe in verbose text but nevertheless extremely valuable. Unfortunately, this compact representation is often not easy to parse by machines. There are many implicit conventions used to obtain a compact table representation. For example, tables often have complex columnand row-headers in order to reduce duplicated cell content. Lines of different shapes and sizes are leveraged to separate content or indicate a tree structure. Additionally, tables can also have empty/missing table-entries or multi-row textual table-entries. Fig. 1 shows a table which presents all these issues. -Figure 1 : Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename:'PMC294423800402'. +## a. Picture of a table: + +Figure 1: Picture of a table with subtle, complex features such as (1) multi-column headers, (2) cell with multi-row text and (3) cells with no content. Image from PubTabNet evaluation set, filename: 'PMC2944238 004 02'. +| issues.a. | | 13 | 201 | | +|-------------|----|------|-------|----| +| 32 | | | | | +| 32 | | | | | +| 32 | | | | | + - b. Red-annotation of bounding boxes, Blue-predictions by TableFormer -| issues.a. 0 | 1 32 | 2 201 13 | 2 201 13 | 2 201 13 | -| - | - | - | - | - | -| 3 | 4 | 5 | 6 | 7 | -| 8 2 | 9 | 10 | 1 1 | 12 | -| 8 2 | 13 | 14 | 15 | 16 | -| 8 2 | 17 | 18 | 19 | | +- c. Structure predicted by TableFormer: -Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document. +| issues.a. 0 | 1 32 | 2 201 13 | 2 201 13 | 2 201 13 | +|---------------|--------|------------|------------|------------| +| 3 | 4 | 5 | 6 | 7 | +| 8 2 | 9 | 10 | 1 1 | 12 | +| 8 2 | 13 | 14 | 15 | 16 | +| 8 2 | 17 | 18 | 19 | | -The first problem is called table-location and has been previously addressed [30, 38, 19, 21, 23, 26, 8] with stateof-the-art object-detection networks (e.g. YOLO and later on Mask-RCNN [9]). For all practical purposes, it can be +Recently, significant progress has been made with vision based approaches to extract tables in documents. For the sake of completeness, the issue of table extraction from documents is typically decomposed into two separate challenges, i.e. (1) finding the location of the table(s) on a document-page and (2) finding the structure of a given table in the document. -considered as a solved problem, given enough ground-truth data to train on. +The first problem is called table-location and has been previously addressed [30, 38, 19, 21, 23, 26, 8] with stateof-the-art object-detection networks (e.g. YOLO and later on Mask-RCNN [9]). For all practical purposes, it can be considered as a solved problem, given enough ground-truth data to train on. The second problem is called table-structure decomposition. The latter is a long standing problem in the community of document understanding [6, 4, 14]. Contrary to the table-location problem, there are no commonly used approaches that can easily be re-purposed to solve this problem. Lately, a set of new model-architectures has been proposed by the community to address table-structure decomposition [37, 36, 18, 20]. All these models have some weaknesses (see Sec. 2). The common denominator here is the reliance on textual features and/or the inability to provide the bounding box of each table-cell in the original image. In this paper, we want to address these weaknesses and present a robust table-structure decomposition algorithm. The design criteria for our model are the following. First, we want our algorithm to be language agnostic. In this way, we can obtain the structure of any table, irregardless of the language. Second, we want our algorithm to leverage as much data as possible from the original PDF document. For programmatic PDF documents, the text-cells can often be extracted much faster and with higher accuracy compared to OCR methods. Last but not least, we want to have a direct link between the table-cell and its bounding box in the image. -To meet the design criteria listed above, we developed a new model called TableFormer and a synthetically generated table structure dataset called SynthTabNet1. In particular, our contributions in this work can be summarised as follows: +To meet the design criteria listed above, we developed a new model called TableFormer and a synthetically generated table structure dataset called SynthTabNet 1 . In particular, our contributions in this work can be summarised as follows: -- We propose TableFormer, a transformer based model that predicts tables structure and bounding boxes for the table content simultaneously in an end-to-end approach. +- We propose TableFormer , a transformer based model that predicts tables structure and bounding boxes for the table content simultaneously in an end-to-end approach. - Across all benchmark datasets TableFormer significantly outperforms existing state-of-the-art metrics, while being much more efficient in training and inference to existing works. - We present SynthTabNet a synthetically generated dataset, with various appearance styles and complexity. - An augmented dataset based on PubTabNet [37], FinTabNet [36], and TableBank [17] with generated ground-truth for reproducibility. The paper is structured as follows. In Sec. 2, we give a brief overview of the current state-of-the-art. In Sec. 3, we describe the datasets on which we train. In Sec. 4, we introduce the TableFormer model-architecture and describe -scribe how this new model-architecture can be re-purposed for other tasks in the computer-vision community. +1https://github.com/IBM/SynthTabNet its results & performance in Sec. 5. As a conclusion, we describe how this new model-architecture can be re-purposed for other tasks in the computer-vision community. ## 2. Previous work and State of the Art -Identifying the structure of a table has been an outstanding problem in the document-parsing community, that motivates many organised public challenges [6, 4, 14]. The difficulty of the problem can be attributed to a number of factors. First, there is a large variety in the shapes and sizes of tables. Such large variety requires a flexible method. This is especially true for complex column- and row headers, which can be extremely intricate and demanding. A second factor of complexity is the lack of data with regard to table-structure. Until the publication of PubTabNet [37], there were no large datasets (i.e. > 100K tables) that provided structure information. This happens primarily due to the fact that tables are notoriously time-consuming to annotate by hand. However, this has definitely changed in recent years with the deliverance of PubTabNet [37], FinTabNet [36], TableBank [17] etc. +Identifying the structure of a table has been an outstanding problem in the document-parsing community, that motivates many organised public challenges [6, 4, 14]. The difficulty of the problem can be attributed to a number of factors. First, there is a large variety in the shapes and sizes of tables. Such large variety requires a flexible method. This is especially true for complex column- and row headers, which can be extremely intricate and demanding. A second factor of complexity is the lack of data with regard to table-structure. Until the publication of PubTabNet [37], there were no large datasets (i.e. > 100 K tables) that provided structure information. This happens primarily due to the fact that tables are notoriously time-consuming to annotate by hand. However, this has definitely changed in recent years with the deliverance of PubTabNet [37], FinTabNet [36], TableBank [17] etc. Before the rising popularity of deep neural networks, the community relied heavily on heuristic and/or statistical methods to do table structure identification [3, 7, 11, 5, 13, 28]. Although such methods work well on constrained tables [12], a more data-driven approach can be applied due to the advent of convolutional neural networks (CNNs) and the availability of large datasets. To the best-of-our knowledge, there are currently two different types of network architecture that are being pursued for state-of-the-art tablestructure identification. -Image-to-Text networks: In this type of network, one predicts a sequence of tokens starting from an encoded image. Such sequences of tokens can be HTML table tags [37, 17] or LaTeX symbols[10]. The choice of symbols is ultimately not very important, since one can be transformed into the other. There are however subtle variations in the Image-to-Text networks. The easiest network architectures are "image-encoder → text-decoder" (IETD), similar to network architectures that try to provide captions to images [32]. In these IETD networks, one expects as output the LaTeX/HTML string of the entire table, i.e. the symbols necessary for creating the table with the content of the table. Another approach is the "image-encoder → dual decoder" (IEDD) networks. In these type of networks, one has two consecutive decoders with different purposes. The first decoder is the tag-decoder, i.e. it only produces the HTML/LaTeX tags which construct an empty table. The second content-decoder uses the encoding of the image in combination with the output encoding of each cell-tag (from the tag-decoder) to generate the textual content of each table cell. The network architecture of IEDD is certainly more elaborate, but it has the advantage that one can pre-train the - -tag-decoder which is constrained to the table-tags. +Image-to-Text networks : In this type of network, one predicts a sequence of tokens starting from an encoded image. Such sequences of tokens can be HTML table tags [37, 17] or LaTeX symbols[10]. The choice of symbols is ultimately not very important, since one can be transformed into the other. There are however subtle variations in the Image-to-Text networks. The easiest network architectures are "image-encoder → text-decoder" (IETD), similar to network architectures that try to provide captions to images [32]. In these IETD networks, one expects as output the LaTeX/HTML string of the entire table, i.e. the symbols necessary for creating the table with the content of the table. Another approach is the "image-encoder → dual decoder" (IEDD) networks. In these type of networks, one has two consecutive decoders with different purposes. The first decoder is the tag-decoder , i.e. it only produces the HTML/LaTeX tags which construct an empty table. The second content-decoder uses the encoding of the image in combination with the output encoding of each cell-tag (from the tag-decoder ) to generate the textual content of each table cell. The network architecture of IEDD is certainly more elaborate, but it has the advantage that one can pre-train the tag-decoder which is constrained to the table-tags. In practice, both network architectures (IETD and IEDD) require an implicit, custom trained object-characterrecognition (OCR) to obtain the content of the table-cells. In the case of IETD, this OCR engine is implicit in the decoder similar to [24]. For the IEDD, the OCR is solely embedded in the content-decoder. This reliance on a custom, implicit OCR decoder is of course problematic. OCR is a well known and extremely tough problem, that often needs custom training for each individual language. However, the limited availability for non-english content in the current datasets, makes it impractical to apply the IETD and IEDD methods on tables with other languages. Additionally, OCR can be completely omitted if the tables originate from programmatic PDF documents with known positions of each cell. The latter was the inspiration for the work of this paper. -Graph Neural networks: Graph Neural networks (GNN's) take a radically different approach to tablestructure extraction. Note that one table cell can constitute out of multiple text-cells. To obtain the table-structure, one creates an initial graph, where each of the text-cells becomes a node in the graph similar to [33, 34, 2]. Each node is then associated with en embedding vector coming from the encoded image, its coordinates and the encoded text. Furthermore, nodes that represent adjacent text-cells are linked. Graph Convolutional Networks (GCN's) based methods take the image as an input, but also the position of the text-cells and their content [18]. The purpose of a GCN is to transform the input graph into a new graph, which replaces the old links with new ones. The new links then represent the table-structure. With this approach, one can avoid the need to build custom OCR decoders. However, the quality of the reconstructed structure is not comparable to the current state-of-the-art [18]. +Graph Neural networks : Graph Neural networks (GNN's) take a radically different approach to tablestructure extraction. Note that one table cell can constitute out of multiple text-cells. To obtain the table-structure, one creates an initial graph, where each of the text-cells becomes a node in the graph similar to [33, 34, 2]. Each node is then associated with en embedding vector coming from the encoded image, its coordinates and the encoded text. Furthermore, nodes that represent adjacent text-cells are linked. Graph Convolutional Networks (GCN's) based methods take the image as an input, but also the position of the text-cells and their content [18]. The purpose of a GCN is to transform the input graph into a new graph, which replaces the old links with new ones. The new links then represent the table-structure. With this approach, one can avoid the need to build custom OCR decoders. However, the quality of the reconstructed structure is not comparable to the current state-of-the-art [18]. -Hybrid Deep Learning-Rule-Based approach: A popular current model for table-structure identification is the use of a hybrid Deep Learning-Rule-Based approach similar to [27, 29]. In this approach, one first detects the position of the table-cells with object detection (e.g. YoloVx or MaskRCNN), then classifies the table into different types (from its images) and finally uses different rule-sets to obtain its table-structure. Currently, this approach achieves stateof-the-art results, but is not an end-to-end deep-learning method. As such, new rules need to be written if different types of tables are encountered. +Hybrid Deep Learning-Rule-Based approach : A popular current model for table-structure identification is the use of a hybrid Deep Learning-Rule-Based approach similar to [27, 29]. In this approach, one first detects the position of the table-cells with object detection (e.g. YoloVx or MaskRCNN), then classifies the table into different types (from its images) and finally uses different rule-sets to obtain its table-structure. Currently, this approach achieves stateof-the-art results, but is not an end-to-end deep-learning method. As such, new rules need to be written if different types of tables are encountered. ## 3. Datasets -We rely on large-scale datasets such as PubTabNet [37], FinTabNet [36], and TableBank [17] datasets to train and evaluate our models. These datasets span over various appearance styles and content. We also introduce our own +We rely on large-scale datasets such as PubTabNet [37], FinTabNet [36], and TableBank [17] datasets to train and evaluate our models. These datasets span over various appearance styles and content. We also introduce our own synthetically generated SynthTabNet dataset to fix an im Figure 2: Distribution of the tables across different table dimensions in PubTabNet + FinTabNet datasets @@ -76,28 +82,26 @@ balance in the previous datasets. The PubTabNet dataset contains 509k tables delivered as annotated PNG images. The annotations consist of the table structure represented in HTML format, the tokenized text and its bounding boxes per table cell. Fig. 1 shows the appearance style of PubTabNet. Depending on its complexity, a table is characterized as "simple" when it does not contain row spans or column spans, otherwise it is "complex". The dataset is divided into Train and Val splits (roughly 98% and 2%). The Train split consists of 54% simple and 46% complex tables and the Val split of 51% and 49% respectively. The FinTabNet dataset contains 112k tables delivered as single-page PDF documents with mixed table structures and text content. Similarly to the PubTabNet, the annotations of FinTabNet include the table structure in HTML, the tokenized text and the bounding boxes on a table cell basis. The dataset is divided into Train, Test and Val splits (81%, 9.5%, 9.5%), and each one is almost equally divided into simple and complex tables (Train: 48% simple, 52% complex, Test: 48% simple, 52% complex, Test: 53% simple, 47% complex). Finally the TableBank dataset consists of 145k tables provided as JPEG images. The latter has annotations for the table structure, but only few with bounding boxes of the table cells. The entire dataset consists of simple tables and it is divided into 90% Train, 3% Test and 7% Val splits. -Due to the heterogeneity across the dataset formats, it was necessary to combine all available data into one homogenized dataset before we could train our models for practical purposes. Given the size of PubTabNet, we adopted its annotation format and we extracted and converted all tables as PNG images with a resolution of 72 dpi. Additionally, we have filtered out tables with extreme sizes due to small - -amount of such tables, and kept only those ones ranging between 1 * 1 and 20* 10 (rows/columns). +Due to the heterogeneity across the dataset formats, it was necessary to combine all available data into one homogenized dataset before we could train our models for practical purposes. Given the size of PubTabNet, we adopted its annotation format and we extracted and converted all tables as PNG images with a resolution of 72 dpi. Additionally, we have filtered out tables with extreme sizes due to small amount of such tables, and kept only those ones ranging between 1*1 and 20*10 (rows/columns). The availability of the bounding boxes for all table cells is essential to train our models. In order to distinguish between empty and non-empty bounding boxes, we have introduced a binary class in the annotation. Unfortunately, the original datasets either omit the bounding boxes for whole tables (e.g. TableBank) or they narrow their scope only to non-empty cells. Therefore, it was imperative to introduce a data pre-processing procedure that generates the missing bounding boxes out of the annotation information. This procedure first parses the provided table structure and calculates the dimensions of the most fine-grained grid that covers the table structure. Notice that each table cell may occupy multiple grid squares due to row or column spans. In case of PubTabNet we had to compute missing bounding boxes for 48% of the simple and 69% of the complex tables. Regarding FinTabNet, 68% of the simple and 98% of the complex tables require the generation of bounding boxes. As it is illustrated in Fig. 2, the table distributions from all datasets are skewed towards simpler structures with fewer number of rows/columns. Additionally, there is very limited variance in the table styles, which in case of PubTabNet and FinTabNet means one styling format for the majority of the tables. Similar limitations appear also in the type of table content, which in some cases (e.g. FinTabNet) is restricted to a certain domain. Ultimately, the lack of diversity in the training dataset damages the ability of the models to generalize well on unseen data. -Motivated by those observations we aimed at generating a synthetic table dataset named SynthTabNet. This approach offers control over: 1) the size of the dataset, 2) the table structure, 3) the table style and 4) the type of content. The complexity of the table structure is described by the size of the table header and the table body, as well as the percentage of the table cells covered by row spans and column spans. A set of carefully designed styling templates provides the basis to build a wide range of table appearances. Lastly, the table content is generated out of a curated collection of text corpora. By controlling the size and scope of the synthetic datasets we are able to train and evaluate our models in a variety of different conditions. For example, we can first generate a highly diverse dataset to train our models and then evaluate their performance on other synthetic datasets which are focused on a specific domain. +Motivated by those observations we aimed at generating a synthetic table dataset named SynthTabNet . This approach offers control over: 1) the size of the dataset, 2) the table structure, 3) the table style and 4) the type of content. The complexity of the table structure is described by the size of the table header and the table body, as well as the percentage of the table cells covered by row spans and column spans. A set of carefully designed styling templates provides the basis to build a wide range of table appearances. Lastly, the table content is generated out of a curated collection of text corpora. By controlling the size and scope of the synthetic datasets we are able to train and evaluate our models in a variety of different conditions. For example, we can first generate a highly diverse dataset to train our models and then evaluate their performance on other synthetic datasets which are focused on a specific domain. -In this regard, we have prepared four synthetic datasets, each one containing 150k examples. The corpora to generate the table text consists of the most frequent terms appearing in PubTabNet and FinTabNet together with randomly generated text. The first two synthetic datasets have been fine-tuned to mimic the appearance of the original datasets +In this regard, we have prepared four synthetic datasets, each one containing 150k examples. The corpora to generate the table text consists of the most frequent terms appearing in PubTabNet and FinTabNet together with randomly generated text. The first two synthetic datasets have been fine-tuned to mimic the appearance of the original datasets but encompass more complicated table structures. The third -| | Tags | Bbox | Size | Format | -| - | - | - | - | - | -| PubTabNet | ✓ | ✓ | 509k | PNG | -| FinTabNet | ✓ | ✓ | 1 12k | PDF | -| TableBank | ✓ | ✗ | 145k | JPEG | -| Combined-Tabnet(*) | ✓ | ✓ | 400k | PNG | -| Combined(**) | ✓ | ✓ | 500k | PNG | -| SynthTabNet | ✓ | ✓ | 600k | PNG | +| | Tags | Bbox | Size | Format | +|--------------------|--------|--------|--------|----------| +| PubTabNet | ✓ | ✓ | 509k | PNG | +| FinTabNet | ✓ | ✓ | 1 12k | PDF | +| TableBank | ✓ | ✗ | 145k | JPEG | +| Combined-Tabnet(*) | ✓ | ✓ | 400k | PNG | +| Combined(**) | ✓ | ✓ | 500k | PNG | +| SynthTabNet | ✓ | ✓ | 600k | PNG | -Table 1 : Both "Combined-Tabnet" and "CombinedTabnet" are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank. +Table 1: Both "Combined-Tabnet" and "CombinedTabnet" are variations of the following: (*) The CombinedTabnet dataset is the processed combination of PubTabNet and Fintabnet. (**) The combined dataset is the processed combination of PubTabNet, Fintabnet and TableBank. one adopts a colorful appearance with high contrast and the last one contains tables with sparse content. Lastly, we have combined all synthetic datasets into one big unified synthetic dataset of 600k examples. @@ -109,7 +113,7 @@ Given the image of a table, TableFormer is able to predict: 1) a sequence of tok ## 4.1. Model architecture. -We now describe in detail the proposed method, which is composed of three main components, see Fig. 4. Our CNN Backbone Network encodes the input as a feature vector of predefined length. The input feature vector of the encoded image is passed to the Structure Decoder to produce a sequence of HTML tags that represent the structure of the table. With each prediction of an HTML standard data cell ('') the hidden state of that cell is passed to the Cell BBox Decoder. As for spanning cells, such as row or column span, the tag is broken down to'<','rowspan=' or' colspan=', with the number of spanning cells (attribute), and'>'. The hidden state attached to'<' is passed to the Cell BBox Decoder. A shared feed forward network (FFN) receives the hidden states from the Structure Decoder, to provide the final detection predictions of the bounding box coordinates and their classification. +We now describe in detail the proposed method, which is composed of three main components, see Fig. 4. Our CNN Backbone Network encodes the input as a feature vector of predefined length. The input feature vector of the encoded image is passed to the Structure Decoder to produce a sequence of HTML tags that represent the structure of the table. With each prediction of an HTML standard data cell (' < td > ') the hidden state of that cell is passed to the Cell BBox Decoder. As for spanning cells, such as row or column span, the tag is broken down to ' < ', 'rowspan=' or 'colspan=', with the number of spanning cells (attribute), and ' > '. The hidden state attached to ' < ' is passed to the Cell BBox Decoder. A shared feed forward network (FFN) receives the hidden states from the Structure Decoder, to provide the final detection predictions of the bounding box coordinates and their classification. CNN Backbone Network. A ResNet-18 CNN is the backbone that receives the table image and encodes it as a vector of predefined length. The network has been modified by removing the linear and pooling layer, as we are not per @@ -117,49 +121,45 @@ Figure 3: TableFormer takes in an image of the PDF and creates bounding box and -Figure 4: Given an input image of a table, the Encoder produces fixed-length features that represent the input image. The features are then passed to both the Structure Decoder and Cell BBox Decoder. During training, the Structure Decoder receives'tokenized tags' of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. Simultaneously, the Cell BBox Decoder selects features referring to the data cells ('','<') and passes them through an attention network, an MLP, and a +Figure 4: Given an input image of a table, the Encoder produces fixed-length features that represent the input image. The features are then passed to both the Structure Decoder and Cell BBox Decoder . During training, the Structure Decoder receives 'tokenized tags' of the HTML code that represent the table structure. Afterwards, a transformer encoder and decoder architecture is employed to produce features that are received by a linear layer, and the Cell BBox Decoder. The linear layer is applied to the features to predict the tags. Simultaneously, the Cell BBox Decoder selects features referring to the data cells (' < td > ', ' < ') and passes them through an attention network, an MLP, and a linear layer to predict the bounding boxes. -of size 28*28. ResNet by default downsamples the image resolution by 32 and then the encoded image is provided to both the Structure Decoder, and Cell BBox Decoder. +forming classification, and adding an adaptive pooling layer of size 28*28. ResNet by default downsamples the image resolution by 32 and then the encoded image is provided to both the Structure Decoder , and Cell BBox Decoder . Structure Decoder. The transformer architecture of this component is based on the work proposed in [31]. After extensive experimentation, the Structure Decoder is modeled as a transformer encoder with two encoder layers and a transformer decoder made from a stack of 4 decoder layers that comprise mainly of multi-head attention and feed forward layers. This configuration uses fewer layers and heads in comparison to networks applied to other problems (e.g. "Scene Understanding", "Image Captioning"), something which we relate to the simplicity of table images. The transformer encoder receives an encoded image from the CNN Backbone Network and refines it through a multi-head dot-product attention layer, followed by a Feed Forward Network. During training, the transformer decoder receives as input the output feature produced by the transformer encoder, and the tokenized input of the HTML ground-truth tags. Using a stack of multi-head attention layers, different aspects of the tag sequence could be inferred. This is achieved by each attention head on a layer operating in a different subspace, and then combining altogether their attention score. -Cell BBox Decoder. Our architecture allows to simultaneously predict HTML tags and bounding boxes for each table cell without the need of a separate object detector end to end. This approach is inspired by DETR [1] which employs a Transformer Encoder, and Decoder that looks for a specific number of object queries (potential object detections). As our model utilizes a transformer architecture, the hidden state of the ' and'<' HTML structure tags become the object query. +Cell BBox Decoder. Our architecture allows to simultaneously predict HTML tags and bounding boxes for each table cell without the need of a separate object detector end to end. This approach is inspired by DETR [1] which employs a Transformer Encoder, and Decoder that looks for a specific number of object queries (potential object detections). As our model utilizes a transformer architecture, the hidden state of the < td > ' and ' < ' HTML structure tags become the object query. -The encoding generated by the CNN Backbone Network along with the features acquired for every data cell from the Transformer Decoder are then passed to the attention network. The attention network takes both inputs and learns to provide an attention weighted encoding. This weighted at - -tention encoding is then multiplied to the encoded image to produce a feature for each table cell. Notice that this is different than the typical object detection problem where imbalances between the number of detections and the amount of objects may exist. In our case, we know up front that the produced detections always match with the table cells in number and correspondence. +The encoding generated by the CNN Backbone Network along with the features acquired for every data cell from the Transformer Decoder are then passed to the attention network. The attention network takes both inputs and learns to provide an attention weighted encoding. This weighted at tention encoding is then multiplied to the encoded image to produce a feature for each table cell. Notice that this is different than the typical object detection problem where imbalances between the number of detections and the amount of objects may exist. In our case, we know up front that the produced detections always match with the table cells in number and correspondence. The output features for each table cell are then fed into the feed-forward network (FFN). The FFN consists of a Multi-Layer Perceptron (3 layers with ReLU activation function) that predicts the normalized coordinates for the bounding box of each table cell. Finally, the predicted bounding boxes are classified based on whether they are empty or not using a linear layer. -Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as ls) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as lbox. lbox consists of the generally used l1 loss for object detection and the IoU loss (liou) to be scale invariant as explained in [25]. In comparison to DETR, we do not use the Hungarian algorithm [15] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-toone match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as input to the Cell BBox Decoder, and 2) Our bounding boxes generation mechanism (see Sec. 3) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets. +Loss Functions. We formulate a multi-task loss Eq. 2 to train our network. The Cross-Entropy loss (denoted as l s ) is used to train the Structure Decoder which predicts the structure tokens. As for the Cell BBox Decoder it is trained with a combination of losses denoted as l box . l box consists of the generally used l 1 loss for object detection and the IoU loss ( l ) to be scale invariant as explained in [25]. In iou comparison to DETR, we do not use the Hungarian algorithm [15] to match the predicted bounding boxes with the ground-truth boxes, as we have already achieved a one-toone match through two steps: 1) Our token input sequence is naturally ordered, therefore the hidden states of the table data cells are also in order when they are provided as input to the Cell BBox Decoder , and 2) Our bounding boxes generation mechanism (see Sec. 3) ensures a one-to-one mapping between the cell content and its bounding box for all post-processed datasets. The loss used to train the TableFormer can be defined as following: -where λ ∈ [0, 1], and λiou, λl1 ∈ R are hyper-parameters. +where λ ∈ [0, 1], and λ , λ ∈ R are hyper-parameters. iou l 1 ## 5. Experimental Results ## 5.1. Implementation Details -TableFormer uses ResNet-18 as the CNN Backbone Network. The input images are resized to 448*448 pixels and the feature map has a dimension of 28*28. Additionally, we enforce the following input constraints: +TableFormer uses ResNet-18 as the CNN Backbone Network . The input images are resized to 448*448 pixels and the feature map has a dimension of 28*28. Additionally, we enforce the following input constraints: -Although input constraints are used also by other methods, - -Former. This allows to utilize input samples with longer sequences and images with larger dimensions. +Although input constraints are used also by other methods, such as EDD, ours are less restrictive due to the improved runtime performance and lower memory footprint of TableFormer. This allows to utilize input samples with longer sequences and images with larger dimensions. The Transformer Encoder consists of two "Transformer Encoder Layers", with an input feature size of 512, feed forward network of 1024, and 4 attention heads. As for the Transformer Decoder it is composed of four "Transformer Decoder Layers" with similar input and output dimensions as the "Transformer Encoder Layers". Even though our model uses fewer layers and heads than the default implementation parameters, our extensive experimentation has proved this setup to be more suitable for table images. We attribute this finding to the inherent design of table images, which contain mostly lines and text, unlike the more elaborate content present in other scopes (e.g. the COCO dataset). Moreover, we have added ResNet blocks to the inputs of the Structure Decoder and Cell BBox Decoder. This prevents a decoder having a stronger influence over the learned weights which would damage the other prediction task (structure vs bounding boxes), but learn task specific weights instead. Lastly our dropout layers are set to 0.5. -For training, TableFormer is trained with 3 Adam optimizers, each one for the CNN Backbone Network, Structure Decoder, and Cell BBox Decoder. Taking the PubTabNet as an example for our parameter set up, the initializing learning rate is 0.001 for 12 epochs with a batch size of 24, and λ set to 0.5. Afterwards, we reduce the learning rate to 0.0001, the batch size to 18 and train for 12 more epochs or convergence. +For training, TableFormer is trained with 3 Adam optimizers, each one for the CNN Backbone Network , Structure Decoder , and Cell BBox Decoder . Taking the PubTabNet as an example for our parameter set up, the initializing learning rate is 0.001 for 12 epochs with a batch size of 24, and λ set to 0.5. Afterwards, we reduce the learning rate to 0.0001, the batch size to 18 and train for 12 more epochs or convergence. -TableFormer is implemented with PyTorch and Torchvision libraries [22]. To speed up the inference, the image undergoes a single forward pass through the CNN Backbone Network and transformer encoder. This eliminates the overhead of generating the same features for each decoding step. Similarly, we employ a'caching' technique to preform faster autoregressive decoding. This is achieved by storing the features of decoded tokens so we can reuse them for each time step. Therefore, we only compute the attention for each new tag. +TableFormer is implemented with PyTorch and Torchvision libraries [22]. To speed up the inference, the image undergoes a single forward pass through the CNN Backbone Network and transformer encoder. This eliminates the overhead of generating the same features for each decoding step. Similarly, we employ a 'caching' technique to preform faster autoregressive decoding. This is achieved by storing the features of decoded tokens so we can reuse them for each time step. Therefore, we only compute the attention for each new tag. ## 5.2. Generalization @@ -173,140 +173,146 @@ The Tree-Edit-Distance-Based Similarity (TEDS) metric was introduced in [37]. It -where Ta and Tb represent tables in tree structure HTML format. EditDist denotes the tree-edit distance, and |T| represents the number of nodes in T. +where T and T represent tables in tree structure HTML format. EditDist denotes the tree-edit distance, and a b | T | represents the number of nodes in T . ## 5.4. Quantitative Analysis Structure. As shown in Tab. 2, TableFormer outperforms all SOTA methods across different datasets by a large margin for predicting the table structure from an image. All the more, our model outperforms pre-trained methods. During the evaluation we do not apply any table filtering. We also provide our baseline results on the SynthTabNet dataset. It has been observed that large tables (e.g. tables that occupy half of the page or more) yield poor predictions. We attribute this issue to the image resizing during the preprocessing step, that produces downsampled images with indistinguishable features. This problem can be addressed by treating such big tables with a separate model which accepts a large input image size. -| Model | Dataset | Simple | TEDS Complex | All | -| - | - | - | - | - | -| EDD | PTN | 91.1 | 88.7 | 89.9 | -| GTE | PTN | - | - | 93.01 | -| TableFormer | PTN | 98.5 | 95.0 | 96.75 | -| EDD | FTN | 88.4 | 92.08 | 90.6 | -| GTE | FTN | - | - | 87.14 | -| GTE (FT) | FTN | - | - | 91.02 | -| TableFormer | FTN | 97.5 | 96.0 | 96.8 | -| EDD | TB | 86.0 | - | 86.0 | -| TableFormer | TB | 89.6 | - | 89.6 | -| TableFormer | STN | 96.9 | 95.7 | 96.7 | +| Model | Dataset | Simple | TEDS Complex | All | +|-------------|-----------|----------|----------------|-------| +| EDD | PTN | 91.1 | 88.7 | 89.9 | +| GTE | PTN | - | - | 93.01 | +| TableFormer | PTN | 98.5 | 95.0 | 96.75 | +| EDD | FTN | 88.4 | 92.08 | 90.6 | +| GTE | FTN | - | - | 87.14 | +| GTE (FT) | FTN | - | - | 91.02 | +| TableFormer | FTN | 97.5 | 96.0 | 96.8 | +| EDD | TB | 86.0 | - | 86.0 | +| TableFormer | TB | 89.6 | - | 89.6 | +| TableFormer | STN | 96.9 | 95.7 | 96.7 | Table 2: Structure results on PubTabNet (PTN), FinTabNet (FTN), TableBank (TB) and SynthTabNet (STN). FT: Model was trained on PubTabNet then finetuned. -Cell Detection. Like any object detector, our Cell BBox Detector provides bounding boxes that can be improved with post-processing during inference. We make use of the grid-like structure of tables to refine the predictions. A detailed explanation on the post-processing is available in the - -bel of'content' only using the PASCAL VOC mAP metric for pre-processing and post-processing. Note that we do not have post-processing results for SynthTabNet as images are only provided. To compare the performance of our proposed approach, we've integrated TableFormer's Cell BBox Decoder into EDD architecture. As mentioned previously, the Structure Decoder provides the Cell BBox Decoder with the features needed to predict the bounding box predictions. Therefore, the accuracy of the Structure Decoder directly influences the accuracy of the Cell BBox Decoder. If the Structure Decoder predicts an extra column, this will result in an extra column of predicted bounding boxes. +Cell Detection. Like any object detector, our Cell BBox Detector provides bounding boxes that can be improved with post-processing during inference. We make use of the grid-like structure of tables to refine the predictions. A detailed explanation on the post-processing is available in the supplementary material. As shown in Tab. 3, we evaluate our Cell BBox Decoder accuracy for cells with a class label of 'content' only using the PASCAL VOC mAP metric for pre-processing and post-processing. Note that we do not have post-processing results for SynthTabNet as images are only provided. To compare the performance of our proposed approach, we've integrated TableFormer's Cell BBox Decoder into EDD architecture. As mentioned previously, the Structure Decoder provides the Cell BBox Decoder with the features needed to predict the bounding box predictions. Therefore, the accuracy of the Structure Decoder directly influences the accuracy of the Cell BBox Decoder . If the Structure Decoder predicts an extra column, this will result in an extra column of predicted bounding boxes. -| Model | Dataset | mAP | mAP (PP) | -| - | - | - | - | -| EDD+BBox | PubTabNet | 79.2 | 82.7 | -| TableFormer | PubTabNet | 82.1 | 86.8 | -| TableFormer | SynthTabNet | 87.7 | - | +| Model | Dataset | mAP | mAP (PP) | +|-------------|-------------|-------|------------| +| EDD+BBox | PubTabNet | 79.2 | 82.7 | +| TableFormer | PubTabNet | 82.1 | 86.8 | +| TableFormer | SynthTabNet | 87.7 | - | Table 3: Cell Bounding Box detection results on PubTabNet, and FinTabNet. PP: Post-processing. Cell Content. In this section, we evaluate the entire pipeline of recovering a table with content. Here we put our approach to test by capitalizing on extracting content from the PDF cells rather than decoding from images. Tab. 4 shows the TEDs score of HTML code representing the structure of the table along with the content inserted in the data cell and compared with the ground-truth. Our method achieved a 5.3% increase over the state-of-the-art, and commercial solutions. We believe our scores would be higher if the HTML ground-truth matched the extracted PDF cell content. Unfortunately, there are small discrepancies such as spacings around words or special characters with various unicode representations. -| Model | Simple | TEDS Complex | All | -| - | - | - | - | -| Tabula | 78.0 | 57.8 | 67.9 | -| Traprange | 60.8 | 49.9 | 55.4 | -| Camelot | 80.0 | 66.0 | 73.0 | -| Acrobat Pro | 68.9 | 61.8 | 65.3 | -| EDD | 91.2 | 85.4 | 88.3 | -| TableFormer | 95.4 | 90.1 | 93.6 | +| Model | Simple | TEDS Complex | All | +|-------------|----------|----------------|-------| +| Tabula | 78.0 | 57.8 | 67.9 | +| Traprange | 60.8 | 49.9 | 55.4 | +| Camelot | 80.0 | 66.0 | 73.0 | +| Acrobat Pro | 68.9 | 61.8 | 65.3 | +| EDD | 91.2 | 85.4 | 88.3 | +| TableFormer | 95.4 | 90.1 | 93.6 | Table 4: Results of structure with content retrieved using cell detection on PubTabNet. In all cases the input is PDF documents with cropped tables. +- a. Red - PDF cells, Green - predicted bounding boxes, Blue - post-processed predictions matched to PDF cells Japanese language (previously unseen by TableFormer): Example table from FinTabNet: + +Japanese language (previously unseen by TableFormer): Example table from FinTabNet: + Figure 5: One of the benefits of TableFormer is that it is language agnostic, as an example, the left part of the illustration demonstrates TableFormer predictions on previously unseen language (Japanese). Additionally, we see that TableFormer is robust to variability in style and content, right side of the illustration shows the example of the TableFormer prediction from the FinTabNet dataset. -- b. Structure predicted by TableFormer - -| | RSUsShares (in millions) | RSUsShares (in millions) | viewingWeighted Average Grant Date Fair Value | viewingWeighted Average Grant Date Fair Value | -| - | - | - | - | - | -| | | PSUs | RSUs | PSUs | -| Nonvested on January 1 | 1.1 | 0.3 | 90.1 0 $ | $ 91.1 9 | -| Granted | 0.5 | 0.1 | 1 1 7.44 | 1 22.41 | -| Vested | (0.5) | (0.1) | 87.08 | 81.14 | -| Canceled or forfeited | (0.1) | — | 102.01 | 92.18 | -| Nonvested on December 31 | 1.0 | 0.3 | 1 04.85 $ | $ 1 04.51 | - -| | | 論文フ ァ イ ル | 論文フ ァ イ ル | 参考文献 | 参考文献 | -| - | - | - | - | - | - | -| 出典 | フ ァ イ ル数 | 英語 | 日本語 | 英語 | 日本語 | -| Association for Computational Linguistics(ACL2003) | 65 | 65 | 0 | 150 | 0 | -| Computational Linguistics(COLING2002) | 140 | 140 | 0 | 150 | 0 | -| 電気情報通信学会2003年総合大会 | 150 | 8 | 142 | 223 | 147 | -| 情報処理学会第65回全国大会(2003) | 177 | 1 | 176 | 150 | 236 | -| 第17回人工知能学会全国大会(2003) | 208 | 5 | 203 | 152 | 244 | -| 自然言語処理研究会第146〜155回 | 98 | 2 | 96 | 150 | 232 | -| WWWから収集 した論文 | 107 | 73 | 34 | 147 | 96 | -| 計 | 945 | 294 | 651 | 1122 | 955 | +- b. Structure predicted by TableFormer, with superimposed matched PDF cell text: + +| | RSUsShares (in millions) | RSUsShares (in millions) | viewingWeighted Average Grant Date Fair Value | viewingWeighted Average Grant Date Fair Value | +|--------------------------|----------------------------|----------------------------|-------------------------------------------------|-------------------------------------------------| +| | | PSUs | RSUs | PSUs | +| Nonvested on January 1 | 1.1 | 0.3 | 90.1 0 $ | $ 91.1 9 | +| Granted | 0.5 | 0.1 | 1 1 7.44 | 1 22.41 | +| Vested | (0.5) | (0.1) | 87.08 | 81.14 | +| Canceled or forfeited | (0.1) | — | 102.01 | 92.18 | +| Nonvested on December 31 | 1.0 | 0.3 | 1 04.85 $ | $ 1 04.51 | + +| | | 論文フ ァ イ ル | 論文フ ァ イ ル | 参考文献 | 参考文献 | +|----------------------------------------------------|----------|-------------|-------------|--------|--------| +| 出典 | フ ァ イ ル数 | 英語 | 日本語 | 英語 | 日本語 | +| Association for Computational Linguistics(ACL2003) | 65 | 65 | 0 | 150 | 0 | +| Computational Linguistics(COLING2002) | 140 | 140 | 0 | 150 | 0 | +| 電気情報通信学会2003年総合大会 | 150 | 8 | 142 | 223 | 147 | +| 情報処理学会第65回全国大会(2003) | 177 | 1 | 176 | 150 | 236 | +| 第17回人工知能学会全国大会(2003) | 208 | 5 | 203 | 152 | 244 | +| 自然言語処理研究会第146〜155回 | 98 | 2 | 96 | 150 | 232 | +| WWWから収集 した論文 | 107 | 73 | 34 | 147 | 96 | +| 計 | 945 | 294 | 651 | 1122 | 955 | +Text is aligned to match original for ease of viewing + Figure 6: An example of TableFormer predictions (bounding boxes and structure) from generated SynthTabNet table. +## 6. Future Work & Conclusion + ## 5.5. Qualitative Analysis In this paper, we presented TableFormer an end-to-end transformer based approach to predict table structures and bounding boxes of cells from an image. This approach enables us to recreate the table structure, and extract the cell content from PDF or OCR by using bounding boxes. Additionally, it provides the versatility required in real-world scenarios when dealing with various types of PDF documents, and languages. Furthermore, our method outperforms all state-of-the-arts with a wide margin. Finally, we introduce "SynthTabNet" a challenging synthetically generated dataset that reinforces missing characteristics from other datasets. -We showcase several visualizations for the different components of our network on various "complex" tables within datasets presented in this work in Fig. 5 and Fig. 6 As it is shown, our model is able to predict bounding boxes for all table cells, even for the empty ones. Additionally, our post-processing techniques can extract the cell content by matching the predicted bounding boxes to the PDF cells based on their overlap and spatial proximity. The left part of Fig. 5 demonstrates also the adaptability of our method to any language, as it can successfully extract Japanese text, although the training set contains only English content. We provide more visualizations including the intermediate steps in the supplementary material. Overall these illustrations justify the versatility of our method across a diverse +We showcase several visualizations for the different components of our network on various "complex" tables within datasets presented in this work in Fig. 5 and Fig. 6 As it is shown, our model is able to predict bounding boxes for all table cells, even for the empty ones. Additionally, our post-processing techniques can extract the cell content by matching the predicted bounding boxes to the PDF cells based on their overlap and spatial proximity. The left part of Fig. 5 demonstrates also the adaptability of our method to any language, as it can successfully extract Japanese text, although the training set contains only English content. We provide more visualizations including the intermediate steps in the supplementary material. Overall these illustrations justify the versatility of our method across a diverse range of table appearances and content type. ## References - [1] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to -end object detection with transformers. In Andrea Vedaldi, Horst Bischof, Thomas Brox, and Jan-Michael Frahm, editors, Computer Vision - ECCV 2020, pages 213-229, Cham, 2020. Springer International Publishing. 5 +end object detection with transformers. 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In 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) , volume 01, pages 1162- 1167, 2017. 1 +- [27] Sebastian Schreiber, Stefan Agne, Ivo Wolf, Andreas Dengel, and Sheraz Ahmed. Deepdesrt: Deep learning for detection and structure recognition of tables in document images. In 2017 14th IAPR international conference on document analysis and recognition (ICDAR) , volume 1, pages 1162-1167. IEEE, 2017. 3 +- [28] Faisal Shafait and Ray Smith. Table detection in heterogeneous documents. In Proceedings of the 9th IAPR International Workshop on Document Analysis Systems , pages 65- 72, 2010. 2 +- [29] Shoaib Ahmed Siddiqui, Imran Ali Fateh, Syed Tahseen Raza Rizvi, Andreas Dengel, and Sheraz Ahmed. Deeptabstr: Deep learning based table structure recognition. In 2019 International Conference on Document Analysis and Recognition (ICDAR) , pages 1403-1409. IEEE, 2019. 3 +- [30] Peter W J Staar, Michele Dolfi, Christoph Auer, and Costas Bekas. 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IEEE, 2019. 3 +- [34] Wenyuan Xue, Baosheng Yu, Wen Wang, Dacheng Tao, and Qingyong Li. Tgrnet: A table graph reconstruction network for table structure recognition. arXiv preprint arXiv:2106.10598 , 2021. 3 +- [35] Quanzeng You, Hailin Jin, Zhaowen Wang, Chen Fang, and Jiebo Luo. Image captioning with semantic attention. In Proceedings of the IEEE conference on computer vision and pattern recognition , pages 4651-4659, 2016. 4 +- [36] Xinyi Zheng, Doug Burdick, Lucian Popa, Peter Zhong, and Nancy Xin Ru Wang. Global table extractor (gte): A framework for joint table identification and cell structure recognition using visual context. Winter Conferencefor Applications in Computer Vision (WACV) , 2021. 2, 3 +- [37] Xu Zhong, Elaheh ShafieiBavani, and Antonio Jimeno Yepes. Image-based table recognition: Data, model, +- and evaluation. In Andrea Vedaldi, Horst Bischof, Thomas Brox, and Jan-Michael Frahm, editors, Computer Vision - ECCV 2020 , pages 564-580, Cham, 2020. Springer International Publishing. 2, 3, 7 +- [38] Xu Zhong, Jianbin Tang, and Antonio Jimeno Yepes. Publaynet: Largest dataset ever for document layout analysis. In 2019 International Conference on Document Analysis and Recognition (ICDAR) , pages 1015-1022, 2019. 1 ## TableFormer: Table Structure Understanding with Transformers Supplementary Material @@ -326,17 +332,15 @@ Figure 7 illustrates the distribution of the tables across different dimensions ## 1.2. Synthetic datasets -Aiming to train and evaluate our models in a broader spectrum of table data we have synthesized four types of - -Every synthetic dataset contains 150k examples, summing up to 600k synthetic examples. All datasets are divided into Train, Test and Val splits (80%, 10%, 10%). +Aiming to train and evaluate our models in a broader spectrum of table data we have synthesized four types of datasets. Each one contains tables with different appear ances in regard to their size, structure, style and content. Every synthetic dataset contains 150k examples, summing up to 600k synthetic examples. All datasets are divided into Train, Test and Val splits (80%, 10%, 10%). The process of generating a synthetic dataset can be decomposed into the following steps: -- 1. Prepare styling and content templates: The styling templates have been manually designed and organized into groups of scope specific appearances (e.g. financial data, marketing data, etc.) Additionally, we have prepared curated collections of content templates by extracting the most frequently used terms out of non-synthetic datasets (e.g. PubTabNet, FinTabNet, etc.). -- 2. Generate table structures: The structure of each synthetic dataset assumes a horizontal table header which potentially spans over multiple rows and a table body that may contain a combination of row spans and column spans. However, spans are not allowed to cross the header - body boundary. The table structure is described by the parameters: Total number of table rows and columns, number of header rows, type of spans (header only spans, row only spans, column only spans, both row and column spans), maximum span size and the ratio of the table area covered by spans. -- 3. Generate content: Based on the dataset theme, a set of suitable content templates is chosen first. Then, this content can be combined with purely random text to produce the synthetic content. -- 4. Apply styling templates: Depending on the domain of the synthetic dataset, a set of styling templates is first manually selected. Then, a style is randomly selected to format the appearance of the synthesized table. -- 5. Render the complete tables: The synthetic table is finally rendered by a web browser engine to generate the bounding boxes for each table cell. A batching technique is utilized to optimize the runtime overhead of the rendering process. +1. Prepare styling and content templates: The styling templates have been manually designed and organized into groups of scope specific appearances (e.g. financial data, marketing data, etc.) Additionally, we have prepared curated collections of content templates by extracting the most frequently used terms out of non-synthetic datasets (e.g. PubTabNet, FinTabNet, etc.). +2. Generate table structures: The structure of each synthetic dataset assumes a horizontal table header which potentially spans over multiple rows and a table body that may contain a combination of row spans and column spans. However, spans are not allowed to cross the header - body boundary. The table structure is described by the parameters: Total number of table rows and columns, number of header rows, type of spans (header only spans, row only spans, column only spans, both row and column spans), maximum span size and the ratio of the table area covered by spans. +3. Generate content: Based on the dataset theme , a set of suitable content templates is chosen first. Then, this content can be combined with purely random text to produce the synthetic content. +4. Apply styling templates: Depending on the domain of the synthetic dataset, a set of styling templates is first manually selected. Then, a style is randomly selected to format the appearance of the synthesized table. +5. Render the complete tables: The synthetic table is finally rendered by a web browser engine to generate the bounding boxes for each table cell. A batching technique is utilized to optimize the runtime overhead of the rendering process. ## 2. Prediction post-processing for PDF documents @@ -353,40 +357,42 @@ However, it is possible to mitigate those limitations by combining the TableForm Here is a step-by-step description of the prediction postprocessing: -- 1. Get the minimal grid dimensions - number of rows and columns for the predicted table structure. This represents the most granular grid for the underlying table structure. -- 2. Generate pair-wise matches between the bounding boxes of the PDF cells and the predicted cells. The Intersection Over Union (IOU) metric is used to evaluate the quality of the matches. -- 3. Use a carefully selected IOU threshold to designate the matches as "good" ones and "bad" ones. -- 3.a. If all IOU scores in a column are below the threshold, discard all predictions (structure and bounding boxes) for that column. -- 4. Find the best-fitting content alignment for the predicted cells with good IOU per each column. The alignment of the column can be identified by the following formula: +1. Get the minimal grid dimensions - number of rows and columns for the predicted table structure. This represents the most granular grid for the underlying table structure. +2. Generate pair-wise matches between the bounding boxes of the PDF cells and the predicted cells. The Intersection Over Union (IOU) metric is used to evaluate the quality of the matches. +3. Use a carefully selected IOU threshold to designate the matches as "good" ones and "bad" ones. + +3. a. If all IOU scores in a column are below the threshold, discard all predictions (structure and bounding boxes) for that column. +4. Find the best-fitting content alignment for the predicted cells with good IOU per each column. The alignment of the column can be identified by the following formula: -where c is one of {left, centroid, right} and xc is the xcoordinate for the corresponding point. +where c is one of { left, centroid, right } and x c is the xcoordinate for the corresponding point. -- 5. Use the alignment computed in step 4, to compute +5. Use the alignment computed in step 4, to compute the median x -coordinate for all table columns and the me -ing the computations, helps to eliminate outliers caused by occasional column spans which are usually wider than the normal. +dian cell size for all table cells. The usage of median during the computations, helps to eliminate outliers caused by occasional column spans which are usually wider than the normal. + +6. Snap all cells with bad IOU to their corresponding median x -coordinates and cell sizes. +7. Generate a new set of pair-wise matches between the corrected bounding boxes and PDF cells. This time use a modified version of the IOU metric, where the area of the intersection between the predicted and PDF cells is divided by the PDF cell area. In case there are multiple matches for the same PDF cell, the prediction with the higher score is preferred. This covers the cases where the PDF cells are smaller than the area of predicted or corrected prediction cells. +8. In some rare occasions, we have noticed that TableFormer can confuse a single column as two. When the postprocessing steps are applied, this results with two predicted columns pointing to the same PDF column. In such case we must de-duplicate the columns according to highest total column intersection score. +9. Pick up the remaining orphan cells. There could be cases, when after applying all the previous post-processing steps, some PDF cells could still remain without any match to predicted cells. However, it is still possible to deduce the correct matching for an orphan PDF cell by mapping its bounding box on the geometry of the grid. This mapping decides if the content of the orphan cell will be appended to an already matched table cell, or a new table cell should be created to match with the orphan. -- 6. Snap all cells with bad IOU to their corresponding median x-coordinates and cell sizes. -- 7. Generate a new set of pair-wise matches between the corrected bounding boxes and PDF cells. This time use a modified version of the IOU metric, where the area of the intersection between the predicted and PDF cells is divided by the PDF cell area. In case there are multiple matches for the same PDF cell, the prediction with the higher score is preferred. This covers the cases where the PDF cells are smaller than the area of predicted or corrected prediction cells. -- 8. In some rare occasions, we have noticed that TableFormer can confuse a single column as two. When the postprocessing steps are applied, this results with two predicted columns pointing to the same PDF column. In such case we must de-duplicate the columns according to highest total column intersection score. -- 9. Pick up the remaining orphan cells. There could be cases, when after applying all the previous post-processing steps, some PDF cells could still remain without any match to predicted cells. However, it is still possible to deduce the correct matching for an orphan PDF cell by mapping its bounding box on the geometry of the grid. This mapping decides if the content of the orphan cell will be appended to an already matched table cell, or a new table cell should be created to match with the orphan. - 9a. Compute the top and bottom boundary of the horizontal band for each grid row (min/max y coordinates per row). - 9b. Intersect the orphan's bounding box with the row bands, and map the cell to the closest grid row. - 9c. Compute the left and right boundary of the vertical band for each grid column (min/max x coordinates per column). - 9d. Intersect the orphan's bounding box with the column bands, and map the cell to the closest grid column. -9e. If the table cell under the identified row and column is not empty, extend its content with the content of the or - -phan cell. +9e. If the table cell under the identified row and column is not empty, extend its content with the content of the or phan cell. 9f. Otherwise create a new structural cell and match it wit the orphan cell. Aditional images with examples of TableFormer predictions and post-processing can be found below. +Figure 8: Example of a table with multi-line header. + -tween cells. +Figure 9: Example of a table with big empty distance between cells. @@ -394,11 +400,11 @@ tween cells. Figure 10: Example of a complex table with empty cells. -Figure 11 : Simple table with different style and empty cells. +Figure 13: Table predictions example on colorful table. - +Figure 11: Simple table with different style and empty cells. @@ -408,9 +414,15 @@ Figure 14: Example with multi-line text. +Figure 12: Simple table predictions and post processing. + + + +Figure 15: Example with triangular table. + -mis-aligned bounding boxes prediction artifact. +Figure 16: Example of how post-processing helps to restore mis-aligned bounding boxes prediction artifact. Figure 17: Example of long table. End-to-end example from initial PDF cells to prediction of bounding boxes, post processing and prediction of structure. diff --git a/tests/pdf_snapshots/pdf/sources/2206.01062.pdf.md b/tests/pdf_snapshots/pdf/sources/2206.01062.pdf.md index bd552496..dd800400 100644 --- a/tests/pdf_snapshots/pdf/sources/2206.01062.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/2206.01062.pdf.md @@ -1,30 +1,32 @@ -Birgit Pfitzmann IBM Research Rueschlikon, Switzerland +## DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis + +Birgit Pfitzmann IBM Research Rueschlikon, Switzerland bpf@zurich.ibm.com -Ahmed S. Nassar IBM Research Rueschlikon, Switzerland +Ahmed S. Nassar IBM Research Rueschlikon, Switzerland ahn@zurich.ibm.com ## ABSTRACT -Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet, a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis. +Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability of public, large ground-truth datasets such as PubLayNet and DocBank, deep-learning models have proven to be very effective at layout detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet , a new, publicly available, document-layout annotation dataset in COCO format. It contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNet also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size. Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis. ## CCS CONCEPTS -• Information systems → Document structure; • Applied computing → Document analysis; • Computing methodologies → Machine learning; Computer vision; Object detection; +• Information systems → Document structure ; • Applied computing → Document analysis ; • Computing methodologies → Machine learning ; Computer vision ; Object detection ; Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). -KDD'22, August 14-18, 2022, Washington, DC, USA +KDD '22, August 14-18, 2022, Washington, DC, USA © 2022 Copyright held by the owner/author(s). ACM ISBN 978-1-4503-9385-0/22/08. -## DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis +https://doi.org/10.1145/3534678.3539043 -IBM Research Rueschlikon, Switzerland dol@zurich.ibm.com +Michele Dolfi IBM Research Rueschlikon, Switzerland dol@zurich.ibm.com -IBM Research Rueschlikon, Switzerland +Christoph Auer IBM Research Rueschlikon, Switzerland cau@zurich.ibm.com -IBM Research Rueschlikon, Switzerland taa@zurich.ibm.com +Peter Staar IBM Research Rueschlikon, Switzerland taa@zurich.ibm.com @@ -44,9 +46,9 @@ PDF document conversion, layout segmentation, object-detection, data set, Machin ## ACM Reference Format: -Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'22), August 14-18, 2022, Washington, DC, USA. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/ +Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for DocumentLayout Analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '22), August 14-18, 2022, Washington, DC, USA. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/ 3534678.3539043 -KDD'22, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar +KDD '22, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar ## 1 INTRODUCTION @@ -54,16 +56,18 @@ Despite the substantial improvements achieved with machine-learning (ML) approac A key problem in the process of document conversion is to understand the structure of a single document page, i.e. which segments of text should be grouped together in a unit. To train models for this task, there are currently two large datasets available to the community, PubLayNet [6] and DocBank [7]. They were introduced in 2019 and 2020 respectively and significantly accelerated the implementation of layout detection and segmentation models due to their sizes of 300K and 500K ground-truth pages. These sizes were achieved by leveraging an automation approach. The benefit of automated ground-truth generation is obvious: one can generate large ground-truth datasets at virtually no cost. However, the automation introduces a constraint on the variability in the dataset, because corresponding structured source data must be available. PubLayNet and DocBank were both generated from scientific document repositories (PubMed and arXiv), which provide XML or LATEX sources. Those scientific documents present a limited variability in their layouts, because they are typeset in uniform templates provided by the publishers. Obviously, documents such as technical manuals, annual company reports, legal text, government tenders, etc. have very different and partially unique layouts. As a consequence, the layout predictions obtained from models trained on PubLayNet or DocBank is very reasonable when applied on scientific documents. However, for more artistic or free-style layouts, we see sub-par prediction quality from these models, which we demonstrate in Section 5. -In this paper, we present the DocLayNet dataset. It provides pageby-page layout annotation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique document pages, of which a fraction carry double- or triple-annotations. DocLayNet is similar in spirit to PubLayNet and DocBank and will likewise be made available to the public1 in order to stimulate the document-layout analysis community. It distinguishes itself in the following aspects: +In this paper, we present the DocLayNet dataset. It provides pageby-page layout annotation ground-truth using bounding-boxes for 11 distinct class labels on 80863 unique document pages, of which a fraction carry double- or triple-annotations. DocLayNet is similar in spirit to PubLayNet and DocBank and will likewise be made available to the public 1 in order to stimulate the document-layout analysis community. It distinguishes itself in the following aspects: + +- (1) Human Annotation : In contrast to PubLayNet and DocBank, we relied on human annotation instead of automation approaches to generate the data set. +- (2) Large Layout Variability : We include diverse and complex layouts from a large variety of public sources. +- (3) Detailed Label Set : We define 11 class labels to distinguish layout features in high detail. PubLayNet provides 5 labels; DocBank provides 13, although not a superset of ours. +- (4) Redundant Annotations : A fraction of the pages in the DocLayNet data set carry more than one human annotation. -- (1) Human Annotation: In contrast to PubLayNet and DocBank, we relied on human annotation instead of automation approaches to generate the data set. -- (2) Large Layout Variability: We include diverse and complex layouts from a large variety of public sources. -- (3) Detailed Label Set: We define 11 class labels to distinguish layout features in high detail. PubLayNet provides 5 labels; DocBank provides 13, although not a superset of ours. -- (4) Redundant Annotations: A fraction of the pages in the DocLayNet data set carry more than one human annotation. +1 https://developer.ibm.com/exchanges/data/all/doclaynet -and quality control analysis. +This enables experimentation with annotation uncertainty and quality control analysis. -- (5) Pre-defined Train-, Test- & Validation-set: Like DocBank, we provide fixed train-, test- & validation-sets to ensure proportional representation of the class-labels. Further , we prevent leakage of unique layouts across sets, which has a large effect on model accuracy scores. +- (5) Pre-defined Train-, Test- & Validation-set : Like DocBank, we provide fixed train-, test- & validation-sets to ensure proportional representation of the class-labels. Further, we prevent leakage of unique layouts across sets, which has a large effect on model accuracy scores. All aspects outlined above are detailed in Section 3. In Section 4, we will elaborate on how we designed and executed this large-scale human annotation campaign. We will also share key insights and lessons learned that might prove helpful for other parties planning to set up annotation campaigns. @@ -77,7 +81,7 @@ Lately, new types of ML models for document-layout analysis have emerged in the ## 3 THE DOCLAYNET DATASET -DocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of human annotations, and 1591 carry three. This amounts to 91104 total annotation instances. The annotations provide layout information in the shape of labeled, rectangular boundingboxes. We define 11 distinct labels for layout features, namely Caption, Footnote, Formula, List-item, Page-footer, Page-header, Picture, Section-header, Table, Text, and Title. Our reasoning for picking this particular label set is detailed in Section 4. +DocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of human annotations, and 1591 carry three. This amounts to 91104 total annotation instances. The annotations provide layout information in the shape of labeled, rectangular boundingboxes. We define 11 distinct labels for layout features, namely Caption , Footnote , Formula , List-item , Page-footer , Page-header , Picture , Section-header , Table , Text , and Title . Our reasoning for picking this particular label set is detailed in Section 4. In addition to open intellectual property constraints for the source documents, we required that the documents in DocLayNet adhere to a few conditions. Firstly, we kept scanned documents @@ -85,74 +89,74 @@ Figure 2: Distribution of DocLayNet pages across document categories. -to a minimum, since they introduce difficulties in annotation (see Section 4). As a second condition, we focussed on medium to large documents (> 10 pages) with technical content, dense in complex tables, figures, plots and captions. Such documents carry a lot of information value, but are often hard to analyse with high accuracy due to their challenging layouts. Counterexamples of documents not included in the dataset are receipts, invoices, hand-written documents or photographs showing "text in the wild". +to a minimum, since they introduce difficulties in annotation (see Section 4). As a second condition, we focussed on medium to large documents ( > 10 pages) with technical content, dense in complex tables, figures, plots and captions. Such documents carry a lot of information value, but are often hard to analyse with high accuracy due to their challenging layouts. Counterexamples of documents not included in the dataset are receipts, invoices, hand-written documents or photographs showing "text in the wild". -The pages in DocLayNet can be grouped into six distinct categories, namely Financial Reports, Manuals, Scientific Articles, Laws & Regulations, Patents and Government Tenders. Each document category was sourced from various repositories. For example, Financial Reports contain both free-style format annual reports2 which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories (Financial Reports and Manuals) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes. +The pages in DocLayNet can be grouped into six distinct categories, namely Financial Reports , Manuals , Scientific Articles , Laws & Regulations , Patents and Government Tenders . Each document category was sourced from various repositories. For example, Financial format annual reports2 Reports contain both free-style which expose company-specific, artistic layouts as well as the more formal SEC filings. The two largest categories ( Financial Reports and Manuals ) contain a large amount of free-style layouts in order to obtain maximum variability. In the other four categories, we boosted the variability by mixing documents from independent providers, such as different government websites or publishers. In Figure 2, we show the document categories contained in DocLayNet with their respective sizes. We did not control the document selection with regard to language. The vast majority of documents contained in DocLayNet (close to 95%) are published in English language. However, DocLayNet also contains a number of documents in other languages such as German (2.5%), French (1.0%) and Japanese (1.0%). While the document language has negligible impact on the performance of computer vision methods such as object detection and segmentation models, it might prove challenging for layout analysis methods which exploit textual features. To ensure that future benchmarks in the document-layout analysis community can be easily compared, we have split up DocLayNet into pre-defined train-, test- and validation-sets. In this way, we can avoid spurious variations in the evaluation scores due to random splitting in train-, test- and validation-sets. We also ensured that less frequent labels are represented in train and test sets in equal proportions. -among the different sets. Importantly, we ensure that subsets are only split on full-document boundaries. This avoids that pages of the same document are spread over train, test and validation set, which can give an undesired evaluation advantage to models and lead to overestimation of their prediction accuracy. We will show the impact of this decision in Section 5. +2e.g. AAPL from https://www.annualreports.com/ -In order to accommodate the different types of models currently in use by the community, we provide DocLayNet in an augmented COCO format [16]. This entails the standard COCO ground-truth file (in JSON format) with the associated page images (in PNG format, 1025×1025 pixels). Furthermore, custom fields have been added to each COCO record to specify document category, original document filename and page number. In addition, we also provide the original PDF pages, as well as sidecar files containing parsed PDF text and text-cell coordinates (in JSON). All additional files are linked to the primary page images by their matching filenames. +Table 1 shows the overall frequency and distribution of the labels among the different sets. Importantly, we ensure that subsets are only split on full-document boundaries. This avoids that pages of the same document are spread over train, test and validation set, which can give an undesired evaluation advantage to models and lead to overestimation of their prediction accuracy. We will show the impact of this decision in Section 5. + +In order to accommodate the different types of models currently in use by the community, we provide DocLayNet in an augmented COCO format [16]. This entails the standard COCO ground-truth file (in JSON format) with the associated page images (in PNG format, 1025 × 1025 pixels). Furthermore, custom fields have been added to each COCO record to specify document category, original document filename and page number. In addition, we also provide the original PDF pages, as well as sidecar files containing parsed PDF text and text-cell coordinates (in JSON). All additional files are linked to the primary page images by their matching filenames. Despite being cost-intense and far less scalable than automation, human annotation has several benefits over automated groundtruth generation. The first and most obvious reason to leverage human annotations is the freedom to annotate any type of document without requiring a programmatic source. For most PDF documents, the original source document is not available. The latter is not a hard constraint with human annotation, but it is for automated methods. A second reason to use human annotations is that the latter usually provide a more natural interpretation of the page layout. The human-interpreted layout can significantly deviate from the programmatic layout used in typesetting. For example, "invisible" tables might be used solely for aligning text paragraphs on columns. Such typesetting tricks might be interpreted by automated methods incorrectly as an actual table, while the human annotation will interpret it correctly as Text or other styles. The same applies to multi-line text elements, when authors decided to space them as "invisible" list elements without bullet symbols. A third reason to gather ground-truth through human annotation is to estimate a "natural" upper bound on the segmentation accuracy. As we will show in Section 4, certain documents featuring complex layouts can have different but equally acceptable layout interpretations. This natural upper bound for segmentation accuracy can be found by annotating the same pages multiple times by different people and evaluating the inter-annotator agreement. Such a baseline consistency evaluation is very useful to define expectations for a good target accuracy in trained deep neural network models and avoid overfitting (see Table 1). On the flip side, achieving high annotation consistency proved to be a key challenge in human annotation, as we outline in Section 4. ## 4 ANNOTATION CAMPAIGN -The annotation campaign was carried out in four phases. In phase one, we identified and prepared the data sources for annotation. In phase two, we determined the class labels and how annotations should be done on the documents in order to obtain maximum consistency. The latter was guided by a detailed requirement analysis and exhaustive experiments. In phase three, we trained the annotation staff and performed exams for quality assurance. In phase four , - -Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as% of row "Total") in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges. - -| | | % of Total | % of Total | % of Total | triple inter-annotator mAP @ 0.5-0.95 (%) | triple inter-annotator mAP @ 0.5-0.95 (%) | triple inter-annotator mAP @ 0.5-0.95 (%) | triple inter-annotator mAP @ 0.5-0.95 (%) | triple inter-annotator mAP @ 0.5-0.95 (%) | triple inter-annotator mAP @ 0.5-0.95 (%) | triple inter-annotator mAP @ 0.5-0.95 (%) | -| - | - | - | - | - | - | - | - | - | - | - | - | -| class label | Count | Train | Test | Val | All | Fin | Man | Sci | Law | Pat | Ten | -| Caption | 22524 | 2.04 | 1.77 | 2.32 | 84-89 | 40-61 | 86-92 | 94-99 | 95-99 | 69-78 | n/a | -| Footnote | 6318 | 0.60 | 0.31 | 0.58 | 83-91 | n/a | 100 | 62-88 | 85-94 | n/a | 82-97 | -| Formula | 25027 | 2.25 | 1.90 | 2.96 | 83-85 | n/a | n/a | 84-87 | 86-96 | n/a | n/a | -| List-item | 185660 | 17.19 | 13.34 | 15.82 | 87-88 | 74-83 | 90-92 | 97-97 | 81-85 | 75-88 | 93-95 | -| Page-footer | 70878 | 6.51 | 5.58 | 6.00 | 93-94 | 88-90 | 95-96 | 100 | 92-97 | 100 | 96-98 | -| Page-header | 58022 | 5.10 | 6.70 | 5.06 | 85-89 | 66-76 | 90-94 | 98-100 | 91-92 | 97-99 | 81-86 | -| Picture | 45976 | 4.21 | 2.78 | 5.31 | 69-71 | 56-59 | 82-86 | 69-82 | 80-95 | 66-71 | 59-76 | -| Section-header | 142884 | 12.60 | 15.77 | 12.85 | 83-84 | 76-81 | 90-92 | 94-95 | 87-94 | 69-73 | 78-86 | -| Table | 34733 | 3.20 | 2.27 | 3.60 | 77-81 | 75-80 | 83-86 | 98-99 | 58-80 | 79-84 | 70-85 | -| Text | 510377 | 45.82 | 49.28 | 45.00 | 84-86 | 81-86 | 88-93 | 89-93 | 87-92 | 71-79 | 87-95 | -| Title | 5071 | 0.47 | 0.30 | 0.50 | 60-72 | 24-63 | 50-63 | 94-100 | 82-96 | 68-79 | 24-56 | -| Total | 1107470 | 941123 | 99816 | 66531 | 82-83 | 71-74 | 79-81 | 89-94 | 86-91 | 71-76 | 68-85 | +The annotation campaign was carried out in four phases. In phase one, we identified and prepared the data sources for annotation. In phase two, we determined the class labels and how annotations should be done on the documents in order to obtain maximum consistency. The latter was guided by a detailed requirement analysis and exhaustive experiments. In phase three, we trained the annotation staff and performed exams for quality assurance. In phase four, + +Table 1: DocLayNet dataset overview. Along with the frequency of each class label, we present the relative occurrence (as % of row "Total") in the train, test and validation sets. The inter-annotator agreement is computed as the mAP@0.5-0.95 metric between pairwise annotations from the triple-annotated pages, from which we obtain accuracy ranges. + +| | | % of Total | % of Total | % of Total | triple inter-annotator mAP @0.5-0.95 (%) | triple inter-annotator mAP @0.5-0.95 (%) | triple inter-annotator mAP @0.5-0.95 (%) | triple inter-annotator mAP @0.5-0.95 (%) | triple inter-annotator mAP @0.5-0.95 (%) | triple inter-annotator mAP @0.5-0.95 (%) | triple inter-annotator mAP @0.5-0.95 (%) | +|----------------|---------|--------------|--------------|--------------|--------------------------------------------|--------------------------------------------|--------------------------------------------|--------------------------------------------|--------------------------------------------|--------------------------------------------|--------------------------------------------| +| class label | Count | Train | Test | Val | All | Fin | Man | Sci | Law | Pat | Ten | +| Caption | 22524 | 2.04 | 1.77 | 2.32 | 84-89 | 40-61 | 86-92 | 94-99 | 95-99 | 69-78 | n/a | +| Footnote | 6318 | 0.60 | 0.31 | 0.58 | 83-91 | n/a | 100 | 62-88 | 85-94 | n/a | 82-97 | +| Formula | 25027 | 2.25 | 1.90 | 2.96 | 83-85 | n/a | n/a | 84-87 | 86-96 | n/a | n/a | +| List-item | 185660 | 17.19 | 13.34 | 15.82 | 87-88 | 74-83 | 90-92 | 97-97 | 81-85 | 75-88 | 93-95 | +| Page-footer | 70878 | 6.51 | 5.58 | 6.00 | 93-94 | 88-90 | 95-96 | 100 | 92-97 | 100 | 96-98 | +| Page-header | 58022 | 5.10 | 6.70 | 5.06 | 85-89 | 66-76 | 90-94 | 98-100 | 91-92 | 97-99 | 81-86 | +| Picture | 45976 | 4.21 | 2.78 | 5.31 | 69-71 | 56-59 | 82-86 | 69-82 | 80-95 | 66-71 | 59-76 | +| Section-header | 142884 | 12.60 | 15.77 | 12.85 | 83-84 | 76-81 | 90-92 | 94-95 | 87-94 | 69-73 | 78-86 | +| Table | 34733 | 3.20 | 2.27 | 3.60 | 77-81 | 75-80 | 83-86 | 98-99 | 58-80 | 79-84 | 70-85 | +| Text | 510377 | 45.82 | 49.28 | 45.00 | 84-86 | 81-86 | 88-93 | 89-93 | 87-92 | 71-79 | 87-95 | +| Title | 5071 | 0.47 | 0.30 | 0.50 | 60-72 | 24-63 | 50-63 | 94-100 | 82-96 | 68-79 | 24-56 | +| Total | 1107470 | 941123 | 99816 | 66531 | 82-83 | 71-74 | 79-81 | 89-94 | 86-91 | 71-76 | 68-85 | Figure 3: Corpus Conversion Service annotation user interface. The PDF page is shown in the background, with overlaid text-cells (in darker shades). The annotation boxes can be drawn by dragging a rectangle over each segment with the respective label from the palette on the right. -company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process. +include publication repositories such as arXiv 3 , government offices, company websites as well as data directory services for financial reports and patents. Scanned documents were excluded wherever possible because they can be rotated or skewed. This would not allow us to perform annotation with rectangular bounding-boxes and therefore complicate the annotation process. Preparation work included uploading and parsing the sourced PDF documents in the Corpus Conversion Service (CCS) [22], a cloud-native platform which provides a visual annotation interface and allows for dataset inspection and analysis. The annotation interface of CCS is shown in Figure 3. The desired balance of pages between the different document categories was achieved by selective subsampling of pages with certain desired properties. For example, we made sure to include the title page of each document and bias the remaining page selection to those with figures or tables. The latter was achieved by leveraging pre-trained object detection models from PubLayNet, which helped us estimate how many figures and tables a given page contains. -Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption, Footnote, Formula, List-item, Pagefooter, Page-header, Picture, Section-header, Table, Text, and Title. Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation, as seen in DocBank, are often only distinguishable by discriminating on - -we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised. +Phase 2: Label selection and guideline. We reviewed the collected documents and identified the most common structural features they exhibit. This was achieved by identifying recurrent layout elements and lead us to the definition of 11 distinct class labels. These 11 class labels are Caption , Footnote , Formula , List-item , Pagefooter , Page-header , Picture , Section-header , Table , Text , and Title . Critical factors that were considered for the choice of these class labels were (1) the overall occurrence of the label, (2) the specificity of the label, (3) recognisability on a single page (i.e. no need for context from previous or next page) and (4) overall coverage of the page. Specificity ensures that the choice of label is not ambiguous, while coverage ensures that all meaningful items on a page can be annotated. We refrained from class labels that are very specific to a document category, such as Abstract in the Scientific Articles category. We also avoided class labels that are tightly linked to the semantics of the text. Labels such as Author and Affiliation , as seen in DocBank, are often only distinguishable by discriminating on we distributed the annotation workload and performed continuous quality controls. Phase one and two required a small team of experts only. For phases three and four, a group of 40 dedicated annotators were assembled and supervised. -Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went +Phase 1: Data selection and preparation. Our inclusion criteria for documents were described in Section 3. A large effort went into ensuring that all documents are free to use. The data sources -3https://arxiv.org/ +3 https://arxiv.org/ the textual content of an element, which goes beyond visual layout recognition, in particular outside the Scientific Articles category. At first sight, the task of visual document-layout interpretation appears intuitive enough to obtain plausible annotations in most cases. However, during early trial-runs in the core team, we observed many cases in which annotators use different annotation styles, especially for documents with challenging layouts. For example, if a figure is presented with subfigures, one annotator might draw a single figure bounding-box, while another might annotate each subfigure separately. The same applies for lists, where one might annotate all list items in one block or each list item separately. In essence, we observed that challenging layouts would be annotated in different but plausible ways. To illustrate this, we show in Figure 4 multiple examples of plausible but inconsistent annotations on the same pages. -Obviously, this inconsistency in annotations is not desirable for datasets which are intended to be used for model training. To minimise these inconsistencies, we created a detailed annotation guideline. While perfect consistency across 40 annotation staff members is clearly not possible to achieve, we saw a huge improvement in annotation consistency after the introduction of our annotation guideline. A few selected, non-trivial highlights of the guideline are: +Obviously, this inconsistency in annotations is not desirable for datasets which are intended to be used for model training. To minimise these inconsistencies, we created a detailed annotation guideline. While perfect consistency across 40 annotation staff members is clearly not possible to achieve, we saw a huge improvement in annotation consistency after the introduction of our annotation guideline. A few selected, non-trivial highlights of the guideline are:( -- (1) Every list-item is an individual object instance with class label List-item. This definition is different from PubLayNet and DocBank, where all list-items are grouped together into one List object. +- 1) Every list-item is an individual object instance with class label List-item . This definition is different from PubLayNet and DocBank, where all list-items are grouped together into one List object. - (2) A List-item is a paragraph with hanging indentation. Singleline elements can qualify as List-item if the neighbour elements expose hanging indentation. Bullet or enumeration symbols are not a requirement. -- (3) For every Caption, there must be exactly one corresponding Picture or Table. +- (3) For every Caption , there must be exactly one corresponding Picture or Table . - (4) Connected sub-pictures are grouped together in one Picture object. - (5) Formula numbers are included in a Formula object. -- (6) Emphasised text (e.g. in italic or bold) at the beginning of a paragraph is not considered a Section-header, unless it appears exclusively on its own line. +- (6) Emphasised text (e.g. in italic or bold) at the beginning of a paragraph is not considered a Section-header , unless it appears exclusively on its own line. The complete annotation guideline is over 100 pages long and a detailed description is obviously out of scope for this paper. Nevertheless, it will be made publicly available alongside with DocLayNet for future reference. -Phase 3: Training. After a first trial with a small group of people, we realised that providing the annotation guideline and a set of random practice pages did not yield the desired quality level for layout annotation. Therefore we prepared a subset of pages with two different complexity levels, each with a practice and an exam part. 974 pages were reference-annotated by one proficient core team member. Annotation staff were then given the task to annotate the same subsets (blinded from the reference). By comparing the annotations of each staff member with the reference annotations, we could quantify how closely their annotations matched the reference. Only after passing two exam levels with high annotation quality, +Phase 3: Training. After a first trial with a small group of people, we realised that providing the annotation guideline and a set of random practice pages did not yield the desired quality level for layout annotation. Therefore we prepared a subset of pages with two different complexity levels, each with a practice and an exam part. 974 pages were reference-annotated by one proficient core team member. Annotation staff were then given the task to annotate the same subsets (blinded from the reference). By comparing the annotations of each staff member with the reference annotations, we could quantify how closely their annotations matched the reference. Only after passing two exam levels with high annotation quality, staff were admitted into the production phase. Practice iterations Figure 4: Examples of plausible annotation alternatives for the same page. Criteria in our annotation guideline can resolve cases A to C, while the case D remains ambiguous. @@ -164,27 +168,27 @@ Phase 4: Production annotation. The previously selected 80K pages were annotated Table 2: Prediction performance (mAP@0.5-0.95) of object detection networks on DocLayNet test set. The MRCNN (Mask R-CNN) and FRCNN (Faster R-CNN) models with ResNet-50 or ResNet-101 backbone were trained based on the network architectures from the detectron2 model zoo (Mask R-CNN R50, R101-FPN 3x, Faster R-CNN R101-FPN 3x), with default configurations. The YOLO implementation utilized was YOLOv5x6 [13]. All models were initialised using pre-trained weights from the COCO 2017 dataset. -| | human | MRCNN | MRCNN | FRCNN | YOLO v5x6 | -| - | - | - | - | - | - | -| | | R50 | R101 | R101 | | -| Caption | 84-89 | 68.4 | 71.5 | 70.1 | 77.7 | -| Footnote | 83-91 | 70.9 | 71.8 | 73.7 | 77.2 | -| Formula | 83-85 | 60.1 | 63.4 | 63.5 | 66.2 | -| List-item | 87-88 | 81.2 | 80.8 | 81.0 | 86.2 | -| Page-footer | 93-94 | 61.6 | 59.3 | 58.9 | 61.1 | -| Page-header | 85-89 | 71.9 | 70.0 | 72.0 | 67.9 | -| Picture | 69-71 | 71.7 | 72.7 | 72.0 | 77.1 | -| Section-header | 83-84 | 67.6 | 69.3 | 68.4 | 74.6 | -| Table | 77-81 | 82.2 | 82.9 | 82.2 | 86.3 | -| Text | 84-86 | 84.6 | 85.8 | 85.4 | 88.1 | -| Title | 60-72 | 76.7 | 80.4 | 79.9 | 82.7 | -| All | 82-83 | 72.4 | 73.5 | 73.4 | 76.8 | - -to avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture. For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity. +| | human | MRCNN | MRCNN | FRCNN | YOLO v5x6 | +|----------------|---------|---------|---------|---------|-------------| +| | | R50 | R101 | R101 | | +| Caption | 84-89 | 68.4 | 71.5 | 70.1 | 77.7 | +| Footnote | 83-91 | 70.9 | 71.8 | 73.7 | 77.2 | +| Formula | 83-85 | 60.1 | 63.4 | 63.5 | 66.2 | +| List-item | 87-88 | 81.2 | 80.8 | 81.0 | 86.2 | +| Page-footer | 93-94 | 61.6 | 59.3 | 58.9 | 61.1 | +| Page-header | 85-89 | 71.9 | 70.0 | 72.0 | 67.9 | +| Picture | 69-71 | 71.7 | 72.7 | 72.0 | 77.1 | +| Section-header | 83-84 | 67.6 | 69.3 | 68.4 | 74.6 | +| Table | 77-81 | 82.2 | 82.9 | 82.2 | 86.3 | +| Text | 84-86 | 84.6 | 85.8 | 85.4 | 88.1 | +| Title | 60-72 | 76.7 | 80.4 | 79.9 | 82.7 | +| All | 82-83 | 72.4 | 73.5 | 73.4 | 76.8 | + +to avoid this at any cost in order to have clear, unbiased baseline numbers for human document-layout annotation. Third, we introduced the feature of snapping boxes around text segments to obtain a pixel-accurate annotation and again reduce time and effort. The CCS annotation tool automatically shrinks every user-drawn box to the minimum bounding-box around the enclosed text-cells for all purely text-based segments, which excludes only Table and Picture . For the latter, we instructed annotation staff to minimise inclusion of surrounding whitespace while including all graphical lines. A downside of snapping boxes to enclosed text cells is that some wrongly parsed PDF pages cannot be annotated correctly and need to be skipped. Fourth, we established a way to flag pages as rejected for cases where no valid annotation according to the label guidelines could be achieved. Example cases for this would be PDF pages that render incorrectly or contain layouts that are impossible to capture with non-overlapping rectangles. Such rejected pages are not contained in the final dataset. With all these measures in place, experienced annotation staff managed to annotate a single page in a typical timeframe of 20s to 60s, depending on its complexity. ## 5 EXPERIMENTS -The primary goal of DocLayNet is to obtain high-quality ML models capable of accurate document-layout analysis on a wide variety of challenging layouts. As discussed in Section 2, object detection models are currently the easiest to use, due to the standardisation of ground-truth data in COCO format [16] and the availability of general frameworks such as detectron2 [17]. Furthermore, baseline numbers in PubLayNet and DocBank were obtained using standard object detection models such as Mask R-CNN and Faster R-CNN. +The primary goal of DocLayNet is to obtain high-quality ML models capable of accurate document-layout analysis on a wide variety of challenging layouts. As discussed in Section 2, object detection models are currently the easiest to use, due to the standardisation of ground-truth data in COCO format [16] and the availability of general frameworks such as detectron2 [17]. Furthermore, baseline numbers in PubLayNet and DocBank were obtained using standard object detection models such as Mask R-CNN and Faster R-CNN. As such, we will relate to these object detection methods in this Figure 5: Prediction performance (mAP@0.5-0.95) of a Mask R-CNN network with ResNet50 backbone trained on increasing fractions of the DocLayNet dataset. The learning curve flattens around the 80% mark, indicating that increasing the size of the DocLayNet dataset with similar data will not yield significantly better predictions. @@ -196,24 +200,24 @@ In this section, we will present several aspects related to the performance of o ## Baselines for Object Detection -In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training and evaluation were performed on RGB images with dimensions of 1025 × 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text, Table and Picture. This is not entirely surprising, as Text, Table and Picture are abundant and the most visually distinctive in a document. +In Table 2, we present baseline experiments (given in mAP) on Mask R-CNN [12], Faster R-CNN [11], and YOLOv5 [13]. Both training and evaluation were performed on RGB images with dimensions of 1025 × 1025 pixels. For training, we only used one annotation in case of redundantly annotated pages. As one can observe, the variation in mAP between the models is rather low, but overall between 6 and 10% lower than the mAP computed from the pairwise human annotations on triple-annotated pages. This gives a good indication that the DocLayNet dataset poses a worthwhile challenge for the research community to close the gap between human recognition and ML approaches. It is interesting to see that Mask R-CNN and Faster R-CNN produce very comparable mAP scores, indicating that pixel-based image segmentation derived from bounding-boxes does not help to obtain better predictions. On the other hand, the more recent Yolov5x model does very well and even out-performs humans on selected labels such as Text , Table and Picture . This is not entirely surprising, as Text , Table and Picture are abundant and the most visually distinctive in a document. Table 3: Performance of a Mask R-CNN R50 network in mAP@0.5-0.95 scores trained on DocLayNet with different class label sets. The reduced label sets were obtained by either down-mapping or dropping labels. -| Class-count | 11 | 6 | 5 | 4 | -| - | - | - | - | - | -| Caption | 68 | Text | Text | Text | -| Footnote | 71 | Text | Text | Text | -| Formula | 60 | Text | Text | Text | -| List-item | 81 | Text | 82 | Text | -| Page-footer | 62 | 62 | - | - | -| Page-header | 72 | 68 | - | - | -| Picture | 72 | 72 | 72 | 72 | -| Section-header | 68 | 67 | 69 | 68 | -| Table | 82 | 83 | 82 | 82 | -| Text | 85 | 84 | 84 | 84 | -| Title | 77 | Sec.-h. | Sec.-h. | Sec.-h. | -| Overall | 72 | 73 | 78 | 77 | +| Class-count | 11 | 6 | 5 | 4 | +|----------------|------|---------|---------|---------| +| Caption | 68 | Text | Text | Text | +| Footnote | 71 | Text | Text | Text | +| Formula | 60 | Text | Text | Text | +| List-item | 81 | Text | 82 | Text | +| Page-footer | 62 | 62 | - | - | +| Page-header | 72 | 68 | - | - | +| Picture | 72 | 72 | 72 | 72 | +| Section-header | 68 | 67 | 69 | 68 | +| Table | 82 | 83 | 82 | 82 | +| Text | 85 | 84 | 84 | 84 | +| Title | 77 | Sec.-h. | Sec.-h. | Sec.-h. | +| Overall | 72 | 73 | 78 | 77 | ## Learning Curve @@ -221,25 +225,25 @@ One of the fundamental questions related to any dataset is if it is "large enoug ## Impact of Class Labels -The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption→Text) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However - -document-wise and page-wise split for different label sets. Naive page-wise split will result in ~10% point improvement. - -| Class-count | 11 | 11 | 5 | 5 | -| - | - | - | - | - | -| Split | Doc | Page | Doc | Page | -| Caption | 68 | 83 | | | -| Footnote | 71 | 84 | | | -| Formula | 60 | 66 | | | -| List-item | 81 | 88 | 82 | 88 | -| Page-footer | 62 | 89 | | | -| Page-header | 72 | 90 | | | -| Picture | 72 | 82 | 72 | 82 | -| Section-header | 68 | 83 | 69 | 83 | -| Table | 82 | 89 | 82 | 90 | -| Text | 85 | 91 | 84 | 90 | -| Title | 77 | 81 | | | -| All | 72 | 84 | 78 | 87 | +The choice and number of labels can have a significant effect on the overall model performance. Since PubLayNet, DocBank and DocLayNet all have different label sets, it is of particular interest to understand and quantify this influence of the label set on the model performance. We investigate this by either down-mapping labels into more common ones (e.g. Caption → Text ) or excluding them from the annotations entirely. Furthermore, it must be stressed that all mappings and exclusions were performed on the data before model training. In Table 3, we present the mAP scores for a Mask R-CNN R50 network on different label sets. Where a label is down-mapped, we show its corresponding label, otherwise it was excluded. We present three different label sets, with 6, 5 and 4 different labels respectively. The set of 5 labels contains the same labels as PubLayNet. However, due to the different definition of + +Table 4: Performance of a Mask R-CNN R50 network with document-wise and page-wise split for different label sets. ~ Naive page-wise split will result in 10% point improvement. + +| Class-count | 11 | 11 | 5 | 5 | +|----------------|------|------|-----|------| +| Split | Doc | Page | Doc | Page | +| Caption | 68 | 83 | | | +| Footnote | 71 | 84 | | | +| Formula | 60 | 66 | | | +| List-item | 81 | 88 | 82 | 88 | +| Page-footer | 62 | 89 | | | +| Page-header | 72 | 90 | | | +| Picture | 72 | 82 | 72 | 82 | +| Section-header | 68 | 83 | 69 | 83 | +| Table | 82 | 89 | 82 | 90 | +| Text | 85 | 91 | 84 | 90 | +| Title | 77 | 81 | | | +| All | 72 | 84 | 78 | 87 | lists in PubLayNet (grouped list-items) versus DocLayNet (separate list-items), the label set of size 4 is the closest to PubLayNet, in the assumption that the List is down-mapped to Text in PubLayNet. The results in Table 3 show that the prediction accuracy on the remaining class labels does not change significantly when other classes are merged into them. The overall macro-average improves by around 5%, in particular when Page-footer and Page-header are excluded. @@ -249,36 +253,38 @@ Many documents in DocLayNet have a unique styling. In order to avoid overfitting ## Dataset Comparison -Throughout this paper, we claim that DocLayNet's wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture, +Throughout this paper, we claim that DocLayNet's wider variety of document layouts leads to more robust layout detection models. In Table 5, we provide evidence for that. We trained models on each of the available datasets (PubLayNet, DocBank and DocLayNet) and evaluated them on the test sets of the other datasets. Due to the different label sets and annotation styles, a direct comparison is not possible. Hence, we focussed on the common labels among the datasets. Between PubLayNet and DocLayNet, these are Picture , -KDD'22, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar +KDD '22, August 14-18, 2022, Washington, DC, USA Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar -Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network across the PubLayNet, DocBank & DocLayNet data-sets. By evaluating on common label classes of each dataset, we observe that the DocLayNet-trained model has much less pronounced variations in performance across all datasets. +Table 5: Prediction Performance (mAP@0.5-0.95) of a Mask R-CNN R50 network across the PubLayNet, DocBank & DocLayNet data-sets. By evaluating on common label classes of each dataset, we observe that the DocLayNet-trained model has much less pronounced variations in performance across all datasets. -| | | Testing on | Testing on | Testing on | -| - | - | - | - | - | -| Training on | labels | PLN | DB | DLN | -| PubLayNet (PLN) confidence.6 | Figure | 96 | 43 | 23 | -| PubLayNet (PLN) confidence.6 | Sec-header | 87 | - | 32 | -| | Table | 95 | 24 | 49 | -| | Text | 96 | - | 42 | -| | total | 93 | 34 | 30 | -| DocBank (DB) | Figure | 77 | 71 | 31 | -| DocBank (DB) | Table | 19 | 65 | 22 | -| DocBank (DB) | total | 48 | 68 | 27 | -| DocLayNet (DLN) | Figure | 67 | 51 | 72 | -| DocLayNet (DLN) | Sec-header | 53 | - | 68 | -| | Table | 87 | 43 | 82 | -| | Text | 77 | - | 84 | -| | total | 59 | 47 | 78 | +| | | Testing on | Testing on | Testing on | +|------------------------------|------------|--------------|--------------|--------------| +| Training on | labels | PLN | DB | DLN | +| PubLayNet (PLN) confidence.6 | Figure | 96 | 43 | 23 | +| PubLayNet (PLN) confidence.6 | Sec-header | 87 | - | 32 | +| | Table | 95 | 24 | 49 | +| | Text | 96 | - | 42 | +| | total | 93 | 34 | 30 | +| DocBank (DB) | Figure | 77 | 71 | 31 | +| DocBank (DB) | Table | 19 | 65 | 22 | +| DocBank (DB) | total | 48 | 68 | 27 | +| DocLayNet (DLN) | Figure | 67 | 51 | 72 | +| DocLayNet (DLN) | Sec-header | 53 | - | 68 | +| | Table | 87 | 43 | 82 | +| | Text | 77 | - | 84 | +| | total | 59 | 47 | 78 | -Section-header, Table and Text. Before training, we either mapped or excluded DocLayNet's other labels as specified in table 3, and also PubLayNet's List to Text. Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text. +Section-header , Table and Text . Before training, we either mapped or excluded DocLayNet's other labels as specified in table 3, and also PubLayNet's List to Text . Note that the different clustering of lists (by list-element vs. whole list objects) naturally decreases the mAP score for Text . For comparison of DocBank with DocLayNet, we trained only on Picture and Table clusters of each dataset. We had to exclude Text because successive paragraphs are often grouped together into a single object in DocBank. This paragraph grouping is incompatible with the individual paragraphs of DocLayNet. As can be seen in Table 5, DocLayNet trained models yield better performance compared to the previous datasets. It is noteworthy that the models trained on PubLayNet and DocBank perform very well on their own test set, but have a much lower performance on the foreign datasets. While this also applies to DocLayNet, the difference is far less pronounced. Thus we conclude that DocLayNet trained models are overall more robust and will produce better results for challenging, unseen layouts. ## Example Predictions -To conclude this section, we illustrate the quality of layout predictions one can expect from DocLayNet-trained models by providing a selection of examples without any further post-processing applied. Figure 6 shows selected layout predictions on pages from the test-set of DocLayNet. Results look decent in general across document categories, however one can also observe mistakes such as overlapping clusters of different classes, or entirely missing boxes +To conclude this section, we illustrate the quality of layout predictions one can expect from DocLayNet-trained models by providing a selection of examples without any further post-processing applied. Figure 6 shows selected layout predictions on pages from the test-set of DocLayNet. Results look decent in general across document categories, however one can also observe mistakes such as overlapping clusters of different classes, or entirely missing boxes due to low confidence. + +## 6 CONCLUSION In this paper, we presented the DocLayNet dataset. It provides the document conversion and layout analysis research community a new and challenging dataset to improve and fine-tune novel ML methods on. In contrast to many other datasets, DocLayNet was created by human annotation in order to obtain reliable layout ground-truth on a wide variety of publication- and typesettingstyles. Including a large proportion of documents outside the scientific publishing domain adds significant value in this respect. @@ -288,18 +294,18 @@ To date, there is still a significant gap between human and ML accuracy on the l ## REFERENCES -- [1] Max Göbel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. Icdar 2013 table competition. In 201312th International Conference on Document Analysis and Recognition, pages 1449-1453, 2013. -- [2] Christian Clausner, Apostolos Antonacopoulos, and Stefan Pletschacher. Icdar2017 competition on recognition of documents with complex layouts - rdcl2017. In 201714th IAPR International Conference on Document Analysis and Recognition (ICDAR), volume 01, pages 1404-1410, 2017. +- [1] Max Göbel, Tamir Hassan, Ermelinda Oro, and Giorgio Orsi. Icdar 2013 table competition. 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IEEE Computer Society, Oct 2017. - [13] Glenn Jocher, Alex Stoken, Ayush Chaurasia, Jirka Borovec, NanoCode012, TaoXie, Yonghye Kwon, Kalen Michael, Liu Changyu, Jiacong Fang, Abhiram V, Laughing, tkianai, yxNONG, Piotr Skalski, Adam Hogan, Jebastin Nadar, imyhxy, Lorenzo Mammana, Alex Wang, Cristi Fati, Diego Montes, Jan Hajek, Laurentiu Figure 6: Example layout predictions on selected pages from the DocLayNet test-set. (A, D) exhibit favourable results on coloured backgrounds. (B, C) show accurate list-item and paragraph differentiation despite densely-spaced lines. (E) demonstrates good table and figure distinction. (F) shows predictions on a Chinese patent with multiple overlaps, label confusion and missing boxes. @@ -310,13 +316,13 @@ Text Caption List-Item Formula Table Picture Section-Header Page-Header Page-Foo Diaconu, Mai Thanh Minh, Marc, albinxavi, fatih, oleg, and wanghao yang. ultralytics/yolov5: v6.0 - yolov5n nano models, roboflow integration, tensorflow export, opencv dnn support, October 2021. -- [14] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier , Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. CoRR, abs/2005.12872, 2020. -- [15] Mingxing Tan, Ruoming Pang, and Quoc V. Le. Efficientdet: Scalable and efficient object detection. CoRR, abs/1911.09070, 2019. +- [14] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. CoRR , abs/2005.12872, 2020. +- [15] Mingxing Tan, Ruoming Pang, and Quoc V. Le. 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In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD, pages 1192-1200, New York, USA, 2020. Asso -- Fusion of visual and text features for document layout analysis, 2021. +- [18] Nikolaos Livathinos, Cesar Berrospi, Maksym Lysak, Viktor Kuropiatnyk, Ahmed Nassar, Andre Carvalho, Michele Dolfi, Christoph Auer, Kasper Dinkla, and Peter W. J. Staar. Robust pdf document conversion using recurrent neural networks. In Proceedings ofthe 35th Conference on Artificial Intelligence , AAAI, pages 15137- 15145, feb 2021. +- [19] Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, and Ming Zhou. Layoutlm: Pre-training of text and layout for document image understanding. In Proceedings ofthe 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , KDD, pages 1192-1200, New York, USA, 2020. Association for Computing Machinery. +- [20] Shoubin Li, Xuyan Ma, Shuaiqun Pan, Jun Hu, Lin Shi, and Qing Wang. Vtlayout: Fusion of visual and text features for document layout analysis, 2021. - [21] Peng Zhang, Can Li, Liang Qiao, Zhanzhan Cheng, Shiliang Pu, Yi Niu, and Fei Wu. Vsr: A unified framework for document layout analysis combining vision, semantics and relations, 2021. -- [22] Peter W J Staar, Michele Dolfi, Christoph Auer, and Costas Bekas. Corpus conversion service: A machine learning platform to ingest documents at scale. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD, pages 774-782. ACM, 2018. -- [23] Connor Shorten and Taghi M. Khoshgoftaar. A survey on image data augmentation for deep learning. Journal of Big Data, 6(1):60, 2019. +- [22] Peter W J Staar, Michele Dolfi, Christoph Auer, and Costas Bekas. Corpus conversion service: A machine learning platform to ingest documents at scale. In Proceedings ofthe 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , KDD, pages 774-782. ACM, 2018. +- [23] Connor Shorten and Taghi M. Khoshgoftaar. A survey on image data augmentation for deep learning. Journal ofBig Data , 6(1):60, 2019. diff --git a/tests/pdf_snapshots/pdf/sources/2305.03393v1-pg9.pdf.md b/tests/pdf_snapshots/pdf/sources/2305.03393v1-pg9.pdf.md index b75b128e..55ca4de2 100644 --- a/tests/pdf_snapshots/pdf/sources/2305.03393v1-pg9.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/2305.03393v1-pg9.pdf.md @@ -6,13 +6,13 @@ We have chosen the PubTabNet data set to perform HPO, since it includes a highly Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart. -| # enc-layers | # dec-layers | Language | TEDs | TEDs | TEDs | mAP (0.75) | Inference time (secs) | -| - | - | - | - | - | - | - | - | -| # enc-layers | # dec-layers | Language | simple | complex | all | mAP (0.75) | Inference time (secs) | -| 6 | 6 | OTSL HTML | 0.965 0.969 | 0.934 0.927 | 0.955 0.955 | 0.88 0.857 | 2.73 5.39 | -| 4 | 4 | OTSL HTML | 0.938 0.952 | 0.904 0.909 | 0.927 0.938 | 0.853 0.843 | 1.97 3.77 | -| 2 | 4 | OTSL HTML | 0.923 0.945 | 0.897 0.901 | 0.915 0.931 | 0.859 0.834 | 1.91 3.81 | -| 4 | 2 | OTSL HTML | 0.952 0.944 | 0.92 0.903 | 0.942 0.931 | 0.857 0.824 | 1.22 2 | +| # enc-layers | # dec-layers | Language | TEDs | TEDs | TEDs | mAP (0.75) | Inference time (secs) | +|----------------|----------------|------------|-------------|-------------|-------------|--------------|-------------------------| +| # enc-layers | # dec-layers | Language | simple | complex | all | mAP (0.75) | Inference time (secs) | +| 6 | 6 | OTSL HTML | 0.965 0.969 | 0.934 0.927 | 0.955 0.955 | 0.88 0.857 | 2.73 5.39 | +| 4 | 4 | OTSL HTML | 0.938 0.952 | 0.904 0.909 | 0.927 0.938 | 0.853 0.843 | 1.97 3.77 | +| 2 | 4 | OTSL HTML | 0.923 0.945 | 0.897 0.901 | 0.915 0.931 | 0.859 0.834 | 1.91 3.81 | +| 4 | 2 | OTSL HTML | 0.952 0.944 | 0.92 0.903 | 0.942 0.931 | 0.857 0.824 | 1.22 2 | ## 5.2 Quantitative Results diff --git a/tests/pdf_snapshots/pdf/sources/2305.03393v1.pdf.md b/tests/pdf_snapshots/pdf/sources/2305.03393v1.pdf.md index 1f34d900..f23a5b9e 100644 --- a/tests/pdf_snapshots/pdf/sources/2305.03393v1.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/2305.03393v1.pdf.md @@ -1,8 +1,8 @@ ## Optimized Table Tokenization for Table Structure Recognition -Maksym Lysak[0000−0002−3723−6960], Ahmed Nassar[0000−0002−9468−0822], Nikolaos Livathinos[0000−0001−8513−3491], Christoph Auer[0000−0001−5761−0422], and Peter Staar[0000−0002−8088−0823] +Maksym Lysak [0000 - 0002 - 3723 - 6960] , Ahmed Nassar [0000 - 0002 - 9468 - 0822] , Nikolaos Livathinos [0000 - 0001 - 8513 - 3491] , Christoph Auer [0000 - 0001 - 5761 - 0422] , and Peter Staar [0000 - 0002 - 8088 - 0823] -IBM Research {mly, ahn,nli, cau, taa}@zurich.ibm.com +IBM Research {mly,ahn,nli,cau,taa}@zurich.ibm.com Abstract. Extracting tables from documents is a crucial task in any document conversion pipeline. Recently, transformer-based models have demonstrated that table-structure can be recognized with impressive accuracy using Image-to-Markup-Sequence (Im2Seq) approaches. Taking only the image of a table, such models predict a sequence of tokens (e.g. in HTML, LaTeX) which represent the structure of the table. Since the token representation of the table structure has a significant impact on the accuracy and run-time performance of any Im2Seq model, we investigate in this paper how table-structure representation can be optimised. We propose a new, optimised table-structure language (OTSL) with a minimized vocabulary and specific rules. The benefits of OTSL are that it reduces the number of tokens to 5 (HTML needs 28+) and shortens the sequence length to half of HTML on average. Consequently, model accuracy improves significantly, inference time is halved compared to HTML-based models, and the predicted table structures are always syntactically correct. This in turn eliminates most post-processing needs. Popular table structure data-sets will be published in OTSL format to the community. @@ -12,7 +12,7 @@ Keywords: Table Structure Recognition · Data Representation · Transformers · Tables are ubiquitous in documents such as scientific papers, patents, reports, manuals, specification sheets or marketing material. They often encode highly valuable information and therefore need to be extracted with high accuracy. Unfortunately, tables appear in documents in various sizes, styling and structure, making it difficult to recover their correct structure with simple analytical methods. Therefore, accurate table extraction is achieved these days with machine-learning based methods. -In modern document understanding systems [1,15], table extraction is typically a two-step process. Firstly, every table on a page is located with a bounding +In modern document understanding systems [1,15], table extraction is typically a two-step process. Firstly, every table on a page is located with a bounding box, and secondly, their logical row and column structure is recognized. As of Fig. 1. Comparison between HTML and OTSL table structure representation: (A) table-example with complex row and column headers, including a 2D empty span, (B) minimal graphical representation of table structure using rectangular layout, (C) HTML representation, (D) OTSL representation. This example demonstrates many of the key-features of OTSL, namely its reduced vocabulary size (12 versus 5 in this case), its reduced sequence length (55 versus 30) and a enhanced internal structure (variable token sequence length per row in HTML versus a fixed length of rows in OTSL). @@ -34,27 +34,23 @@ Approaches to formalize the logical structure and layout of tables in electronic Other work [20] aims at predicting a grid for each table and deciding which cells must be merged using an attention network. Im2Seq methods cast the problem as a sequence generation task [4,5,9,22], and therefore need an internal tablestructure representation language, which is often implemented with standard markup languages (e.g. HTML, LaTeX, Markdown). In theory, Im2Seq methods have a natural advantage over the OD and GNN methods by virtue of directly predicting the table-structure. As such, no post-processing or rules are needed in order to obtain the table-structure, which is necessary with OD and GNN approaches. In practice, this is not entirely true, because a predicted sequence of table-structure markup does not necessarily have to be syntactically correct. Hence, depending on the quality of the predicted sequence, some post-processing needs to be performed to ensure a syntactically valid (let alone correct) sequence. -Within the Im2Seq method, we find several popular models, namely the encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders to predict a table in HTML representation. The tag decoder predicts a sequence of HTML tags. For each decoded table cell (), the attention is passed to the cell decoder to predict the content with an embedded OCR approach. The latter makes it susceptible to transcription errors in the cell content of the table. TableFormer address this reliance on OCR and uses two transformer decoders for HTML structure and cell bounding box prediction in an end-to-end architecture. The predicted cell bounding box is then used to extract text tokens from an originating (digital) PDF page, circumventing any need for OCR. TabSplitter [2] proposes a compact double-matrix representation of table rows and columns to do error detection and error correction of HTML structure sequences based on predictions from [19]. This compact double-matrix representation can not be used directly by the Img2seq model training, so the model uses HTML as an intermediate form. Chi et. al. [4] introduce a data set and a baseline method using bidirectional LSTMs to predict LaTeX code. Kayal [5] introduces Gated ResNet transformers to predict LaTeX code, and a separate OCR module to extract content. +Within the Im2Seq method, we find several popular models, namely the encoder-dual-decoder model (EDD) [22], TableFormer [9], Tabsplitter[2] and Ye et. al. [19]. EDD uses two consecutive long short-term memory (LSTM) decoders to predict a table in HTML representation. The tag decoder predicts a sequence of HTML tags. For each decoded table cell ( <td> ), the attention is passed to the cell decoder to predict the content with an embedded OCR approach. The latter makes it susceptible to transcription errors in the cell content of the table. TableFormer address this reliance on OCR and uses two transformer decoders for HTML structure and cell bounding box prediction in an end-to-end architecture. The predicted cell bounding box is then used to extract text tokens from an originating (digital) PDF page, circumventing any need for OCR. TabSplitter [2] proposes a compact double-matrix representation of table rows and columns to do error detection and error correction of HTML structure sequences based on predictions from [19]. This compact double-matrix representation can not be used directly by the Img2seq model training, so the model uses HTML as an intermediate form. Chi et. al. [4] introduce a data set and a baseline method using bidirectional LSTMs to predict LaTeX code. Kayal [5] introduces Gated ResNet transformers to predict LaTeX code, and a separate OCR module to extract content. Im2Seq approaches have shown to be well-suited for the TSR task and allow a full end-to-end network design that can output the final table structure without pre- or post-processing logic. Furthermore, Im2Seq models have demonstrated to deliver state-of-the-art prediction accuracy [9]. This motivated the authors to investigate if the performance (both in accuracy and inference time) can be further improved by optimising the table structure representation language. We believe this is a necessary step before further improving neural network architectures for this task. ## 3 Problem Statement -All known Im2Seq based models for TSR fundamentally work in similar ways. Given an image of a table, the Im2Seq model predicts the structure of the table by generating a sequence of tokens. These tokens originate from a finite vocab- - -ulary and can be interpreted as a table structure. For example, with the HTML tokens ,
, , , and , one can construct simple table structures without any spanning cells. In reality though, one needs at least 28 HTML tokens to describe the most common complex tables observed in real-world documents [21,22], due to a variety of spanning cells definitions in the HTML token vocabulary. +All known Im2Seq based models for TSR fundamentally work in similar ways. Given an image of a table, the Im2Seq model predicts the structure of the table by generating a sequence of tokens. These tokens originate from a finite vocab- ulary and can be interpreted as a table structure. For example, with the HTML tokens <table> , </table> , <tr> , </tr> , <td> and </td> , one can construct simple table structures without any spanning cells. In reality though, one needs at least 28 HTML tokens to describe the most common complex tables observed in real-world documents [21,22], due to a variety of spanning cells definitions in the HTML token vocabulary. Fig. 2. Frequency of tokens in HTML and OTSL as they appear in PubTabNet. -Obviously, HTML and other general-purpose markup languages were not designed for Im2Seq models. As such, they have some serious drawbacks. First, the token vocabulary needs to be artificially large in order to describe all plausible tabular structures. Since most Im2Seq models use an autoregressive approach, they generate the sequence token by token. Therefore, to reduce inference time, a shorter sequence length is critical. Every table-cell is represented by at least two tokens ( and ). Furthermore, when tokenizing the HTML structure, one needs to explicitly enumerate possible column-spans and row-spans as words. In practice, this ends up requiring 28 different HTML tokens (when including column- and row-spans up to 10 cells) just to describe every table in the PubTabNet dataset. Clearly, not every token is equally represented, as is depicted in Figure 2. This skewed distribution of tokens in combination with variable token row-length makes it challenging for models to learn the HTML structure. +Obviously, HTML and other general-purpose markup languages were not designed for Im2Seq models. As such, they have some serious drawbacks. First, the token vocabulary needs to be artificially large in order to describe all plausible tabular structures. Since most Im2Seq models use an autoregressive approach, they generate the sequence token by token. Therefore, to reduce inference time, a shorter sequence length is critical. Every table-cell is represented by at least two tokens ( <td> and </td> ). Furthermore, when tokenizing the HTML structure, one needs to explicitly enumerate possible column-spans and row-spans as words. In practice, this ends up requiring 28 different HTML tokens (when including column- and row-spans up to 10 cells) just to describe every table in the PubTabNet dataset. Clearly, not every token is equally represented, as is depicted in Figure 2. This skewed distribution of tokens in combination with variable token row-length makes it challenging for models to learn the HTML structure. Additionally, it would be desirable if the representation would easily allow an early detection of invalid sequences on-the-go, before the prediction of the entire table structure is completed. HTML is not well-suited for this purpose as the verification of incomplete sequences is non-trivial or even impossible. -In a valid HTML table, the token sequence must describe a 2D grid of table cells, serialised in row-major ordering, where each row and each column have the same length (while considering row- and column-spans). Furthermore, every opening tag in HTML needs to be matched by a closing tag in a correct hierarchical manner. Since the number of tokens for each table row and column can vary significantly, especially for large tables with many row- and column-spans, it is complex to verify the consistency of predicted structures during sequence - -generation. Implicitly, this also means that Im2Seq models need to learn these complex syntax rules, simply to deliver valid output. +In a valid HTML table, the token sequence must describe a 2D grid of table cells, serialised in row-major ordering, where each row and each column have the same length (while considering row- and column-spans). Furthermore, every opening tag in HTML needs to be matched by a closing tag in a correct hierarchical manner. Since the number of tokens for each table row and column can vary significantly, especially for large tables with many row- and column-spans, it is complex to verify the consistency of predicted structures during sequence generation. Implicitly, this also means that Im2Seq models need to learn these complex syntax rules, simply to deliver valid output. In practice, we observe two major issues with prediction quality when training Im2Seq models on HTML table structure generation from images. On the one hand, we find that on large tables, the visual attention of the model often starts to drift and is not accurately moving forward cell by cell anymore. This manifests itself in either in an increasing location drift for proposed table-cells in later rows on the same column or even complete loss of vertical alignment, as illustrated in Figure 5. Addressing this with post-processing is partially possible, but clearly undesired. On the other hand, we find many instances of predictions with structural inconsistencies or plain invalid HTML output, as shown in Figure 6, which are nearly impossible to properly correct. Both problems seriously impact the TSR model performance, since they reflect not only in the task of pure structure recognition but also in the equally crucial recognition or matching of table cell content. @@ -69,10 +65,10 @@ In Figure 3, we illustrate how the OTSL is defined. In essence, the OTSL defines The OTSL vocabulary is comprised of the following tokens: - "C" cell - a new table cell that either has or does not have cell content -- "L" cell - left-looking cell, merging with the left neighbor cell to create a span -- "U" cell - up-looking cell, merging with the upper neighbor cell to create a span -- "X" cell - cross cell, to merge with both left and upper neighbor cells -- "NL" - new-line, switch to the next row. +- "L" cell - left-looking cell , merging with the left neighbor cell to create a span +- "U" cell - up-looking cell , merging with the upper neighbor cell to create a span +- "X" cell - cross cell , to merge with both left and upper neighbor cells +- "NL" - new-line , switch to the next row. A notable attribute of OTSL is that it has the capability of achieving lossless conversion to HTML. @@ -84,21 +80,19 @@ Fig. 3. OTSL description of table structure: A - table example; B - graphical re The OTSL representation follows these syntax rules: -- 1. Left-looking cell rule: The left neighbour of an "L" cell must be either another "L" cell or a "C" cell. -- 2. Up-looking cell rule: The upper neighbour of a "U" cell must be either another "U" cell or a "C" cell. -- 3. Cross cell rule: +1. Left-looking cell rule : The left neighbour of an "L" cell must be either another "L" cell or a "C" cell. +2. Up-looking cell rule : The upper neighbour of a "U" cell must be either another "U" cell or a "C" cell. +3. Cross cell rule : The left neighbour of an "X" cell must be either another "X" cell or a "U" cell, and the upper neighbour of an "X" cell must be either another "X" cell or an "L" cell. -- 4. First row rule: Only "L" cells and "C" cells are allowed in the first row. -- 5. First column rule: Only "U" cells and "C" cells are allowed in the first column. -- 6. Rectangular rule: The table representation is always rectangular - all rows must have an equal number of tokens, terminated with "NL" token. +4. First row rule : Only "L" cells and "C" cells are allowed in the first row. +5. First column rule : Only "U" cells and "C" cells are allowed in the first column. +6. Rectangular rule : The table representation is always rectangular - all rows must have an equal number of tokens, terminated with "NL" token. The application of these rules gives OTSL a set of unique properties. First of all, the OTSL enforces a strictly rectangular structure representation, where every new-line token starts a new row. As a consequence, all rows and all columns have exactly the same number of tokens, irrespective of cell spans. Secondly, the OTSL representation is unambiguous: Every table structure is represented in one way. In this representation every table cell corresponds to a "C"-cell token, which in case of spans is always located in the top-left corner of the table cell definition. Third, OTSL syntax rules are only backward-looking. As a consequence, every predicted token can be validated straight during sequence generation by looking at the previously predicted sequence. As such, OTSL can guarantee that every predicted sequence is syntactically valid. -These characteristics can be easily learned by sequence generator networks, as we demonstrate further below. We find strong indications that this pattern - -reduces significantly the column drift seen in the HTML based models (see Figure 5). +These characteristics can be easily learned by sequence generator networks, as we demonstrate further below. We find strong indications that this pattern reduces significantly the column drift seen in the HTML based models (see Figure 5). ## 4.3 Error-detection and -mitigation @@ -112,9 +106,7 @@ Fig. 4. Architecture sketch of the TableFormer model, which is a representative -We rely on standard metrics such as Tree Edit Distance score (TEDs) for table structure prediction, and Mean Average Precision (mAP) with 0.75 Intersection Over Union (IOU) threshold for the bounding-box predictions of table cells. The predicted OTSL structures were converted back to HTML format in - -order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz. +We rely on standard metrics such as Tree Edit Distance score (TEDs) for table structure prediction, and Mean Average Precision (mAP) with 0.75 Intersection Over Union (IOU) threshold for the bounding-box predictions of table cells. The predicted OTSL structures were converted back to HTML format in order to compute the TED score. Inference timing results for all experiments were obtained from the same machine on a single core with AMD EPYC 7763 CPU @2.45 GHz. ## 5.1 Hyper Parameter Optimization @@ -122,13 +114,13 @@ We have chosen the PubTabNet data set to perform HPO, since it includes a highly Table 1. HPO performed in OTSL and HTML representation on the same transformer-based TableFormer [9] architecture, trained only on PubTabNet [22]. Effects of reducing the # of layers in encoder and decoder stages of the model show that smaller models trained on OTSL perform better, especially in recognizing complex table structures, and maintain a much higher mAP score than the HTML counterpart. -| # enc-layers | # dec-layers | Language | TEDs | TEDs | TEDs | mAP (0.75) | Inference time (secs) | -| - | - | - | - | - | - | - | - | -| # enc-layers | # dec-layers | Language | simple | complex | all | mAP (0.75) | Inference time (secs) | -| 6 | 6 | OTSL HTML | 0.965 0.969 | 0.934 0.927 | 0.955 0.955 | 0.88 0.857 | 2.73 5.39 | -| 4 | 4 | OTSL HTML | 0.938 0.952 | 0.904 0.909 | 0.927 0.938 | 0.853 0.843 | 1.97 3.77 | -| 2 | 4 | OTSL HTML | 0.923 0.945 | 0.897 0.901 | 0.915 0.931 | 0.859 0.834 | 1.91 3.81 | -| 4 | 2 | OTSL HTML | 0.952 0.944 | 0.92 0.903 | 0.942 0.931 | 0.857 0.824 | 1.22 2 | +| # enc-layers | # dec-layers | Language | TEDs | TEDs | TEDs | mAP (0.75) | Inference time (secs) | +|----------------|----------------|------------|-------------|-------------|-------------|--------------|-------------------------| +| # enc-layers | # dec-layers | Language | simple | complex | all | mAP (0.75) | Inference time (secs) | +| 6 | 6 | OTSL HTML | 0.965 0.969 | 0.934 0.927 | 0.955 0.955 | 0.88 0.857 | 2.73 5.39 | +| 4 | 4 | OTSL HTML | 0.938 0.952 | 0.904 0.909 | 0.927 0.938 | 0.853 0.843 | 1.97 3.77 | +| 2 | 4 | OTSL HTML | 0.923 0.945 | 0.897 0.901 | 0.915 0.931 | 0.859 0.834 | 1.91 3.81 | +| 4 | 2 | OTSL HTML | 0.952 0.944 | 0.92 0.903 | 0.942 0.931 | 0.857 0.824 | 1.22 2 | ## 5.2 Quantitative Results @@ -138,22 +130,22 @@ Additionally, the results show that OTSL has an advantage over HTML when applied Table 2. TSR and cell detection results compared between OTSL and HTML on the PubTabNet [22], FinTabNet [21] and PubTables-1M [14] data sets using TableFormer [9] (with enc=6, dec=6, heads=8). -| Data set | Language | TEDs | TEDs | TEDs | mAP(0.75) | Inference time (secs) | -| - | - | - | - | - | - | - | -| Data set | Language | simple | complex | all | mAP(0.75) | Inference time (secs) | -| PubTabNet | OTSL HTML | 0.965 0.969 | 0.934 0.927 | 0.955 0.955 | 0.88 0.857 | 2.73 5.39 | -| FinTabNet | OTSL HTML | 0.955 0.917 | 0.961 0.922 | 0.959 0.92 | 0.862 0.722 | 1.85 3.26 | -| PubTables-1M | OTSL HTML | 0.987 0.983 | 0.964 0.944 | 0.977 0.966 | 0.896 0.889 | 1.79 3.26 | +| Data set | Language | TEDs | TEDs | TEDs | mAP(0.75) | Inference time (secs) | +|--------------|------------|-------------|-------------|-------------|-------------|-------------------------| +| Data set | Language | simple | complex | all | mAP(0.75) | Inference time (secs) | +| PubTabNet | OTSL HTML | 0.965 0.969 | 0.934 0.927 | 0.955 0.955 | 0.88 0.857 | 2.73 5.39 | +| FinTabNet | OTSL HTML | 0.955 0.917 | 0.961 0.922 | 0.959 0.92 | 0.862 0.722 | 1.85 3.26 | +| PubTables-1M | OTSL HTML | 0.987 0.983 | 0.964 0.944 | 0.977 0.966 | 0.896 0.889 | 1.79 3.26 | ## 5.3 Qualitative Results To illustrate the qualitative differences between OTSL and HTML, Figure 5 demonstrates less overlap and more accurate bounding boxes with OTSL. In Figure 6, OTSL proves to be more effective in handling tables with longer token sequences, resulting in even more precise structure prediction and bounding boxes. -Fig. 5. The OTSL model produces more accurate bounding boxes with less overlap (E) than the HTML model (D), when predicting the structure of a sparse table (A), at twice the inference speed because of shorter sequence length (B),(C). "PMC2807444_006_00.png" PubTabNet. +Fig. 5. The OTSL model produces more accurate bounding boxes with less overlap (E) than the HTML model (D), when predicting the structure of a sparse table (A), at twice the inference speed because of shorter sequence length (B),(C). "PMC2807444\_006\_00.png" PubTabNet. μ -Fig. 6. Visualization of predicted structure and detected bounding boxes on a complex table with many rows. The OTSL model (B) captured repeating pattern of horizontally merged cells from the GT (A), unlike the HTML model (C). The HTML model also didn't complete the HTML sequence correctly and displayed a lot more of drift and overlap of bounding boxes. "PMC5406406_003_01.png" PubTabNet. +Fig. 6. Visualization of predicted structure and detected bounding boxes on a complex table with many rows. The OTSL model (B) captured repeating pattern of horizontally merged cells from the GT (A), unlike the HTML model (C). The HTML model also didn't complete the HTML sequence correctly and displayed a lot more of drift and overlap of bounding boxes. "PMC5406406\_003\_01.png" PubTabNet. @@ -167,26 +159,26 @@ Secondly, OTSL has more inherent structure and a significantly restricted vocabu ## References -- 1. Auer, C., Dolfi, M., Carvalho, A., Ramis, C.B., Staar, P.W.J.: Delivering document conversion as a cloud service with high throughput and responsiveness. CoRR abs/2206.00785 (2022). https : //doi.org/10.48550/arXiv.2206.00785, https : //doi.org/10.48550/arXiv.2206.00785 -- 2. 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Siddiqui, S.A., Fateh, I.A., Rizvi, S.T.R., Dengel, A., Ahmed, S.: Deeptabstr: Deep learning based table structure recognition. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 1403-1409 (2019). https:// doi.org/10.1109/ICDAR.2019.00226 +14. Smock, B., Pesala, R., Abraham, R.: PubTables-1M: Towards comprehensive table extraction from unstructured documents. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 4634-4642 (June 2022) +15. Staar, P.W.J., Dolfi, M., Auer, C., Bekas, C.: Corpus conversion service: A machine learning platform to ingest documents at scale. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. pp. 774-782. KDD '18, Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3219819.3219834 , https://doi.org/10. 1145/3219819.3219834 +16. Wang, X.: Tabular Abstraction, Editing, and Formatting. Ph.D. thesis, CAN (1996), aAINN09397 +17. Xue, W., Li, Q., Tao, D.: Res2tim: Reconstruct syntactic structures from table images. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 749-755. IEEE (2019) +18. Xue, W., Yu, B., Wang, W., Tao, D., Li, Q.: Tgrnet: A table graph reconstruction network for table structure recognition. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 1295-1304 (2021) +19. Ye, J., Qi, X., He, Y., Chen, Y., Gu, D., Gao, P., Xiao, R.: Pingan-vcgroup's solution for icdar 2021 competition on scientific literature parsing task b: Table recognition to html (2021). https://doi.org/10.48550/ARXIV.2105.01848 , https://arxiv.org/abs/2105.01848 +20. Zhang, Z., Zhang, J., Du, J., Wang, F.: Split, embed and merge: An accurate table structure recognizer. Pattern Recognition 126 , 108565 (2022) +21. Zheng, X., Burdick, D., Popa, L., Zhong, X., Wang, N.X.R.: Global table extractor (gte): A framework for joint table identification and cell structure recognition using visual context. In: 2021 IEEE Winter Conference on Applications of Computer Vision (WACV). pp. 697-706 (2021). https://doi.org/10.1109/WACV48630.2021. 00074 +22. Zhong, X., ShafieiBavani, E., Jimeno Yepes, A.: Image-based table recognition: Data, model, and evaluation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.M. (eds.) Computer Vision - ECCV 2020. pp. 564-580. Springer International Publishing, Cham (2020) +23. Zhong, X., Tang, J., Yepes, A.J.: Publaynet: largest dataset ever for document layout analysis. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 1015-1022. IEEE (2019) diff --git a/tests/pdf_snapshots/pdf/sources/amt_handbook_sample.pdf.md b/tests/pdf_snapshots/pdf/sources/amt_handbook_sample.pdf.md index 7547c497..35caca77 100644 --- a/tests/pdf_snapshots/pdf/sources/amt_handbook_sample.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/amt_handbook_sample.pdf.md @@ -1,27 +1,31 @@ pulleys, provided the inner race of the bearing is clamped to the supporting structure by the nut and bolt. Plates must be attached to the structure in a positive manner to eliminate rotation or misalignment when tightening the bolts or screws. -The two general types of self-locking nuts currently in use are the all-metal type and the fiber lock type. For the sake of simplicity, only three typical kinds of self-locking nuts are considered in this handbook: the Boots self-locking and the stainless steel self-locking nuts, representing the all-metal types; and the elastic stop nut, representing the fiber insert type. +The two general types of self-locking nuts currently in use are the all-metal type and the fiber lock type. For the sake of simplicity, only three typical kinds of self-locking nuts are considered in this handbook: the Boots self-locking and the stainless steel self-locking nuts, representing the all-metal types; and the elastic stop nut, representing the fiber insert type. ## Boots Self-Locking Nut -The Boots self-locking nut is of one piece, all-metal construction designed to hold tight despite severe vibration. Note in Figure 7-26 that it has two sections and is essentially two nuts in one: a locking nut and a load-carrying nut. The two sections are connected with a spring, which is an integral part of the nut. +The Boots self-locking nut is of one piece, all-metal construction designed to hold tight despite severe vibration. Note in Figure 7-26 that it has two sections and is essentially two nuts in one: a locking nut and a load-carrying nut. The two sections are connected with a spring, which is an integral part of the nut. The spring keeps the locking and load-carrying sections such a distance apart that the two sets of threads are out of phase or spaced so that a bolt, which has been screwed through the load-carrying section, must push the locking section outward against the force of the spring to engage the threads of the locking section properly. The spring, through the medium of the locking section, exerts a constant locking force on the bolt in the same direction as a force that would tighten the nut. In this nut, the load-carrying section has the thread strength of a standard nut of comparable size, while the locking section presses against the threads of the bolt and locks the nut firmly in position. Only a wrench applied to the nut loosens it. The nut can be removed and reused without impairing its efficiency. -Boots self-locking nuts are made with three different spring +Boots self-locking nuts are made with three different spring styles and in various shapes and sizes. The wing type that is + +Figure 7-26. Self-locking nuts. -Rol-top ranges from 1⁄4 inch to 1⁄6 inch, and the bellows type ranges in size from No. 8 up to 3⁄8 inch. Wing-type nuts are made of anodized aluminum alloy, cadmium-plated carbon steel, or stainless steel. The Rol-top nut is cadmium-plated steel, and the bellows type is made of aluminum alloy only. +the most common ranges in size for No. 6 up to 1 / 4 inch, the Rol-top ranges from 1 / 4 inch to 1 / 6 inch, and the bellows type ranges in size from No. 8 up to 3 / 8 inch. Wing-type nuts are made of anodized aluminum alloy, cadmium-plated carbon steel, or stainless steel. The Rol-top nut is cadmium-plated steel, and the bellows type is made of aluminum alloy only. ## Stainless Steel Self-Locking Nut -The stainless steel self-locking nut may be spun on and off by hand as its locking action takes places only when the nut is seated against a solid surface and tightened. The nut consists of two parts: a case with a beveled locking shoulder and key and a thread insert with a locking shoulder and slotted keyway. Until the nut is tightened, it spins on the bolt easily, because the threaded insert is the proper size for the bolt. However, when the nut is seated against a solid surface and tightened, the locking shoulder of the insert is pulled downward and wedged against the locking shoulder of the case. This action compresses the threaded insert and causes it to clench the bolt tightly. The cross-sectional view in Figure 7-27 shows how the key of the case fits into the slotted keyway of the insert so that when the case is turned, the threaded insert is turned with it. Note that the slot is wider than the key. This permits the slot to be narrowed and the insert to be compressed when the nut is tightened. +The stainless steel self-locking nut may be spun on and off by hand as its locking action takes places only when the nut is seated against a solid surface and tightened. The nut consists of two parts: a case with a beveled locking shoulder and key and a thread insert with a locking shoulder and slotted keyway. Until the nut is tightened, it spins on the bolt easily, because the threaded insert is the proper size for the bolt. However, when the nut is seated against a solid surface and tightened, the locking shoulder of the insert is pulled downward and wedged against the locking shoulder of the case. This action compresses the threaded insert and causes it to clench the bolt tightly. The cross-sectional view in Figure 7-27 shows how the key of the case fits into the slotted keyway of the insert so that when the case is turned, the threaded insert is turned with it. Note that the slot is wider than the key. This permits the slot to be narrowed and the insert to be compressed when the nut is tightened. ## Elastic Stop Nut -The elastic stop nut is a standard nut with the height increased to accommodate a fiber locking collar. This +The elastic stop nut is a standard nut with the height increased to accommodate a fiber locking collar. This + +Figure 7-27. Stainless steel self-locking nut. diff --git a/tests/pdf_snapshots/pdf/sources/multi_page.pdf.md b/tests/pdf_snapshots/pdf/sources/multi_page.pdf.md index 280234c1..308d5d47 100644 --- a/tests/pdf_snapshots/pdf/sources/multi_page.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/multi_page.pdf.md @@ -12,8 +12,8 @@ During this period, the term "word processing" didn't exist, but the typewriter The term "word processor" first emerged in the 1960s and referred to any system designed to streamline written communication and document production. Early word processors were not software programs but rather standalone machines. -- IBM MT/ST (Magnetic Tape/Selectric Typewriter): Introduced in 1964, this machine combined IBM's Selectric typewriter with magnetic tape storage. It allowed users to record, edit, and replay typed content-an early example of digital text storage. -- Wang Laboratories: In the 1970s, Wang introduced dedicated word processing machines. These devices, like the Wang 1200, featured small screens and floppy disks, making them revolutionary for their time. +- IBM MT/ST (Magnetic Tape/Selectric Typewriter) : Introduced in 1964, this machine combined IBM's Selectric typewriter with magnetic tape storage. It allowed users to record, edit, and replay typed content-an early example of digital text storage. +- Wang Laboratories : In the 1970s, Wang introduced dedicated word processing machines. These devices, like the Wang 1200, featured small screens and floppy disks, making them revolutionary for their time. These machines were primarily used in offices, where secretarial pools benefited from their ability to make revisions without retyping entire documents. @@ -21,8 +21,8 @@ These machines were primarily used in offices, where secretarial pools benefited The advent of personal computers in the late 1970s and early 1980s transformed word processing from a niche tool to an essential technology for businesses and individuals alike. -- WordStar (1978): Developed for the CP/M operating system, WordStar was one of the first widely used word processing programs. It featured early examples of modern features like cut, copy, and paste. -- Microsoft Word (1983): Microsoft launched Word for MS-DOS in 1983, introducing a graphical user interface (GUI) and mouse support. Over the years, Microsoft Word became the industry standard for word processing. +- WordStar (1978) : Developed for the CP/M operating system, WordStar was one of the first widely used word processing programs. It featured early examples of modern features like cut, copy, and paste. +- Microsoft Word (1983) : Microsoft launched Word for MS-DOS in 1983, introducing a graphical user interface (GUI) and mouse support. Over the years, Microsoft Word became the industry standard for word processing. Other notable software from this era included WordPerfect, which was popular among legal professionals, and Apple's MacWrite, which leveraged the Macintosh's graphical capabilities. @@ -30,9 +30,9 @@ Other notable software from this era included WordPerfect, which was popular amo By the 1990s, word processing software had become more sophisticated, with features like spell check, grammar check, templates, and collaborative tools. -- Microsoft Office Suite: Microsoft continued to dominate with its Office Suite, integrating Word with other productivity tools like Excel and PowerPoint. -- OpenOffice and LibreOffice: Open-source alternatives emerged in the early 2000s, offering free and flexible word processing options. -- Google Docs (2006): The introduction of cloud-based word processing revolutionized collaboration. Google Docs enabled real-time editing and sharing, making it a staple for teams and remote work. +- Microsoft Office Suite : Microsoft continued to dominate with its Office Suite, integrating Word with other productivity tools like Excel and PowerPoint. +- OpenOffice and LibreOffice : Open-source alternatives emerged in the early 2000s, offering free and flexible word processing options. +- Google Docs (2006) : The introduction of cloud-based word processing revolutionized collaboration. Google Docs enabled real-time editing and sharing, making it a staple for teams and remote work. ## Future of Word Processing @@ -44,44 +44,44 @@ From the clunky typewriters of the 19th century to the AI-powered cloud tools of In addition to general-purpose word processors, specialized tools have emerged to cater to specific industries and needs. These tools incorporate unique features tailored to their users' workflows: -- Academic and Technical Writing: Tools like LaTeX gained popularity among academics, scientists, and engineers. Unlike traditional word processors, LaTeX focuses on precise formatting, particularly for complex mathematical equations, scientific papers, and technical documents. It relies on a markup language to produce polished documents suitable for publishing. -- Screenwriting Software: For screenwriters, tools like Final Draft and Celtx are specialized to handle scripts for film and television. These programs automate the formatting of dialogue, scene descriptions, and other elements unique to screenwriting. -- Legal Document Processors: Word processors tailored for legal professionals, like WordPerfect, offered features such as redlining (early version tracking) and document comparison. Even today, many law firms rely on these tools due to their robust formatting options for contracts and legal briefs. +- Academic and Technical Writing : Tools like LaTeX gained popularity among academics, scientists, and engineers. Unlike traditional word processors, LaTeX focuses on precise formatting, particularly for complex mathematical equations, scientific papers, and technical documents. It relies on a markup language to produce polished documents suitable for publishing. +- Screenwriting Software : For screenwriters, tools like Final Draft and Celtx are specialized to handle scripts for film and television. These programs automate the formatting of dialogue, scene descriptions, and other elements unique to screenwriting. +- Legal Document Processors : Word processors tailored for legal professionals, like WordPerfect, offered features such as redlining (early version tracking) and document comparison. Even today, many law firms rely on these tools due to their robust formatting options for contracts and legal briefs. ## Key Features That Changed Word Processing The evolution of word processors wasn't just about hardware or software improvements-it was about the features that revolutionized how people wrote and edited. Some of these transformative features include: -- 1. Undo/Redo: Introduced in the 1980s, the ability to undo mistakes and redo actions made experimentation and error correction much easier. -- 2. Spell Check and Grammar Check: By the 1990s, these became standard, allowing users to spot errors automatically. -- 3. Templates: Pre-designed formats for documents, such as resumes, letters, and invoices, helped users save time. -- 4. Track Changes: A game-changer for collaboration, this feature allowed multiple users to suggest edits while maintaining the original text. -- 5. Real-Time Collaboration: Tools like Google Docs and Microsoft 365 enabled multiple users to edit the same document simultaneously, forever changing teamwork dynamics. +1. Undo/Redo : Introduced in the 1980s, the ability to undo mistakes and redo actions made experimentation and error correction much easier. +2. Spell Check and Grammar Check : By the 1990s, these became standard, allowing users to spot errors automatically. +3. Templates : Pre-designed formats for documents, such as resumes, letters, and invoices, helped users save time. +4. Track Changes : A game-changer for collaboration, this feature allowed multiple users to suggest edits while maintaining the original text. +5. Real-Time Collaboration : Tools like Google Docs and Microsoft 365 enabled multiple users to edit the same document simultaneously, forever changing teamwork dynamics. ## The Cultural Impact of Word Processors The word processor didn't just change workplaces-it changed culture. It democratized writing, enabling anyone with access to a computer to produce professional-quality documents. This shift had profound implications for education, business, and creative fields: -- Accessibility: Writers no longer needed expensive publishing equipment or training in typesetting to create polished work. This accessibility paved the way for selfpublishing, blogging, and even fan fiction communities. -- Education: Word processors became a cornerstone of education, teaching students not only how to write essays but also how to use technology effectively. Features like bibliography generators and integrated research tools enhanced learning. -- Creative Writing: Writers gained powerful tools to organize their ideas. Programs like Scrivener allowed authors to manage large projects, from novels to screenplays, with features like chapter outlines and character notes. +- Accessibility : Writers no longer needed expensive publishing equipment or training in typesetting to create polished work. This accessibility paved the way for selfpublishing, blogging, and even fan fiction communities. +- Education : Word processors became a cornerstone of education, teaching students not only how to write essays but also how to use technology effectively. Features like bibliography generators and integrated research tools enhanced learning. +- Creative Writing : Writers gained powerful tools to organize their ideas. Programs like Scrivener allowed authors to manage large projects, from novels to screenplays, with features like chapter outlines and character notes. ## Word Processors in a Post-Digital Era As we move further into the 21st century, the role of the word processor continues to evolve: -- 1. Artificial Intelligence: Modern word processors are leveraging AI to suggest content improvements. Tools like Grammarly, ProWritingAid, and even native features in Word now analyze tone, conciseness, and clarity. Some AI systems can even generate entire paragraphs or rewrite sentences. -- 2. Integration with Other Tools: Word processors are no longer standalone. They integrate with task managers, cloud storage, and project management platforms. For instance, Google Docs syncs with Google Drive, while Microsoft Word integrates seamlessly with OneDrive and Teams. -- 3. Voice Typing: Speech-to-text capabilities have made word processing more accessible, particularly for those with disabilities. Tools like Dragon NaturallySpeaking and built-in options in Google Docs and Microsoft Word have made dictation mainstream. -- 4. Multimedia Documents: Word processing has expanded beyond text. Modern tools allow users to embed images, videos, charts, and interactive elements, transforming simple documents into rich multimedia experiences. -- 5. Cross-Platform Accessibility: Thanks to cloud computing, documents can now be accessed and edited across devices. Whether you're on a desktop, tablet, or smartphone, you can continue working seamlessly. +1. Artificial Intelligence : Modern word processors are leveraging AI to suggest content improvements. Tools like Grammarly, ProWritingAid, and even native features in Word now analyze tone, conciseness, and clarity. Some AI systems can even generate entire paragraphs or rewrite sentences. +2. Integration with Other Tools : Word processors are no longer standalone. They integrate with task managers, cloud storage, and project management platforms. For instance, Google Docs syncs with Google Drive, while Microsoft Word integrates seamlessly with OneDrive and Teams. +3. Voice Typing : Speech-to-text capabilities have made word processing more accessible, particularly for those with disabilities. Tools like Dragon NaturallySpeaking and built-in options in Google Docs and Microsoft Word have made dictation mainstream. +4. Multimedia Documents : Word processing has expanded beyond text. Modern tools allow users to embed images, videos, charts, and interactive elements, transforming simple documents into rich multimedia experiences. +5. Cross-Platform Accessibility : Thanks to cloud computing, documents can now be accessed and edited across devices. Whether you're on a desktop, tablet, or smartphone, you can continue working seamlessly. ## A Glimpse Into the Future The word processor's future lies in adaptability and intelligence. Some exciting possibilities include: -- Fully AI-Assisted Writing: Imagine a word processor that understands your writing style, drafts emails, or creates entire essays based on minimal input. -- Immersive Interfaces: As augmented reality (AR) and virtual reality (VR) technology advance, users may be able to write and edit in 3D spaces, collaborating in virtual environments. -- Hyper-Personalization: Word processors could offer dynamic suggestions based on industry-specific needs, user habits, or even regional language variations. +- Fully AI-Assisted Writing : Imagine a word processor that understands your writing style, drafts emails, or creates entire essays based on minimal input. +- Immersive Interfaces : As augmented reality (AR) and virtual reality (VR) technology advance, users may be able to write and edit in 3D spaces, collaborating in virtual environments. +- Hyper-Personalization : Word processors could offer dynamic suggestions based on industry-specific needs, user habits, or even regional language variations. -The journey of the word processor-from clunky typewriters to AI-powered platforms- reflects humanity's broader technological progress. What began as a tool to simply replace handwriting has transformed into a powerful ally for creativity, communication, and collaboration. As technology continues to advance, the word processor will undoubtedly remain at the heart of how we express ideas and connect with one another. +The journey of the word processor-from clunky typewriters to AI-powered platformsreflects humanity's broader technological progress. What began as a tool to simply replace handwriting has transformed into a powerful ally for creativity, communication, and collaboration. As technology continues to advance, the word processor will undoubtedly remain at the heart of how we express ideas and connect with one another. diff --git a/tests/pdf_snapshots/pdf/sources/normal_4pages.pdf.md b/tests/pdf_snapshots/pdf/sources/normal_4pages.pdf.md index d6ab2b16..074f6236 100644 --- a/tests/pdf_snapshots/pdf/sources/normal_4pages.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/normal_4pages.pdf.md @@ -4,13 +4,21 @@ 발행일 2020년 4월 2일 -발행일 2020년 4월 2일 발행처 국회입법조사처 발행인 김하중 국회입법조사처장 +발행일 2020년 4월 2일 발행처 국회입법조사처 발행인 김하중 국회입법조사처장 www.nars.go.kr 발행처 국회입법조사처 -발행인 김하중 국회입법조사처장 +발행인 김하중 국회입법조사처장 www.nars.go.kr -## 코로나-19 관련 보험약관상 + + +제1695호 + +## 이슈와 논점 + +## 코로나-19 관련 보험약관상 재해보험금 지급문제 및 개선과제 + +김 창 호* 2020.1.1. 「감염병예방법」 관련 규정이 변경되어 적용됨에 따라, 세계적 유행(Pandemic) 단계에 돌 입하였다고 세계보건기구(WHO)가 인정한 코로나-19와 관련하여, 보험회사에서 그동안 판매했거나 향후 판매하게 될 보험상품의 입원 및 사망의 재해 인정 여부에 대하여 보험실무상 혼선이 초래되어 이 에 대하여 보험약관의 현황 및 문제점을 살펴보고 그에 따른 개선방안을 제시하고자 하였다. @@ -20,17 +28,17 @@ 이와 관련하여 세계보건기구(WHO)는 지난 2020. 3.11. 코로나-19가 세계적 유행(Pandemic) 단계 에 돌입하였음을 선언하였다. -한편 코로나-19가 전 세계적 유행단계에 돌입한 와중에 국내 보험업계에서는 코로나-19를 과연 질 병으로 보아야 할지, 상해1)나 재해2)로 보아야 할지 +한편 코로나-19가 전 세계적 유행단계에 돌입한 와중에 국내 보험업계에서는 코로나-19를 과연 질 병으로 보아야 할지, 상해 1) 나 재해 2) 로 보아야 할지 1) 손해보험의 표준약관 규정에 따르면, 보험기간 중에 발생한 급격하고도 우연한 외래의 사고로 신체에 입은 상해라고 규정하고 있는데, 즉, "급 격성, 우연성, 외래성"을 충족하는 사고를 상해로 정의함 2) 생명보험은 표준약관 "재해분류표"에서 "우발적인 외래의 사고"를 재 해라고 정의하며 보장대상이 되는 재해는 다음 중 어느 하나에 해당하 는 것을 말함 -1) 한국표준질병·사인분류상의'S80~Y84'에 해당하는 우발적인 외래의 사고 +1) 한국표준질병·사인분류상의 'S80~Y84'에 해당하는 우발적인 외래의 사고 -1) 한국표준질병·사인분류상의'S80~Y84'에 해당하는 우발적인 외래의 사고 2) 감염병의 예방 및 관리에 관한 법률 제2조 제2호에서 규정한 감염병 +1) 한국표준질병·사인분류상의 'S80~Y84'에 해당하는 우발적인 외래의 사고 2) 감염병의 예방 및 관리에 관한 법률 제2조 제2호에서 규정한 감염병 그러나, 약관상 한국표준질병·사인분류상의 U00~U99에 해당하는 - +이슈가 될 것으로 판단된다. 통상 질병보험금에 비해 상해나 재해로 인한 보 험금의 경우 보장금액 등이 높게 책정되어 있다 보 니 코로나-19를 상해나 재해로 인정할 지 여부에 대하여 약관 상 명백한 규정이 없는 한 관련 분쟁이 지속적으로 발생할 가능성이 있다. @@ -38,48 +46,50 @@ 따라서 코로나-19와 관련하여 보험회사에서 이 미 판매했거나 향후 판매하게 될 보험상품의 약관 상 재해보험금의 입원 및 사망담보 적용여부에 대 하여 살펴보고 그에 따른 재해보험금 지급문제의 개선과제에 대하여 정리하고자 한다. +질병은 보장대상이 되지 않는 것으로 정의 + ## 2 코로나-19 관련 보험 현황 ## (1) 「감염병의 예방 및 관리에 관한 법률」 개정 -2020.1.1. 시행된3) 「감염병의 예방 및 관리에 관 한 법률」(이하'감염병예방법') 제2조 제2호에 의하 면 제1급감염병이란 생물테러감염병 또는 치명률 이 높거나 집단 발생의 우려가 커서 발생 또는 유행 즉시 신고하여야 하고, 음압격리와 같은 높은 수준 의 격리가 필요한 감염병으로서 제2호 각 목4)에 규 정된 감염병을 말한다. +3) 「감염병의 예방 및 관리에 관 2020.1.1. 시행된 한 법률」(이하 '감염병예방법') 제2조 제2호에 의하 면 제1급감염병이란 생물테러감염병 또는 치명률 이 높거나 집단 발생의 우려가 커서 발생 또는 유행 즉시 신고하여야 하고, 음압격리와 같은 높은 수준 의 격리가 필요한 감염병으로서 제2호 각 목 4) 에 규 정된 감염병을 말한다. -다만, 갑작스러운 국내 유입 또는 유행이 예견되 어 긴급한 예방 ㆍ 관리가 필요하여 보건복지부장관 이 지정하는 감염병을 포함한다고 설명하고 있다. +다만, 갑작스러운 국내 유입 또는 유행이 예견되 어 긴급한 예방ㆍ관리가 필요하여 보건복지부장관 이 지정하는 감염병을 포함한다고 설명하고 있다. [표 1] 감염병예방법 제2조제2호 개정전·후 비교 -| 구분 | 개정전 | 개정후 | -| - | - | - | -| 분류 | 1군 감염병 | 1급 감염병 | -| 분류 기준 | ◦ 마시는 물 또는 식품을 매개로 발생하고 집단 발생의 우려가 큰 감염병 | ◦ 생물테러감염병 또는 치명률이 높거나 집단 발생의 우려가 커 높 은 수준의 격리가 필요한 감염병 | -| 대상 질병 | ◦ (6종) 콜레라, 장티푸스, 파라 티푸스, 세균성이질, 장출혈 성대장균감염증, A형간염 | ◦ (17종) 에볼라, 페스트 등 * 좌측 6종(2급 감염병 분류)은 미포함 | -| U코드 | ◦ 대상질병에 U코드 없음 | ◦ 대상질병에 U코드 일부(3종) 포함 ① 신종감염병증후군 → 코로나19(U) ② 중증급성호흡기증후군(SARS) (U) ③ 중동호흡기증후군(MERS)(U) | +| 구분 | 개정전 | 개정후 | +|-------|----------------------------------------------------|----------------------------------------------------------------------------------------| +| 분류 | 1군 감염병 | 1급 감염병 | +| 분류 기준 | ◦ 마시는 물 또는 식품을 매개로 발생하고 집단 발생의 우려가 큰 감염병 | ◦ 생물테러감염병 또는 치명률이 높거나 집단 발생의 우려가 커 높 은 수준의 격리가 필요한 감염병 | +| 대상 질병 | ◦ (6종) 콜레라, 장티푸스, 파라 티푸스, 세균성이질, 장출혈 성대장균감염증, A형간염 | ◦ (17종) 에볼라, 페스트 등 * 좌측 6종(2급 감염병 분류)은 미포함 | +| U코드 | ◦ 대상질병에 U코드 없음 | ◦ 대상질병에 U코드 일부(3종) 포함 ① 신종감염병증후군 → 코로나19(U) ② 중증급성호흡기증후군(SARS) (U) ③ 중동호흡기증후군(MERS)(U) | ※ 주: 2020.1.1. 기준 -※ 자료: 생명보험협회,'20.3.11. +※ 자료: 생명보험협회, '20.3.11. 기존 생명보험에서 재해로 보장하는 법정1군 전염 병은 "콜레라, 장티푸스, 파라티푸스, 세균성이질, 장 출혈성대장균감염증, A형간염"이었으나 이번에 개 -3) 개정 이유는 질환의 특성별'군(群)'별로 구분되어 있는 현행 감염병 분 류체계를 감염병의 심각도 ㆍ 전파력 ㆍ 격리수준 ㆍ 신고시기 등을 중심으 로 한'급(級)'별 분류체계로 개편하고, 감염병 위기상황 발생 시 컨트롤 타워 역할을 수행할 수 있는 긴급상황실의 설치 ㆍ 운영과 감염병환자와 접촉한 자를 격리할 수 있는 접촉자격리시설의 지정을 위한 법적 근거 를 신설하며, 감염병관리위원회 위원장을 보건복지부차관에서 질병관 리본부장으로 변경하는 등 감염병 발생 시 보다 효율적인 대처가 이루 어질 수 있도록 현행 감염병 관리체계를 개선 ㆍ 보완하려는 것임. +3) 개정 이유는 질환의 특성별 '군(群)'별로 구분되어 있는 현행 감염병 분 류체계를 감염병의 심각도ㆍ전파력ㆍ격리수준ㆍ신고시기 등을 중심으 로 한 '급(級)'별 분류체계로 개편하고, 감염병 위기상황 발생 시 컨트롤 타워 역할을 수행할 수 있는 긴급상황실의 설치ㆍ운영과 감염병환자와 접촉한 자를 격리할 수 있는 접촉자격리시설의 지정을 위한 법적 근거 를 신설하며, 감염병관리위원회 위원장을 보건복지부차관에서 질병관 리본부장으로 변경하는 등 감염병 발생 시 보다 효율적인 대처가 이루 어질 수 있도록 현행 감염병 관리체계를 개선ㆍ보완하려는 것임. -4) 2. 가. ~ 더.; 에볼라바이러스병, 마버그열, 라싸열, 크리미안콩고출혈 열, 남아메리카출혈열, 리프트밸리열, 두창, 페스트, 탄저, 보툴리눔독 소증, 야토병, 신종감염병증후군, 중증급성호흡기증후군(SARS), 중동 호흡기증후군(MERS), 동물인플루엔자 인체감염증, 신종인플루엔자, +4) 2. 가. ~ 더.; 에볼라바이러스병, 마버그열, 라싸열, 크리미안콩고출혈 열, 남아메리카출혈열, 리프트밸리열, 두창, 페스트, 탄저, 보툴리눔독 소증, 야토병, 신종감염병증후군, 중증급성호흡기증후군(SARS), 중동 호흡기증후군(MERS), 동물인플루엔자 인체감염증, 신종인플루엔자, 디프테리아 등 17종 -대신에 「감염병예방법」 제1급감염병으로 "에볼 라바이러스병, ~ 신종감염증후군, SARS, MERS, ~ 디프테리아"등 17종이 새롭게 포함되었다. +정된 법에서 이들은 제2급감염병으로 변경되었다. 대신에 「감염병예방법」 제1급감염병으로 "에볼 라바이러스병, ~ 신종감염증후군, SARS, MERS, ~ 디프테리아"등 17종이 새롭게 포함되었다. ## (2) 생명보험 표준약관 재해분류표 -현행 생명보험 표준약관상 재해분류표5)는 위 「감염병예방법」 제2조 제2호의 제1급 감염병들을 질병임에도 불구하고 보장대상이 되는'재해'로 규 정하고 있다. +현행 생명보험 표준약관상 재해분류표 5) 는 위 「감염병예방법」 제2조 제2호의 제1급 감염병들을 질병임에도 불구하고 보장대상이 되는 '재해'로 규 정하고 있다. -그러나 제1급 감염병에 포함되는 질병임에도 불 구하고, 생명보험 표준약관 재해분류표의 한국표 준질병 ·사인분류6)(이하'KCD'라 함)상 U코드 (U00~U99)에 해당하는 질병들(SARS-U04.9, MERS-U19.9 등)은 보장제외 대상으로 분류되어 보험금을 지급하지 않는 재해로 규정되어 있다. +그러나 제1급 감염병에 포함되는 질병임에도 불 구하고, 생명보험 표준약관 재해분류표의 한국표 준질병·사인분류 6) (이하 'KCD'라 함)상 U코드 (U00~U99)에 해당하는 질병들(SARS-U04.9, MERS-U19.9 등)은 보장제외 대상으로 분류되어 보험금을 지급하지 않는 재해로 규정되어 있다. 코로나-19 역시 KCD 수록 정식 명칭은 "코로나 바이러스 질환 2019"로 질병분류기호는 "U07.1" 로 표시하고 있어서 보험금이 지급되지 않는 재해 로 분류되어 재해보험금 지급대상에 포함되지 않는 다고 해석될 수 있다. 한편, 현재 생명보험 표준약관은 2020.1.1.부터 신규 판매되고 있는 보험상품의 약관이며 해당 약 관에는 아직 상기 법 개정사항이 반영되지 않은 상 황이지만, 재해분류표를 보면 별도의 각주를 통해 "감염병에 관한 법률이 제·개정될 경우, 보험사고 발생 당시 제·개정된 법률을 적용합니다."라고 명 시되어 코로나-19를 재해보험금 지급대상에 포함 되는 것으로도 해석할 수 있다. -- (3) 「약관의 규제에 관한 법률」상 약관해석의 원칙 현재 「약관의 규제에 관한 법률」7)을 보면 약관은 +- (3) 「약관의 규제에 관한 법률」상 약관해석의 원칙 현재 「약관의 규제에 관한 법률」 7) 을 보면 약관은 5) 「생명보험 표준약관 부표4」 @@ -87,7 +97,7 @@ 7) 제5조(약관의 해석) ① 약관은 신의성실의 원칙에 따라 공정하게 해석 되어야 하며 고객에 따라 다르게 해석되어서는 아니 된다. ② 약관의 뜻이 명백하지 아니한 경우에는 고객에게 유리하게 해석되어 야 한다. -작성자인 사업자에 의하여 일방적으로 유리하게 작 성되고 고객에게는 그 약관내용에 관한 교섭이나 검토의 기회가 제대로 주어지지 않는 것이 일반적 이므로 이러한 형성과정에 비추어 고객보호의 측면 에서 약관내용이 명백하지 못하거나 의심스러운 때 에는 고객에게 유리하게 해석되어야 한다는'작성 자 불이익의 해석원칙'이 있다. +작성자인 사업자에 의하여 일방적으로 유리하게 작 성되고 고객에게는 그 약관내용에 관한 교섭이나 검토의 기회가 제대로 주어지지 않는 것이 일반적 이므로 이러한 형성과정에 비추어 고객보호의 측면 에서 약관내용이 명백하지 못하거나 의심스러운 때 에는 고객에게 유리하게 해석되어야 한다는 '작성 자 불이익의 해석원칙'이 있다. ## (4) 생보업계의 재해보험금 지급에 대한 의견 @@ -103,15 +113,15 @@ ## (2) 감독당국의 표준약관 개정작업 소홀 -통상 보험사는 금융감독원이 보험상품의 표준약 관을 변경한 후 보험회사의 개별 상품에 대한 약관 변경작업을 진행하는데, 이번 경우는 「감염병예방 법」이 변경 시행되었음에도 불구하고 금융감독당 국이 생명보험 표준약관 개정작업을 시의적절하게 +통상 보험사는 금융감독원이 보험상품의 표준약 관을 변경한 후 보험회사의 개별 상품에 대한 약관 변경작업을 진행하는데, 이번 경우는 「감염병예방 법」이 변경 시행되었음에도 불구하고 금융감독당 국이 생명보험 표준약관 개정작업을 시의적절하게 이행하지 못하는 바람에 나타난 혼선이다. -서 약관 개정 시 해당 내용을 어떻게 규정할 지에 대 하여 미리 검토가 요구되며 이는 보험회사와 금융 당국 간의 적절한 절차를 거쳐 사전논의를 했어야 하는 사항이라고 판단된다. +개정된 「감염병예방법」이 시행된 후 보험회사에 서 약관 개정 시 해당 내용을 어떻게 규정할 지에 대 하여 미리 검토가 요구되며 이는 보험회사와 금융 당국 간의 적절한 절차를 거쳐 사전논의를 했어야 하는 사항이라고 판단된다. 개별 보험사의 모든 상품을 심사하는 내용이 아 니라 생명보험 표준약관의 규정을 관련법과 비교하 여 개정하는 작업을 소홀히 한 만큼 금융감독당국 의 책임이 크다고 할 수 있다. ## (3) 보험사의 보험금 지급실무상 혼선 초래 -보험업계는 보험은 사고 위험의 예측가능성과, 보험의 기본원리인 대수의 법칙8)이나 수지상등의 원칙9)에 부합하고, 최대 손실을 보험회사가 감당할 수 있어야 한다고 주장한다. +보험업계는 보험은 사고 위험의 예측가능성과, 보험의 기본원리인 대수의 법칙 8) 이나 수지상등의 9) 원칙 에 부합하고, 최대 손실을 보험회사가 감당할 수 있어야 한다고 주장한다. 특히, 신종감염병증후군과 신종인플루엔자는 특 정 질병이 아닌 앞으로 새롭게 발생할 모든 신종감 염병을 포괄하는 개념으로 위험률 측정이 불가능하 고, 담보 범위도 확정할 수도 없다고 주장한다. @@ -125,6 +135,8 @@ 9) 수지가 같아진다는 것은 다수의 동일연령의 피보험자가 같은 보험종류 를 동시에 계약했을 때 보험기간 만료시에 수입과 지출이 균형이 잡혀 지도록 순보험료를 계산하는 것을 의미함 +이슈와 논점 + ## 4 개선과제 ## (1) 「약관규제법」에 따른 보험금 지급 검토 필요 @@ -137,15 +149,17 @@ 우선 「감염병예방법」 변경에 따른 감독당국의 조속한 표준약관 개정작업이 진행되어야 할 필요가 있다. -보험사가 생명보험에서'일부 감염병'을 재해로 보장하는 이유는'일부 감염병'이 우연성, 외래성, 급격성 등 재해의 특성을 지니고 있기 때문이며, 실 례로 페스트는 질병이지만 재해에 준하는 급격성도 지니고 있어, 정책적으로 재해로 취급하여 보험의 보장 범위를 확대한 바 있다. +보험사가 생명보험에서 '일부 감염병'을 재해로 보장하는 이유는 '일부 감염병'이 우연성, 외래성, 급격성 등 재해의 특성을 지니고 있기 때문이며, 실 례로 페스트는 질병이지만 재해에 준하는 급격성도 지니고 있어, 정책적으로 재해로 취급하여 보험의 보장 범위를 확대한 바 있다. 코로나-19 역시 질병이지만 세계적 유행단계에 돌입하는 등 페스트와 같은 재해에 준하는 성격을 포함하고 있어 이를 재해로 인정할 필요가 있다. -아울러 코로나-19의 경우, 국내 경제를 넘어 세 계경제의 동반침체를 일으키고 있는 바, 금융감독 당국이 적극적 경기부양정책에 부응하는 보험정책 을 시행하는 것이 코로나-19로 지친 국민에게 경제 +아울러 코로나-19의 경우, 국내 경제를 넘어 세 계경제의 동반침체를 일으키고 있는 바, 금융감독 당국이 적극적 경기부양정책에 부응하는 보험정책 을 시행하는 것이 코로나-19로 지친 국민에게 경제 적 위안을 제공하는 것이라 할 수 있다. + +## (3) 신종위험에 대비하는 보험상품 개발 필요 -보험사는 기후변화, 전염병 등과 같은 신종위험 으로 인한 사회적 손실이 증가하는 추세에 있는바 손실이 광범위하고 직 ·간접적이어서 손해 규모를 측정하기 어려운 경우를 대비한 보험상품을 개발할 필요가 있다. +보험사는 기후변화, 전염병 등과 같은 신종위험 으로 인한 사회적 손실이 증가하는 추세에 있는바 손실이 광범위하고 직·간접적이어서 손해 규모를 측정하기 어려운 경우를 대비한 보험상품을 개발할 필요가 있다. -따라서 감염병 보험, 파라메트릭(Parametric Insurance)보험10), 인덱스(Index)보험 등과 같이 실제 발생한 손실액이 아니라 특정 지표에 의해 보 험금이 지급되는 신종보험상품을 개발 보급 판매할 필요가 점진적으로 증가하고 있다. +따라서 감염병 보험, 파라메트릭(Parametric Insurance)보험 10) , 인덱스(Index)보험 등과 같이 실제 발생한 손실액이 아니라 특정 지표에 의해 보 험금이 지급되는 신종보험상품을 개발 보급 판매할 필요가 점진적으로 증가하고 있다. ## 5 맺으며 diff --git a/tests/pdf_snapshots/pdf/sources/redp5110_sampled.pdf.md b/tests/pdf_snapshots/pdf/sources/redp5110_sampled.pdf.md index 324e820d..3d344d7c 100644 --- a/tests/pdf_snapshots/pdf/sources/redp5110_sampled.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/redp5110_sampled.pdf.md @@ -1,28 +1,38 @@ -of duties + + +## Row and Column Access Control Support in IBM DB2 for i + +Implement roles and separation of duties Leverage row permissions on the database -Protect columns by defining column masks +Protect columns by defining column masks ibm.com /redbooks - +Jim Bainbridge Hernando Bedoya Rob Bestgen Mike Cain Dan Cruikshank Jim Denton Doug Mack Tom McKinley Kent Milligan -## Row and Column Access Control Support in IBM DB2 for i +Redpaper -Jim Bainbridge Hernando Bedoya Rob Bestgen Mike Cain Dan Cruikshank Jim Denton Doug Mack Tom McKinley +## Contents +Solution Brief + --                --                     --       !   " #  --  ! #      " "       +## Highlights + +-                +-                     +-       !   " #  +-  ! #      " "       +Power Services + ## DB2 for i Center of Excellence Expert help to achieve your business requirements @@ -47,10 +57,13 @@ Global CoE engagements cover topics including: - r Database modernization and re-engineering - r Data-centric architecture and design - r Extremely large database and overcoming limits to growth +- ISV education and enablement r + +## Preface This IBM® Redpaper™ publication provides information about the IBM i 7.2 feature of IBM DB2® for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment. -This paper is intended for database engineers, data-centric application developers, and secu rity officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed. +This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed. ## Authors @@ -58,17 +71,19 @@ This paper was produced by the IBM DB2 for i Center of Excellence team in partne -Jim Bainbridge is a senior DB2 consultant on the DB2 for i Center of Excellence team in the IBM Lab Services and Training organization. His primary role is training and implementation services for IBM DB2 Web Query for i and business analytics. Jim began his career with IBM 30 years ago in the IBM Rochester Development Lab, where he developed cooperative processing products that paired IBM PCs with IBM S/36 and AS/.400 systems. In the years since, Jim has held nu merous technical roles, including independent software vendors technical support on a broad range of IBM technologies and products, and supporting customers in the IBM Executive Briefing Center and IBM Project Office. +Jim Bainbridge is a senior DB2 consultant on the DB2 for i Center of Excellence team in the IBM Lab Services and Training organization. His primary role is training and implementation services for IBM DB2 Web Query for i and business analytics. Jim began his career with IBM 30 years ago in the IBM Rochester Development Lab, where he developed cooperative processing products that paired IBM PCs with IBM S/36 and AS/.400 systems. In the years since, Jim has held numerous technical roles, including independent software vendors technical support on a broad range of IBM technologies and products, and supporting customers in the IBM Executive Briefing Center and IBM Project Office. -Hernando Bedoya is a Senior IT Specialist at STG Lab Services and Training in Rochester, Minnesota. He writes extensively and teaches IBM classes worldwide in all areas of DB2 for i. Before joining STG Lab Services, he worked in the ITSO for nine years writing multiple IBM Redbooks® publications. He also worked for IBM Colombia as an IBM AS/400® IT Specialist doing presales support for the Andean countries. He has 28 years of experience in the computing field and has taught database classes in Colombian universities. He holds a Master's degree in Computer Science from EAFIT, Colombia. His areas of expertise are database technology, performance, and data warehousing. Hernando can be contacted at hbedoya@us.i bm.com. +Hernando Bedoya is a Senior IT Specialist at STG Lab Services and Training in Rochester, Minnesota. He writes extensively and teaches IBM classes worldwide in all areas of DB2 for i. Before joining STG Lab Services, he worked in the ITSO for nine years writing multiple IBM Redbooks® publications. He also worked for IBM Colombia as an IBM AS/400® IT Specialist doing presales support for the Andean countries. He has 28 years of experience in the computing field and has taught database classes in Colombian universities. He holds a Master's degree in Computer Science from EAFIT, Colombia. His areas of expertise are database technology, performance, and data warehousing. Hernando can be contacted at hbedoya@us.ibm.com . -## data +## 1 + +## Securing and protecting IBM DB2 data -global businesses of all sizes. The Identity Theft Resource Center1 reports that almost 5000Recent news headlines are filled with reports of data breaches and cyber-attacks impacting financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute2data breaches have occurred since 2005, exposing over 600 million records of data. The revealed that the average cost of a data breach increased in 2013 by 15% globally and resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for each lost record containing sensitive information increased more than 9% to $145 per record. +Recent news headlines are filled with reports of data breaches and cyber-attacks impacting 1 reports that almost 5000 global businesses of all sizes. The Identity Theft Resource Center data breaches have occurred since 2005, exposing over 600 million records of data. The financial cost of these data breaches is skyrocketing. Studies from the Ponemon Institute2 revealed that the average cost of a data breach increased in 2013 by 15% globally and resulted in a brand equity loss of $9.4 million per attack. The average cost that is incurred for each lost record containing sensitive information increased more than 9% to $145 per record. Businesses must make a serious effort to secure their data and recognize that securing information assets is a cost of doing business. In many parts of the world and in many industries, securing the data is required by law and subject to audits. Data security is no longer an option; it is a requirement. @@ -76,24 +91,27 @@ This chapter describes how you can secure and protect data in DB2 for i. The fol - Security fundamentals - Current state of IBM i security +- DB2 for i security controls + +1 http://www.idtheftcenter.org -1 http : //www.i dtheftcenter.org +2 http://www.ponemon.org / -2 http : //www.ponemon.org/ +## 1.1 Security fundamentals Before reviewing database security techniques, there are two fundamental steps in securing information assets that must be described: -- First, and most important, is the definition of a company's security policy. Without a secu rity policy, there is no definition of what are acceptable practices for using, accessing, and storing information by who, what, when, where, and how. A security policy should minimally address three things: confidentiality, integrity, and availability. +- First, and most important, is the definition of a company's security policy . Without a security policy, there is no definition of what are acceptable practices for using, accessing, and storing information by who, what, when, where, and how. A security policy should minimally address three things: confidentiality, integrity, and availability. -The monitoring and assessment of adherence to the security policy determines whether your security strategy is working. Often, IBM security consultants are asked to perform secu rity assessments for companies without regard to the security policy. Although these assessments can be useful for observing how the system is defined and how data is being accessed, they cannot determine the level of security without a security policy. Without a secu rity policy, it really is not an assessment as much as it is a baseline for monitoring the changes in the security settings that are captured. +The monitoring and assessment of adherence to the security policy determines whether your security strategy is working. Often, IBM security consultants are asked to perform security assessments for companies without regard to the security policy. Although these assessments can be useful for observing how the system is defined and how data is being accessed, they cannot determine the level of security without a security policy. Without a security policy, it really is not an assessment as much as it is a baseline for monitoring the changes in the security settings that are captured. A security policy is what defines whether the system and its settings are secure (or not). -- The second fundamental in securing data assets is the use of resource security. If implemented properly, resource security prevents data breaches from both internal and external intrusions. Resource security controls are closely tied to the part of the security policy that defines who should have access to what information resources. A hacker might be good enough to get through your company firewalls and sift his way through to your system, but if they do not have explicit access to your database, the hacker cannot compromise your information assets. +- The second fundamental in securing data assets is the use of resource security . If implemented properly, resource security prevents data breaches from both internal and external intrusions. Resource security controls are closely tied to the part of the security policy that defines who should have access to what information resources. A hacker might be good enough to get through your company firewalls and sift his way through to your system, but if they do not have explicit access to your database, the hacker cannot compromise your information assets. With your eyes now open to the importance of securing information assets, the rest of this chapter reviews the methods that are available for securing database resources on IBM i. -## 1.2 Current state of IBM i security +## 1.2 Current state of IBM i security Because of the inherently secure nature of IBM i, many clients rely on the default system settings to protect their business data that is stored in DB2 for i. In most cases, this means no data protection because the default setting for the Create default public authority (QCRTAUT) system value is *CHANGE. @@ -101,100 +119,106 @@ Even more disturbing is that many IBM i clients remain in this state, despite th Traditionally, IBM i applications have employed menu-based security to counteract this default configuration that gives all users access to the data. The theory is that data is protected by the menu options controlling what database operations that the user can perform. This approach is ineffective, even if the user profile is restricted from running interactive commands. The reason is that in today's connected world there are a multitude of interfaces into the system, from web browsers to PC clients, that bypass application menus. If there are no object-level controls, users of these newer interfaces have an open door to your data. -means that users should be given access only to the minimum set of data that is required to perform their job. Often, users with object-level access are given access to row and column values that are beyond what their business task requires because that object-level security provides an all-or-nothing solution. For example, object-level controls allow a manager to access data about all employees. Most security policies limit a manager to accessing data only for the employees that they manage. +Many businesses are trying to limit data access to a need-to-know basis. This security goal means that users should be given access only to the minimum set of data that is required to perform their job. Often, users with object-level access are given access to row and column values that are beyond what their business task requires because that object-level security provides an all-or-nothing solution. For example, object-level controls allow a manager to access data about all employees. Most security policies limit a manager to accessing data only for the employees that they manage. -## 1.3.1 Existing row and column control +## 1.3.1 Existing row and column control -Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL views (or logical files) and application logic, as shown in Figure 1 -2. However, application-based logic is easy to bypass with all of the different data access interfaces that are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) and System i Navigator. +Some IBM i clients have tried augmenting the all-or-nothing object-level security with SQL views (or logical files) and application logic, as shown in Figure 1-2. However, application-based logic is easy to bypass with all of the different data access interfaces that are provided by the IBM i operating system, such as Open Database Connectivity (ODBC) and System i Navigator. Using SQL views to limit access to a subset of the data in a table also has its own set of challenges. First, there is the complexity of managing all of the SQL view objects that are used for securing data access. Second, scaling a view-based security solution can be difficult as the amount of data grows and the number of users increases. Even if you are willing to live with these performance and management issues, a user with *ALLOBJ access still can directly access all of the data in the underlying DB2 table and easily bypass the security controls that are built into an SQL view. +Figure 1-2 Existing row and column controls + +## 2.1.6 Change Function Usage CL command + The following CL commands can be used to work with, display, or change function usage IDs: -- Work Function Usage (WRKFCNUSG) -- Change Function Usage (CHGFCNUSG) -- Display Function Usage (DSPFCNUSG) +- Work Function Usage ( WRKFCNUSG ) +- Change Function Usage ( CHGFCNUSG ) +- Display Function Usage ( DSPFCNUSG ) For example, the following CHGFCNUSG command shows granting authorization to user HBEDOYA to administer and manage RCAC rules: -CHGFCNUSG FCN I D(QIBM_DB_SECADM) USER(HBEDOYA) USAGE (*ALLOWED) +CHGFCNUSG FCNID(QIBM\_DB\_SECADM) USER(HBEDOYA) USAGE(*ALLOWED) -## 2.1.7 Verifying function usage IDs for RCAC with the FUNCTION_USAGE view +## 2.1.7 Verifying function usage IDs for RCAC with the FUNCTION\_USAGE view -The FUNCTION_USAGE view contains function usage configuration details. Table 2-1 describes the columns in the FUNCTION_USAGE view. +The FUNCTION\_USAGE view contains function usage configuration details. Table 2-1 describes the columns in the FUNCTION\_USAGE view. -Table 2- 1 FUNCTION_USAGE view +Table 2-1 FUNCTION\_USAGE view -| name | Data type | Description | -| - | - | - | -| FUNCTION_ID | VARCHAR(30) | ID of the function. | -| USER_NAME | VARCHAR(10) | Name of the user profile that has a usage setting for this function. | -| USAGE | VARCHAR(7) | authority.Column Usage setting:  ALLOWED: The user profile is allowed to use the function.  DENIED: The user profile is not allowed to use the function. | -| USER_TYPE | VARCHAR(5) | i2.1.6 Type of user profile:  USER: The user profile is a user.  GROUP: The user profile is a group. | +| name | Data type | Description | +|-------------|-------------|------------------------------------------------------------------------------------------------------------------------------------------------------------| +| FUNCTION_ID | VARCHAR(30) | ID of the function. | +| USER_NAME | VARCHAR(10) | Name of the user profile that has a usage setting for this function. | +| USAGE | VARCHAR(7) | authority.Column Usage setting:  ALLOWED: The user profile is allowed to use the function.  DENIED: The user profile is not allowed to use the function. | +| USER_TYPE | VARCHAR(5) | i2.1.6 Type of user profile:  USER: The user profile is a user.  GROUP: The user profile is a group. | To discover who has authorization to define and manage RCAC, you can use the query that is shown in Example 2-1. Example 2-1 Query to determine who has authority to define and manage RCAC -| SELECT | functi on_i d, u s er_n ame, u s age, er_type | -| - | - | -| FROM | i2.1.6 functi on_usage functi on_i d=’QIBM_DB_SECADM’ user_name; authority.Column | -| WHERE | | +| SELECT | functi on_i d, u s er_n ame, u s age, er_type | +|----------|-----------------------------------------------------------------------------------| +| FROM | i2.1.6 functi on_usage functi on_i d=’QIBM_DB_SECADM’ user_name; authority.Column | +| WHERE | | -## 2.2 Separation of duties +## 2.2 Separation of duties -Separation of duties helps businesses comply with industry regulations or organizational requirements and simplifies the management of authorities. Separation of duties is commonly used to prevent fraudulent activities or errors by a single person. It provides the ability for administrative functions to be divided across individuals without overlapping responsibilities, +Separation of duties helps businesses comply with industry regulations or organizational requirements and simplifies the management of authorities. Separation of duties is commonly used to prevent fraudulent activities or errors by a single person. It provides the ability for administrative functions to be divided across individuals without overlapping responsibilities, so that one user does not possess unlimited authority, such as with the *ALLOBJ authority. -Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same privileges Theresa was granting to others. Therefore, to grant *USE privileges to the PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the PAYROLL table even though Theresa's job description was only to manage its security. +For example, assume that a business has assigned the duty to manage security on IBM i to Theresa. Before release IBM i 7.2, to grant privileges, Theresa had to have the same privileges Theresa was granting to others. Therefore, to grant *USE privileges to the PAYROLL table, Theresa had to have *OBJMGT and *USE authority (or a higher level of authority, such as *ALLOBJ). This requirement allowed Theresa to access the data in the PAYROLL table even though Theresa's job description was only to manage its security. -In IBM i 7.2, the QIBM_DB_SECADM function usage grants authorities, revokes authorities, changes ownership, or changes the primary group without giving access to the object or, in the case of a database table, to the data that is in the table or allowing other operations on the table. +In IBM i 7.2, the QIBM\_DB\_SECADM function usage grants authorities, revokes authorities, changes ownership, or changes the primary group without giving access to the object or, in the case of a database table, to the data that is in the table or allowing other operations on the table. -QIBM_DB_SECADM function usage can be granted only by a user with *SECADM special authority and can be given to a user or a group. +QIBM\_DB\_SECADM function usage can be granted only by a user with *SECADM special authority and can be given to a user or a group. -QIBM_DB_SECADM also is responsible for administering RCAC, which restricts which rows a user is allowed to access in a table and whether a user is allowed to see information in certain columns of a table. +QIBM\_DB\_SECADM also is responsible for administering RCAC, which restricts which rows a user is allowed to access in a table and whether a user is allowed to see information in certain columns of a table. -A preferred practice is that the RCAC administrator has the QIBM_DB_SECADM function usage ID, but absolutely no other data privileges. The result is that the RCAC administrator can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized access to data itself. +A preferred practice is that the RCAC administrator has the QIBM\_DB\_SECADM function usage ID, but absolutely no other data privileges. The result is that the RCAC administrator can deploy and maintain the RCAC constructs, but cannot grant themselves unauthorized access to data itself. Table 2-2 shows a comparison of the different function usage IDs and *JOBCTL authority to the different CL commands and DB2 for i tools. Table 2-2 Comparison of the different function usage IDs and *JOBCTL authority -| User action | *JOBCTL | QIBM_DB_SECADM | QIBM_DB_SQLADM | QIBM_DB_SYSMON | No Authority | -| - | - | - | - | - | - | -| SET CURRENT DEGREE (SQL statement) | X | | X | | | -| CHGQRYA command targeting a different user's job | X | | X | | | -| STRDBMON or ENDDBMON commands targeting a different user's job | X | | X | | | -| STRDBMON or ENDDBMON commands targeting a job that matches the current user | X | | X | X | X | -| QUSRJOBI() API format 900 or System i Navigator's SQL Details for Job | X | | X | X | | -| Visual Explain within Run SQL scripts | X | | X | X | X | -| Visual Explain outside of Run SQL scripts | X | | X | | | -| ANALYZE PLAN CACHE procedure | X | | X | | | -| DUMP PLAN CACHE procedure | X | | X | | | -| MODIFY PLAN CACHE procedure | X | | X | | | -| 1For MODIFY PLAN CACHE PROPERTIES procedure (currently does not check authority) | X | | X | | | -| CHANGE PLAN CACHE SIZE procedure (currently does not check authority) | X | | X | | | - -initially enable or disable the row access rules. - -Figure 3-1 CREATE PERMISSION SQL statement +| User action | *JOBCTL | QIBM_DB_SECADM | QIBM_DB_SQLADM | QIBM_DB_SYSMON | No Authority | +|----------------------------------------------------------------------------------|-----------|------------------|------------------|------------------|----------------| +| SET CURRENT DEGREE (SQL statement) | X | | X | | | +| CHGQRYA command targeting a different user's job | X | | X | | | +| STRDBMON or ENDDBMON commands targeting a different user's job | X | | X | | | +| STRDBMON or ENDDBMON commands targeting a job that matches the current user | X | | X | X | X | +| QUSRJOBI() API format 900 or System i Navigator's SQL Details for Job | X | | X | X | | +| Visual Explain within Run SQL scripts | X | | X | X | X | +| Visual Explain outside of Run SQL scripts | X | | X | | | +| ANALYZE PLAN CACHE procedure | X | | X | | | +| DUMP PLAN CACHE procedure | X | | X | | | +| MODIFY PLAN CACHE procedure | X | | X | | | +| 1For MODIFY PLAN CACHE PROPERTIES procedure (currently does not check authority) | X | | X | | | +| CHANGE PLAN CACHE SIZE procedure (currently does not check authority) | X | | X | | | + +The SQL CREATE PERMISSION statement that is shown in Figure 3-1 is used to define and initially enable or disable the row access rules. + +Figure 3-1 CREATE PERMISSION SQL statement ## Column mask -A column mask is a database object that manifests a column value access control rule for a specific column in a specific table. It uses a CASE expression that describes what you see when you access the column. For example, a teller can see only the last four digits of a tax +A column mask is a database object that manifests a column value access control rule for a specific column in a specific table. It uses a CASE expression that describes what you see when you access the column. For example, a teller can see only the last four digits of a tax identification number. + +Table 3-1 summarizes these special registers and their values. Table 3-1 Special registers and their corresponding values -| register | Corresponding value | -| - | - | -| USER or SESSION_USER | The effective user of the thread excluding adopted authority. | -| CURRENT_USER | 9Table logic.Special The effective user of the thread including adopted authority. When no adopted authority is present, this has the same value as USER. | -| SYSTEM_USER | The authorization ID that initiated the connection. | +| register | Corresponding value | +|----------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------| +| USER or SESSION_USER | The effective user of the thread excluding adopted authority. | +| CURRENT_USER | 9Table logic.Special The effective user of the thread including adopted authority. When no adopted authority is present, this has the same value as USER. | +| SYSTEM_USER | The authorization ID that initiated the connection. | Figure 3-5 shows the difference in the special register values when an adopted authority is used: @@ -208,53 +232,58 @@ Figure 3-5 Special registers and adopted authority -## 3.2.2 Built-in global variables +## 3.2.2 Built-in global variables Built-in global variables are provided with the database manager and are used in SQL statements to retrieve scalar values that are associated with the variables. -IBM DB2 for i supports nine different built-in global variables that are read only and maintained by the system. These global variables can be used to identify attributes of the +IBM DB2 for i supports nine different built-in global variables that are read only and maintained by the system. These global variables can be used to identify attributes of the database connection and used as part of the RCAC logic. + +Table 3-2 lists the nine built-in global variables. Table 3-2 Built-in global variables -| variable | Type | Description | -| - | - | - | -| CLIENT_HOST | VARCHAR(255) | Host name of the current client as returned by the system | -| CLIENT_IPADDR | VARCHAR(128) | TONY')Global IP address of the current client as returned by the system | -| CLIENT_PORT | INTEGER | Port used by the current client to communicate with the server | -| PACKAGE_NAME | VARCHAR(128) | Name of the currently running package | -| PACKAGE_SCHEMA | VARCHAR(128) | Schema name of the currently running package | -| PACKAGE_VERSION | VARCHAR(64) | Version identifier of the currently running package | -| ROUTINE_SCHEMA | VARCHAR(128) | Schema name of the currently running routine | -| ROUTINE_SPECIFIC_NAME | iTable VARCHAR(128) | Name of the currently running routine | -| ROUTINE_TYPE | CHAR(1) | Type of the currently running routine | +| variable | Type | Description | +|-----------------------|---------------------|-------------------------------------------------------------------------| +| CLIENT_HOST | VARCHAR(255) | Host name of the current client as returned by the system | +| CLIENT_IPADDR | VARCHAR(128) | TONY')Global IP address of the current client as returned by the system | +| CLIENT_PORT | INTEGER | Port used by the current client to communicate with the server | +| PACKAGE_NAME | VARCHAR(128) | Name of the currently running package | +| PACKAGE_SCHEMA | VARCHAR(128) | Schema name of the currently running package | +| PACKAGE_VERSION | VARCHAR(64) | Version identifier of the currently running package | +| ROUTINE_SCHEMA | VARCHAR(128) | Schema name of the currently running routine | +| ROUTINE_SPECIFIC_NAME | iTable VARCHAR(128) | Name of the currently running routine | +| ROUTINE_TYPE | CHAR(1) | Type of the currently running routine | -## 3.3 VERIFY_GROUP_FOR_USER function +## 3.3 VERIFY\_GROUP\_FOR\_USER function -The VERIFY_GROUP_FOR_USER function was added in IBM i 7.2. Although it is primarily intended for use with RCAC permissions and masks, it can be used in other SQL statements. The first parameter must be one of these three special registers: SESSION_USER, USER, or CURRENT_USER. The second and subsequent parameters are a list of user or group profiles. Each of these values must be 1 - 10 characters in length. These values are not validated for their existence, which means that you can specify the names of user profiles that do not exist without receiving any kind of error. +The VERIFY\_GROUP\_FOR\_USER function was added in IBM i 7.2. Although it is primarily intended for use with RCAC permissions and masks, it can be used in other SQL statements. The first parameter must be one of these three special registers: SESSION\_USER, USER, or CURRENT\_USER. The second and subsequent parameters are a list of user or group profiles. Each of these values must be 1 - 10 characters in length. These values are not validated for their existence, which means that you can specify the names of user profiles that do not exist without receiving any kind of error. If a special register value is in the list of user profiles or it is a member of a group profile included in the list, the function returns a long integer value of 1. Otherwise, it returns a value of 0. It never returns the null value. -Here is an example of using the VERIFY_GROUP_FOR_USER function: +Here is an example of using the VERIFY\_GROUP\_FOR\_USER function: -- 1. There are user profiles for MGR, JANE, JUDY, and TONY. -- 2. The user profile JANE specifies a group profile of MGR. -- 3. If a user is connected to the server using user profile JANE, all of the following function invocations return a value of 1 : +1. There are user profiles for MGR, JANE, JUDY, and TONY. +2. The user profile JANE specifies a group profile of MGR. +3. If a user is connected to the server using user profile JANE, all of the following function invocations return a value of 1: -VERI FY_GROUP_FOR_USER (CURRENT_USER,' MGR') VERI FY_GROUP_FOR_USER (CURRENT_USER,' JANE',' MGR') VERI FY_GROUP_FOR_USER (CURRENT_USER,' JANE',' MGR',' STEVE') +VERIFY\_GROUP\_FOR\_USER (CURRENT\_USER, 'MGR') VERIFY\_GROUP\_FOR\_USER (CURRENT\_USER, 'JANE', 'MGR') VERIFY\_GROUP\_FOR\_USER (CURRENT\_USER, 'JANE', 'MGR', 'STEVE') The following function invocation returns a value of 0: -The following function invocation returns a value of 0: +The following function invocation returns a value of 0: VERIFY\_GROUP\_FOR\_USER (CURRENT\_USER, 'JUDY', 'TONY') + +VERIFY\_GROUP\_FOR\_USER (CURRENT\_USER, 'JUDY', 'TONY') ``` -CASE WHEN VERIFY_GROUP_FOR_USER( SESSION_USER, 'HR', 'EMP') = 1 THEN EMPLOYEES. DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER( SESSION_USER, 'MGR') = 1 AND SESSION_USER = EMPLOYEES. USER_ID THEN EMPLOYEES. DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER( SESSION_USER, 'MGR') = 1 AND SESSION_USER <> EMPLOYEES. USER_IDTHEN( 9999 || '-' || MONTH( EMPLOYEES. DATE_OF_BIRTH) || '-' || DAY(EMPLOYEES.DATE_OF_BIRTH)) ELSE NULL END ENABLE; +CASE WHEN VERIFY_GROUP_FOR_USER( SESSION_USER, 'HR', 'EMP') = 1 THEN EMPLOYEES. DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER( SESSION_USER, 'MGR') = 1 AND SESSION_USER = EMPLOYEES. USER_ID THEN EMPLOYEES. DATE_OF_BIRTH WHEN VERIFY_GROUP_FOR_USER( SESSION_USER, 'MGR') = 1 AND SESSION_USER <> EMPLOYEES. USER_ID THEN( 9999 || '-' || MONTH( EMPLOYEES. DATE_OF_BIRTH) || '-' || DAY(EMPLOYEES.DATE_OF_BIRTH)) ELSE NULL END ENABLE; ``` -- 2. The other column to mask in this example is the TAX_ID information. In this example, the rules to enforce include the following ones: -- Human Resources can see the unmasked TAX_ID of the employees. -- Employees can see only their own unmasked TAX_ID. -- Managers see a masked version of TAX_ID with the first five characters replaced with the X character (for example, XXX-XX-1234). -- Any other person sees the entire TAX_ID as masked, for example, XXX-XX-XXXX. +2. The other column to mask in this example is the TAX\_ID information. In this example, the rules to enforce include the following ones: + +- Human Resources can see the unmasked TAX\_ID of the employees. +- Employees can see only their own unmasked TAX\_ID. +- Managers see a masked version of TAX\_ID with the first five characters replaced with the X character (for example, XXX-XX-1234). +- Any other person sees the entire TAX\_ID as masked, for example, XXX-XX-XXXX. To implement this column mask, run the SQL statement that is shown in Example 3-9. @@ -264,15 +293,17 @@ CREATE MASK HR_SCHEMA.MASK_TAX_ID_ON_EMPLOYEES ON HR_SCHEMA.EMPLOYEES AS EMPLOYE Example 3-9 Creating a mask on the TAX_ID column +3. Figure 3-10 shows the masks that are created in the HR\_SCHEMA. + Figure 3-10 Column masks shown in System i Navigator -## 3.6.6 Activating RCAC +## 3.6.6 Activating RCAC Now that you have created the row permission and the two column masks, RCAC must be activated. The row permission and the two column masks are enabled (last clause in the scripts), but now you must activate RCAC on the table. To do so, complete the following steps: -- 1. Run the SQL statements that are shown in Example 3-10. +1. Run the SQL statements that are shown in Example 3-10. Example 3-10 Activating RCAC on the EMPLOYEES table @@ -280,48 +311,48 @@ Example 3-10 Activating RCAC on the EMPLOYEES table /* Active Row Access Control(permissions) */ /* Active Column Access Control(masks) */ ALTER TABLE HR_SCHEMA.EMPLOYEES ACTIVATE ROW ACCESS CONTROL ACTIVATE COLUMN ACCESS CONTROL; ``` -- 2. Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from the main navigation pane of System i Navigator, click Schemas  HR_SCHEMA  Tables, right-click the EMPLOYEES table, and click Definition. +2. Look at the definition of the EMPLOYEE table, as shown in Figure 3-11. To do this, from the main navigation pane of System i Navigator, click Schemas  HR\_SCHEMA  Tables , right-click the EMPLOYEES table, and click Definition . Figure 3-11 Selecting the EMPLOYEES table from System i Navigator -- enabled. It is clear that the implementation of the SQL statement is more complex because the row permission rule becomes part of the WHERE clause. +2. Figure 4-68 shows the Visual Explain of the same SQL statement, but with RCAC enabled. It is clear that the implementation of the SQL statement is more complex because the row permission rule becomes part of the WHERE clause. Figure 4-68 Visual Explain with RCAC enabled -- 3. Compare the advised indexes that are provided by the Optimizer without RCAC and with RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC enabled. The index being advised is for the ORDER BY clause. +3. Compare the advised indexes that are provided by the Optimizer without RCAC and with RCAC enabled. Figure 4-69 shows the index advice for the SQL statement without RCAC enabled. The index being advised is for the ORDER BY clause. Figure 4-69 Index advice with no RCAC ``` -WHEN QSYS2. VERIFY_GROUP_FOR_USER( SESSION_USER, 'TELLER') = 1WHEN QSYS2. VERIFY_GROUP_FOR_USER( SESSION_USER, 'CUSTOMER') = 1WHEN QSYS2. VERIFY_GROUP_FOR_USER( SESSION_USER, 'ADMIN') = 1WHEN QSYS2. VERIFY_GROUP_FOR_USER( SESSION_USER, 'TELLER') = 1WHEN QSYS2. VERIFY_GROUP_FOR_USER( SESSION_USER, 'CUSTOMER') = 1124 Row and Column Access Control Support in IBM DB2 for iTHEN C. CUSTOMER_TAX_IDTHEN( 'XXX-XX-' CONCAT QSYS2. SUBSTR( C. CUSTOMER_TAX_ID, 8, 4))THEN C. CUSTOMER_TAX_IDTHEN C. CUSTOMER_DRIVERS_LICENSE_NUMBERTHEN C. CUSTOMER_DRIVERS_LICENSE_NUMBERTHEN C. CUSTOMER_DRIVERS_LICENSE_NUMBERCREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS CCREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS CELSE 'XXX-XX-XXXX'ENDELSE '*************'END RETURN CASERETURN CASEENABLE;FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBERENABLE;FOR COLUMN CUSTOMER_LOGIN_ID ALTER TABLE BANK_SCHEMA.CUSTOMERSWHEN QSYS2. VERIFY_GROUP_FOR_USER( SESSION_USER, 'ADMIN') = 1WHEN QSYS2. VERIFY_GROUP_FOR_USER( SESSION_USER, 'CUSTOMER') = 1WHEN QSYS2. VERIFY_GROUP_FOR_USER( SESSION_USER, 'ADMIN') = 1WHEN QSYS2. VERIFY_GROUP_FOR_USER( SESSION_USER, 'CUSTOMER') = 1WHEN QSYS2. VERIFY_GROUP_FOR_USER( SESSION_USER, 'ADMIN') = 1WHEN QSYS2. VERIFY_GROUP_FOR_USER( SESSION_USER, 'CUSTOMER') = 1THEN C. CUSTOMER_LOGIN_IDTHEN C. CUSTOMER_LOGIN_IDTHEN C. CUSTOMER_SECURITY_QUESTIONTHEN C. CUSTOMER_SECURITY_QUESTIONTHEN C. CUSTOMER_SECURITY_QUESTION_ANSWERTHEN C. CUSTOMER_SECURITY_QUESTION_ANSWERCREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS CCREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS CELSE '*****'ENDELSE '*****'ENDELSE '*****'ENDACTIVATE ROW ACCESS CONTROLACTIVATE COLUMN ACCESS CONTROL;RETURN CASERETURN CASEENABLE;FOR COLUMN CUSTOMER_SECURITY_QUESTIONENABLE;FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWERENABLE; +WHEN QSYS2. VERIFY_GROUP_FOR_USER( SESSION_USER, 'TELLER') = 1 WHEN QSYS2. VERIFY_GROUP_FOR_USER( SESSION_USER, 'CUSTOMER') = 1 WHEN QSYS2. VERIFY_GROUP_FOR_USER( SESSION_USER, 'ADMIN') = 1 WHEN QSYS2. VERIFY_GROUP_FOR_USER( SESSION_USER, 'TELLER') = 1 WHEN QSYS2. VERIFY_GROUP_FOR_USER( SESSION_USER, 'CUSTOMER') = 1 124 Row and Column Access Control Support in IBM DB2 for iTHEN C. CUSTOMER_TAX_ID THEN( 'XXX-XX-' CONCAT QSYS2. SUBSTR( C. CUSTOMER_TAX_ID, 8, 4)) THEN C. CUSTOMER_TAX_ID THEN C. CUSTOMER_DRIVERS_LICENSE_NUMBER THEN C. CUSTOMER_DRIVERS_LICENSE_NUMBER THEN C. CUSTOMER_DRIVERS_LICENSE_NUMBER CREATE MASK BANK_SCHEMA.MASK_DRIVERS_LICENSE_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C CREATE MASK BANK_SCHEMA.MASK_LOGIN_ID_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ELSE 'XXX-XX-XXXX' END ELSE '*************' END RETURN CASE RETURN CASE ENABLE; FOR COLUMN CUSTOMER_DRIVERS_LICENSE_NUMBER ENABLE; FOR COLUMN CUSTOMER_LOGIN_ID ALTER TABLE BANK_SCHEMA.CUSTOMERS WHEN QSYS2. VERIFY_GROUP_FOR_USER( SESSION_USER, 'ADMIN') = 1 WHEN QSYS2. VERIFY_GROUP_FOR_USER( SESSION_USER, 'CUSTOMER') = 1 WHEN QSYS2. VERIFY_GROUP_FOR_USER( SESSION_USER, 'ADMIN') = 1 WHEN QSYS2. VERIFY_GROUP_FOR_USER( SESSION_USER, 'CUSTOMER') = 1 WHEN QSYS2. VERIFY_GROUP_FOR_USER( SESSION_USER, 'ADMIN') = 1 WHEN QSYS2. VERIFY_GROUP_FOR_USER( SESSION_USER, 'CUSTOMER') = 1 THEN C. CUSTOMER_LOGIN_ID THEN C. CUSTOMER_LOGIN_ID THEN C. CUSTOMER_SECURITY_QUESTION THEN C. CUSTOMER_SECURITY_QUESTION THEN C. CUSTOMER_SECURITY_QUESTION_ANSWER THEN C. CUSTOMER_SECURITY_QUESTION_ANSWER CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C CREATE MASK BANK_SCHEMA.MASK_SECURITY_QUESTION_ANSWER_ON_CUSTOMERS ON BANK_SCHEMA.CUSTOMERS AS C ELSE '*****' END ELSE '*****' END ELSE '*****' END ACTIVATE ROW ACCESS CONTROL ACTIVATE COLUMN ACCESS CONTROL; RETURN CASE RETURN CASE ENABLE; FOR COLUMN CUSTOMER_SECURITY_QUESTION ENABLE; FOR COLUMN CUSTOMER_SECURITY_QUESTION_ANSWER ENABLE; ``` -## Support in IBM DB2 for i + + +## Row and Column Access Control Support in IBM DB2 for i Implement roles and separation of duties -feature of IBM DB2 for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment. +This IBM Redpaper publication provides information about the IBM i 7.2 feature of IBM DB2 for i Row and Column Access Control (RCAC). It offers a broad description of the function and advantages of controlling access to data in a comprehensive and transparent way. This publication helps you understand the capabilities of RCAC and provides examples of defining, creating, and implementing the row permissions and column masks in a relational database environment. Leverage row permissions on the database -This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database +This paper is intended for database engineers, data-centric application developers, and security officers who want to design and implement RCAC as a part of their data control and governance policy. A solid background in IBM i object level security, DB2 for i relational database concepts, and SQL is assumed. -Protect columns by defining column - - +Protect columns by defining column masks -TECHNICAL SUPPORT ORGANIZATION +INTERNATIONAL TECHNICAL SUPPORT ORGANIZATION ## BUILDING TECHNICAL INFORMATION BASED ON PRACTICAL EXPERIENCE IBM Redbooks are developed by the IBM International Technical Support Organization. Experts from IBM, Customers and Partners from around the world create timely technical information based on realistic scenarios. Specific recommendations are provided to help you implement IT solutions more effectively in your environment. -For more information: +For more information: ibm.com /redbooks diff --git a/tests/pdf_snapshots/pdf/sources/right_to_left_01.pdf.md b/tests/pdf_snapshots/pdf/sources/right_to_left_01.pdf.md index 0faa4fe0..29aa2785 100644 --- a/tests/pdf_snapshots/pdf/sources/right_to_left_01.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/right_to_left_01.pdf.md @@ -1,3 +1,3 @@ -## تحسين الإنتاجية وحل المشكالت من خالل البرمجة بلغة R و Python +## تحسين الإنتاجية وحل المشكلات من خلال البرمجة بلغة R و Python -تعتبر البرمجة بلغة R و Python من الأدوات القوية التي يمكن أن تعزز الإنتاجية وتساعد في إيجاد حلول فعالة للمشكالت. يمتلك كل من R و Python ميزات فريدة تجعلها مثالية لتحليل البيانات، مما يسهل على المحللين والعلماء إجراء تحليالت معقدة بطريقة سريعة وفعالة. إذا كان لديك عقلية تحليلية، فإن استخدام هذه اللغات يمكن أن يسهم بشكل كبير في تحسين نتائج العمل. عندما يجتمع التفكير التحليلي مع مهارات البرمجة، يصبح من الممكن معالجة كميات هائلة من البيانات واستخراج الأنماط والتوجهات منها. يمكن للمبرمجين استخدام R و Python لتنفيذ عمليات تحليلية متقدمة، مثل النمذجة الإحصائية وتحليل البيانات الكبيرة. هذا ليس فقط يوفر الوقت، بل يمكن أن يؤدي أي ًضا إلى اتخاذ قرارات أكثر دقة بنا ًء على استنتاجات قائمة على البيانات. عالوة على ذلك، توفر كل من R و Python مكتبات وأدوات غنية تدعم مجموعة واسعة من التطبيقات، من التحليل البياني إلى التعلم الآلي. يمكن للمستخدمين االستفادة من هذه المكتبات لتطوير حلول مبتكرة للمشكالت المختلفة. على سبيل المثال، يمكن استخدام مكتبة pandas في Python إلدارة البيانات بكفاءة، بينما توفر R أدوات قوية للرسم البياني والتحليل الإحصائي، مما يجعلها مثالية للباحثين والمحللين. في النهاية، يمكن أن تؤدي البرمجة بلغة R و Python مع عقلية تحليلية إلى تحسين الإنتاجية وتوفير حلول مبتكرة للمشكالت المعقدة. إن القدرة على تحليل البيانات بشكل فعال وتطبيق الأساليب البرمجية المناسبة يمكن أن تكون لها تأثيرات إيجابية بعيدة المدى على الأداء الشخصي والمهني. +تعتبر البرمجة بلغة R و Python من الأدوات القوية التي يمكن أن تعزز الإنتاجية وتساعد في إيجاد حلول فعالة للمشكلات. يمتلك كل من R و Python ميزات فريدة تجعلها مثالية لتحليل البيانات، مما يسهل على المحللين والعلماء إجراء تحليلات معقدة بطريقة سريعة وفعالة. إذا كان لديك عقلية تحليلية، فإن استخدام هذه اللغات يمكن أن يسهم بشكل كبير في تحسين نتائج العمل. عندما يجتمع التفكير التحليلي مع مهارات البرمجة، يصبح من الممكن معالجة كميات هائلة من البيانات واستخراج الأنماط والتوجهات منها. يمكن للمبرمجين استخدام R و Python لتنفيذ عمليات تحليلية متقدمة، مثل النمذجة الإحصائية وتحليل البيانات الكبيرة. هذا ليس فقط يوفر الوقت، بل يمكن أن يؤدي أيضًا إلى اتخاذ قرارات أكثر دقة بناءً على استنتاجات قائمة على البيانات. علاوة على ذلك، توفر كل من R و Python مكتبات وأدوات غنية تدعم مجموعة واسعة من التطبيقات، من التحليل البياني إلى التعلم اللآي. يمكن للمستخدمين الاستفادة من هذه المكتبات لتطوير حلول مبتكرة للمشكلات المختلفة. على سبيل المثال، يمكن استخدام مكتبة pandas في Python لإدارة البيانات بكفاءة، بينما توفر R أدوات قوية للرسم البياني والتحليل الإحصائي، مما يجعلها مثالية للباحثين والمحللين. في النهاية، يمكن أن تؤدي البرمجة بلغة R و Python مع عقلية تحليلية إلى تحسين الإنتاجية وتوفير حلول مبتكرة للمشكلات المعقدة. إن القدرة على تحليل البيانات بشكل فعال وتطبيق الأساليب البرمجية المناسبة يمكن أن تكون لها تأثيرات إيجابية بعيدة المدى على الأداء الشخصي والمهني. diff --git a/tests/pdf_snapshots/pdf/sources/right_to_left_02.pdf.md b/tests/pdf_snapshots/pdf/sources/right_to_left_02.pdf.md index 0e064869..4eb87f6b 100644 --- a/tests/pdf_snapshots/pdf/sources/right_to_left_02.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/right_to_left_02.pdf.md @@ -1,9 +1,9 @@ -تكايف ا لسيد رئيس ا لجماورية لاخ بخ لعمل ياى تح يق يدد من الأهودا ياى رعساخ: وضع ماف بهخء الإنسخن المصري ياى رعس قخئموة الأولويوخت، لخ صوة فووا مجوخ حت ا لصووحة وا للعاوويل، العمول ياووى تح يوق معوودحت نمووو قويوووة ومسووولدا مة و وووخ ماة فوووا عذاوووف ال لخيوووخت، و ووو ا الح وووخ ياوووى محوددا ت الأمون ال ووما المصوري فوا ضووء اللحوديخت الإقايميوة والدوليوة، ومواصواة واوود تلووير ا لماوخ ر ة السيخ سوية، وا سولمرا ر ملخ بعوة ما وخ ت الأمووون واحسووول رار ومكخفحوووة الإرهوووخ ، تلووووير ما وووخت ال خفوووة والوووويا ا لوووو،ها، وا لبلوووخ ا لوووديها ا لمعلووودل ياوووى الهحوووو الووو ي يرسووو م وووخهيل ا لموا،هة وا لسام المجلمعا. +وعليه، فإن الحكومة المصرية تضع صوو عييهاوخ لوال المر اوة الم باوة تكايف السيد رئيس الجماورية لاخ بخلعمل ياى تح يق يدد من الأهودا ياى رعساخ: وضع ماف بهخء الإنسخن المصري ياى رعس قخئموة الأولويوخت، لخصوة فووا مجوخحت الصووحة واللعاوويل، العمول ياووى تح يوق معوودحت نمووو قويوووة ومسووولدامة و وووخماة فوووا عذاوووف ال لخيوووخت، و ووو ا الح وووخ ياوووى محوددات الأمون ال ووما المصوري فوا ضووء اللحوديخت الإقايميوة والدوليوة، ومواصواة واوود تلووير الماوخر ة السيخسوية، واسولمرار ملخبعوة ما وخت الأمووون واحسووول رار ومكخفحوووة الإرهوووخ ، تلووووير ما وووخت ال خفوووة والوووويا الوووو،ها، والبلوووخ الوووديها المعلووودل ياوووى الهحوووو الووو ي يرسووو م وووخهيل الموا،هة والسام المجلمعا. -ووف ًخ لمخ سبق، يسولاد برنوخما الحكوموة المصورية لوال ال لور)2024 - 2026(تح يق عربعة عهدا اسلراتيجية رئيسة، وها ياى الهحو الآتا: +ووف ً خ لمخ سبق، يسولاد برنوخما الحكوموة المصورية لوال ال لور ) - 2024 تح يق عربعة عهدا (2026 اسلراتيجية رئيسة، وها ياى الهحو الآتا: -تجدر الإ خر إلى عنه قد تل تحديد مسولادفخت البرنوخما بخحسولهخد باوكل رئووويس ياوووى مسووولادفخ ت ر يوووة مصووور ،2023 وتوصووويخ ت واسوووخ ت ا لحووووا ر ا لوو،ها، ومسولادفخ ت ا لوو ا را ت، وا لبرنوخ ما الوو،ها ليصوا خت الايكايوة، +تجدر الإ خر إلى عنه قد تل تحديد مسولادفخت البرنوخما بخحسولهخد باوكل رئووويس ياوووى مسووولادفخت ر يوووة مصووور ، 2023 وتوصووويخت واسوووخت الحووووار الوو،ها، ومسولادفخت الوو ارات، والبرنوخما الوو،ها ليصوا خت الايكايوة، ومبلاف احسلراتيجيخت الو،هية. diff --git a/tests/pdf_snapshots/pdf/sources/right_to_left_03.pdf.md b/tests/pdf_snapshots/pdf/sources/right_to_left_03.pdf.md index 095df189..f05605ed 100644 --- a/tests/pdf_snapshots/pdf/sources/right_to_left_03.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/right_to_left_03.pdf.md @@ -1,13 +1,21 @@ -| بورس1403/09/19 | تاريخ ارائه مدارک | -| - | - | -| 1 4 0 3/ 1 0 / 0 4 | تاريخ پذيرش | -| 436 | شماره جلسه کميته عرضه | -| 1 4 0 3/ 1 0 / 0 5 | تاريخ درج اميدنامه | -| کارگزاری آرمون بورس | مشاور پذيرش | -| اساس قيمت های جهانی | نحوة تعيين قيمت پايه پس از پذيرش کاال در بورسبر | -| از توليد ساليانه يا 47.50 0 تن | حداقل درصد عرضه از توليد/ کل فروش/ فروش داخلیحداقل%50 | -| 5% آخرين محموله قابل تحويل | خطای مجاز تحويل | +## اميدنامه پذيرش در بازار اصلی - کالای داخلی + +## شرکت بورس کالای ايران + +## 5 - -2 استاندارد کالا + +## -3 پذيرش در بورس + +| بورس1403/09/19 | تاريخ ارائه مدارک | +|--------------------------------|-------------------------------------------------------| +| 1 4 0 3/ 1 0 / 0 4 | تاريخ پذيرش | +| 436 | شماره جلسه کميته عرضه | +| 1 4 0 3/ 1 0 / 0 5 | تاريخ درج اميدنامه | +| کارگزاری آرمون بورس | مشاور پذيرش | +| اساس قيمت های جهانی | نحوة تعيين قيمت پايه پس از پذيرش کالا در بورسبر | +| از توليد ساليانه يا 47.50 0 تن | حداقل درصد عرضه از توليد/ کل فروش/ فروش داخلیحداقل%50 | +| 5% آخرين محموله قابل تحويل | خطای مجاز تحويل | diff --git a/tests/pdf_snapshots/pdf/sources/skipped_1page.pdf.md b/tests/pdf_snapshots/pdf/sources/skipped_1page.pdf.md index 8c15c95f..dc68fbc3 100644 --- a/tests/pdf_snapshots/pdf/sources/skipped_1page.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/skipped_1page.pdf.md @@ -1,47 +1,35 @@ - 보안분 - +## [그림 4-5] 8대 미래 유망 보안기술 도출 - - -## 스마트 데이터 컪비스 훟심 컴퓨팅 보안 기술 + -(Edge Computing) +엣지컴퓨팅 (Edge Computing) -(Next AI) +차세대 AI (Next AI) -(DATA) - - - -## 블록체인에컪픦 팢 솒빪 방지 짝 쭖쩣 먾래 헏방지 기술 - -차세대 AI +차세대 AI (Next AI) - - -## 젊기몒 인터페이스 몋에컪픦 캏오윦이캏맞지 기술 - @@ -52,11 +40,9 @@ 웨어러블 컨트롤 인터페이스 -(Wearable Control - -## [그림 4-5] 8대 미래 유망 보안기술 도출 +(Wearable Control Interfaces) -묾푷 차세대 비체(1A7)킹 방지읊 퓒 칺몮 대픟 기술 +(Hyper-Connected Networking) 84 ICT 기술변화에 따른 미래 보안기술 전망 보고서 diff --git a/tests/pdf_snapshots/pdf/sources/skipped_2pages.pdf.md b/tests/pdf_snapshots/pdf/sources/skipped_2pages.pdf.md index 6f80f346..2bb335c0 100644 --- a/tests/pdf_snapshots/pdf/sources/skipped_2pages.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/skipped_2pages.pdf.md @@ -6,48 +6,36 @@ - +## [그림 4-5] 8대 미래 유망 보안기술 도출 - - -## 스마트 데이터 컪비스 훟심 컴퓨팅 보안 기술 + -(Edge Computing) +엣지컴퓨팅 (Edge Computing) -(Next AI) +차세대 AI (Next AI) -(DATA) - - - -## 블록체인에컪픦 팢 솒빪 방지 짝 쭖쩣 먾래 헏방지 기술 - -차세대 AI +차세대 AI (Next AI) - - -## 젊기몒 인터페이스 몋에컪픦 캏오윦이캏맞지 기술 - @@ -58,11 +46,9 @@ 웨어러블 컨트롤 인터페이스 -(Wearable Control - -## [그림 4-5] 8대 미래 유망 보안기술 도출 +(Wearable Control Interfaces) -묾푷 차세대 비체(1A7)킹 방지읊 퓒 칺몮 대픟 기술 +(Hyper-Connected Networking) 84 ICT 기술변화에 따른 미래 보안기술 전망 보고서 diff --git a/tests/pdf_snapshots/pdf/sources/table_mislabeled_as_picture.pdf.md b/tests/pdf_snapshots/pdf/sources/table_mislabeled_as_picture.pdf.md index 53980fc4..ab83293a 100644 --- a/tests/pdf_snapshots/pdf/sources/table_mislabeled_as_picture.pdf.md +++ b/tests/pdf_snapshots/pdf/sources/table_mislabeled_as_picture.pdf.md @@ -1,77 +1,84 @@ -| | 209„ They work in parallel to State funded private practitioners who take assignments to represent people eligible for legal aid | -| - | - | -| | They coordinate appointments of private practitioners (ex officio, or panel appoint ments) to legal aid cases | -| | They supervise, coach or mentor private practitioners who take legal aid cases | -| | They conduct or organize training sessions for staff lawyers/paralegals | -| | They conduct or organize training sessions for all providers of legal aid, including both staff and private lawyers/paralegals | -| | Other (Please specify) | -| | Not applicable, there is no institutional legal aid provider | - -- represent people eligible for legal aid +| | 209„ They work in parallel to State funded private practitioners who take assignments to represent people eligible for legal aid | +|----|------------------------------------------------------------------------------------------------------------------------------------| +| | They coordinate appointments of private practitioners (ex officio, or panel appoint ments) to legal aid cases | +| | They supervise, coach or mentor private practitioners who take legal aid cases | +| | They conduct or organize training sessions for staff lawyers/paralegals | +| | They conduct or organize training sessions for all providers of legal aid, including both staff and private lawyers/paralegals | +| | Other (Please specify) | +| | Not applicable, there is no institutional legal aid provider | + +- „ They work in parallel to State funded private practitioners who take assignments to represent people eligible for legal aid - „ They coordinate appointments of private practitioners (ex officio, or panel appointments) to legal aid cases - „ They supervise, coach or mentor private practitioners who take legal aid cases - „ They conduct or organize training sessions for staff lawyers/paralegals - „ They conduct or organize training sessions for all providers of legal aid, including both staff and private lawyers/paralegals -- „ Other (Please specify) _____________________________________________________ +- „ Other (Please specify) \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ - „ Not applicable, there is no institutional legal aid provider -- 23. If your country has an institutional legal aid provider (e.g. public defender), what is the maximum caseload per lawyer at one time? -| 23. | 209„ If your country has an institutional legal aid provider (e.g. public defender), what is the maximum caseload per lawyer at one time? _______ at the national (federal) level | -| - | - | +23. If your country has an institutional legal aid provider (e.g. public defender), what is the maximum caseload per lawyer at one time? + +| 23. | 209„ If your country has an institutional legal aid provider (e.g. public defender), what is the maximum caseload per lawyer at one time? _______ at the national (federal) level | +|-------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| -- _______ at the national (federal) level -- _______ at the regional (district) level -- _______ at the local (municipal) level +- \_\_\_\_\_\_\_ at the national (federal) level +- \_\_\_\_\_\_\_ at the regional (district) level +- \_\_\_\_\_\_\_ at the local (municipal) level - „ There is no such limitation -- 24. If your country has an institutional legal aid provider (e.g. public defender), do the staff lawyers coordinate to uniformly challenge common violations of national and international due process rights and human rights? + +24. If your country has an institutional legal aid provider (e.g. public defender), do the staff lawyers coordinate to uniformly challenge common violations of national and international due process rights and human rights? + - „ Yes, at the national (federal) level - „ Yes, at regional (district) level - „ Yes, at the local (municipal) level - „ No -| 25. | 209„ If your country has an institutional legal aid provider (e.g. public defender), does it have specialized providers and/or units for representing child victims, child witness es or suspected and accused children? | -| - | - | -| | Yes, at the national (federal) level | -| „ | Yes, at regional (district) level | -| „ | Yes, at the local (municipal) level | -| „ | No | +| 25. | 209„ If your country has an institutional legal aid provider (e.g. public defender), does it have specialized providers and/or units for representing child victims, child witness es or suspected and accused children? | +|-------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| | Yes, at the national (federal) level | +| „ | Yes, at regional (district) level | +| „ | Yes, at the local (municipal) level | +| „ | No | + +25. If your country has an institutional legal aid provider (e.g. public defender), does it have specialized providers and/or units for representing child victims, child witnesses or suspected and accused children? -- 25. If your country has an institutional legal aid provider (e.g. public defender), does it have specialized providers and/or units for representing child victims, child witnesses or suspected and accused children? - „ Yes, at the national (federal) level -- „ Yes, at regional (district) level +- Yes, at regional (district) level - „ Yes, at the local (municipal) level -- are there national guidelines on how students are supervised in providing legal aid services? (Please select all that apply) + +26. If your country allows legal aid services through university-based student law clinics, are there national guidelines on how students are supervised in providing legal aid services? (Please select all that apply) + - „ Yes, there are specific guidelines for non-lawyers providing legal aid services - „ Yes, there are specific guidelines on faculty/student ratios - „ No, it is up to the discretion of each university - „ Don't know - „ There are no university-based student law clinics -| 27. | 209„ If your country allows legal aid services through university-based student law clinics, what type of legal aid services is a student authorized to undertake? (Please select all that apply) | -| - | - | -| „ | There is no limitation; they have the same authority as lawyers | -| „ | They can represent people in administrative or civil law hearings | -| „ | They can provide primary legal aid (legal advice) | -| „ | They can prepare legal documents | -| „ | They can represent people in court in civil and criminal matters | -| „ | They have the same authority as lawyers in criminal cases of low to mid gravity | -| „ | They can provide a full range of legal services in criminal cases regardless of gravity | -| „ | They can conduct mediation | -| „ | They are authorized to provide only those services that a faculty member or practic ing lawyer supervises | -| „ | Don’t know | -| „ | Other (Please specify) _____________________________________________________ | - -- 27. If your country allows legal aid services through university-based student law clinics, what type of legal aid services is a student authorized to undertake? (Please select all that apply) -- 28. Are specialized legal aid services provided focusing on specific disadvantaged population groups? If yes, please indicate to whom these services are provided, and whether they are provided by State-funded legal aid, civil society organizations, or both. +| 27. | 209„ If your country allows legal aid services through university-based student law clinics, what type of legal aid services is a student authorized to undertake? (Please select all that apply) | +|-------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| „ | There is no limitation; they have the same authority as lawyers | +| „ | They can represent people in administrative or civil law hearings | +| „ | They can provide primary legal aid (legal advice) | +| „ | They can prepare legal documents | +| „ | They can represent people in court in civil and criminal matters | +| „ | They have the same authority as lawyers in criminal cases of low to mid gravity | +| „ | They can provide a full range of legal services in criminal cases regardless of gravity | +| „ | They can conduct mediation | +| „ | They are authorized to provide only those services that a faculty member or practic ing lawyer supervises | +| „ | Don’t know | +| „ | Other (Please specify) _____________________________________________________ | + +27. If your country allows legal aid services through university-based student law clinics, what type of legal aid services is a student authorized to undertake? (Please select all that apply) + +- specialized legal aid services 28. Are provided focusing on specific disadvantaged population groups? If yes, please indicate to whom these services are provided, and whether they are provided by State-funded legal aid, civil society organizations, or both. (Please select all that apply) -| | 209„ | State funded legal aid | CSOs | -| - | - | - | - | -| y | Persons with disabilities | * | * | -| y | Children | * | * | -| y | Women | * | * | -| y | The elderly | * | * | -| y | Migrants | * | * | -| y | Refugees, asylum seekers, or stateless persons | * | * | -| y | Internally displaced persons | * | * | +| | 209„ | State funded legal aid | CSOs | +|----|------------------------------------------------|--------------------------|--------| +| y | Persons with disabilities | * | * | +| y | Children | * | * | +| y | Women | * | * | +| y | The elderly | * | * | +| y | Migrants | * | * | +| y | Refugees, asylum seekers, or stateless persons | * | * | +| y | Internally displaced persons | * | * | diff --git a/tests/pdf_snapshots/scanned/sources/nemotron_multipage.pdf.md b/tests/pdf_snapshots/scanned/sources/nemotron_multipage.pdf.md index 23a83a33..4ccde8ad 100644 --- a/tests/pdf_snapshots/scanned/sources/nemotron_multipage.pdf.md +++ b/tests/pdf_snapshots/scanned/sources/nemotron_multipage.pdf.md @@ -1,9 +1,3 @@ -Docling bundles PDF document conversion to JSON and Markdown in an easy self contained package +Docling bundles PDF document conversion to JSON and Markdown in an easy self contained package aerosaeoe e o a -aerosaeoe e o a - -H Wep9ps 1se.uuP1 po - -teodnn nasm 88 ekdasd bdp0M - -e 00 a K C a p +H W ep 9 ps 1s e. uu P1 po te od nn na sm 88 ek da sd bd p0 M e 00 a K C a p diff --git a/tests/pdf_snapshots/scanned/sources/ocr_test_rotated_270.pdf.md b/tests/pdf_snapshots/scanned/sources/ocr_test_rotated_270.pdf.md index 538f2b79..4d299e55 100644 --- a/tests/pdf_snapshots/scanned/sources/ocr_test_rotated_270.pdf.md +++ b/tests/pdf_snapshots/scanned/sources/ocr_test_rotated_270.pdf.md @@ -1 +1 @@ -H Wep9ps 1se.uuP1 po +H W ep 9 ps 1s e. uu P1 po diff --git a/tests/pdf_snapshots/scanned/sources/ocr_test_rotated_90.pdf.md b/tests/pdf_snapshots/scanned/sources/ocr_test_rotated_90.pdf.md index c8bc9593..a8bf7add 100644 --- a/tests/pdf_snapshots/scanned/sources/ocr_test_rotated_90.pdf.md +++ b/tests/pdf_snapshots/scanned/sources/ocr_test_rotated_90.pdf.md @@ -1,3 +1 @@ -teodnn nasm 88 ekdasd bdp0M - -e 00 a K C a p +te od nn na sm 88 ek da sd bd p0 M e 00 a K C a p diff --git a/tests/pdf_snapshots/scanned/sources/old_newspaper.png.md b/tests/pdf_snapshots/scanned/sources/old_newspaper.png.md new file mode 100644 index 00000000..bb3003db --- /dev/null +++ b/tests/pdf_snapshots/scanned/sources/old_newspaper.png.md @@ -0,0 +1,117 @@ +## French Institute Creates A French Community At OU + +ology.A demonstration class of first year students of French at the high school and junior high schoollevel is to be conducted by Pierre Simonian, the Institute's teacher in charge of demonstrationandmethodology. + +strengthen the teacher's knowledge and control of the langlage, + +AnNDEAFrench Institute opened its doors on OU's campuslastweekwith the arrival of its 48participants,eightexperienced professors of French, and four graduate assistants'and natives of France. + +Lastweek marked the beginning of an intensive program in the French language,methods of teaching the language,and the culture of France.Faculty and Institute participants meet together instudysessionsallmorning and afternoon,Classes are conducted entirely in French, with the exception of courses in language analysis and method- + +The participants eatlunchand dinner together and speak,it is expected,nothing but French. After dinner,participants share extracurricular activitiestogether--films,music,lectures.Often the arranged program of activi- + +Theparticipants in the Institute(14menand34women,10 of them nuns)are teachers of French in junior and senior high schoolsinMichiganand14other states.Director of the Institute Is Ou's assistant professor of French,Don Iodice.Also from OU's staff are Mme Genevieve Prevost, assistant director of the Institute,-and M. Charles Forton,in charge of language Improvement. Familiar to OU's campus as well are M. et Mme Francis Tafoya,who will be workingwithcontemporary French-civilization andlanguage improvementrespectively.M. Tafoya was formerly the head of OU's department of foreign languages. + + + +## MITZELFELD'S + +ROCHESTER + +The object of the Institute is twofold.The iirst objective is to bring the teachers up to date In theirsubject area,Because of rapid changes in content,approach,.and teaching techniques, trueofsuchfieldsasmathematics andscience as well asforeign languages,the National Defense EducationAct has established summer institutes all over the country to help teachers keep pace with advances within their own fields.The second goal of theFrench institute will be to + + + +## HILLS THEATRE + +Rochester + +Friday -Tuesday ONE SHOWING NIGHTLY 7:30 SUNDAY2:30&7:30 Programlnformation651-8311 + +## FIRSTTIME AT POPULARPRICES + + + + + +IR LaDY + +Daiey + +OF ROCHESTER 743N.MAIN + + + +Try our + +FIESTAS + +Winner of 8Academy Awards + +## AUDREY HEPBURN·REXHARRISONSTANLEYHOLLOWAY + +SPLITS + +Hours Hours 11A.M.to11P.M 11A.M.to11P.M + +Hours Hours + +TECHNICOLOR SUPER PANAVISION 7OFROMWARNER BRO + +11A.M.to11P.M 11A.M.to11P.M + +GETDUNLOPIMPORTQUALITY INTHEAMERICAN MADE GOLD SEAL FULL 4PLY (NOT2PLY)CONSTRUCTION NO THUMP WITHTYREX CORD NYLONALSOAVAILABLE） Certified Safe At A SUSTAINED 100 M.P.H. Wholesale Prices to O.U.Students & Faculty on Passenger Car, Sports Car, Radial Ply & Racing Tires + +BruceRobertson + +Tom Hill + +Bill Basinger + +## R.B. DUNLOP TIRE SALES + +Phone:651-3422 or 673-9227after5p.m.call 334-6452 + +## Arnold Rexall Pharmacy + +ties cohtinuesuntil sevenoreight in the evening.When they return tothe dormitory(allparticipants are housed in AnibalHousethis summer),they areencouragedto continue speaking nothingbut French.They are bound together in a linguistic and cultural island, andtheirveryisolationwithinthe French language and their tightnessasa community is meant to Increase theirfacility with the language + +Prescriptions Cosmetics SundryItems Liquor,Beer,Wine + +2026OpdykeRd. Corner of Pontiac Road 333-7033 + +## CLASSIFIED ADS + +Wanted:Man or womanfullor part time.Direct Sales Prestige Products.Call Mrs.Lodge,682- 5540or335-9937forappt + +## TUKO + +872E.Auburn,NearJohnR.Rochester UL2-5363 + + + +Treat* the* eptire family +$ to the happiest entertaipment ofall! + + + +## HAV FULL HOUSE? + +Then use our storage service for all your clothes. It includes complete protection for all your garments, including your furs. Everything is thoroughly cleaned and mothproofed before storing. + +## P.S.FREE... + + + +s the wider your choice.Bring your Springcleaning in nov + + + + + +## M.G.M. Cleaners, Inc. + +In Business for 2l Years Auburn Rd..at Adams Crooks Rd..at Auburn Mound Rd..at 23 Mile Rd. Also on Campus at Oakland Unirersity B Plants and Stores Serving Oakland and Macomb Counties + +DUNLOP + +OpenytoMMu My huSat diff --git a/tests/pdf_snapshots/scanned/sources/qr_bill_example.jpg.md b/tests/pdf_snapshots/scanned/sources/qr_bill_example.jpg.md new file mode 100644 index 00000000..9c4377b5 --- /dev/null +++ b/tests/pdf_snapshots/scanned/sources/qr_bill_example.jpg.md @@ -0,0 +1,59 @@ +## Fictitiousexampleforillustrationpurposes + +Ms PiaRutschmann Marktgasse28 9400Rorschach + +DearMs.Rutschmann, + +Wearebillingyouasfollowsforcompletionoftheassignedactivities + +## Billno.3139 + +RobertSchneiderAG RueduLac1268 2501Bie + +059/9876540 + +E-Mail robert@rschneider.ch www.rschneider.ch + +Internet: + +Date + +01.07.2020 + +| | | | | | +|----|----|----|----|----| +| | | | | | +| | | | | | +| | | | | | +| | | | | | +| | | | | | +| | | | | | +| | | | | | + +Thankyoufortheassignment.Pleasepaythebillamountwithin3Odays + +Yourssincerely + +RobertSchneider + +## Receipt + +Paymentpart + +Account/Payableto CH5800791123000889012 RobertSchneiderAG RueduLac1268 2501Bie + + + +Payableby PiaRutschmann Marktgasse28 9400Rorschach + +Amount + +3.949.75 + +Acceptancepoint + +Account/Payableto CH5800791123000889012 RobertSchneiderAG RueduLac1268 2501Bie + +Additionalinformation BillNo.3139forgardenworkanddisposalof cuttings + +Payableby PiaRutschmann Marktgasse28 9400Rorschach diff --git a/tests/pdf_snapshots/scanned/sources/sample_with_rotation_mismatch.pdf.md b/tests/pdf_snapshots/scanned/sources/sample_with_rotation_mismatch.pdf.md index 50001b92..256d8c6a 100644 --- a/tests/pdf_snapshots/scanned/sources/sample_with_rotation_mismatch.pdf.md +++ b/tests/pdf_snapshots/scanned/sources/sample_with_rotation_mismatch.pdf.md @@ -2,11 +2,11 @@ ## THE SLEREXE COMPANY LIMITED -SAPORSLANE-BOOLE.DORSET-BH258ERSAPORSLANE-BOOLE.DORSET-BH258ER +SAPORSLANE-BOOLE.DORSET-BH258ER SAPORSLANE-BOOLE.DORSET-BH258ER -SAPORSLANE-BOOLE.DORSET-BH258ERSAPORSLANE-BOOLE.DORSET-BH258ER TELEPHONEB0OLB(94513)51617-TELEX123456TELEPHONEB0OLE(94513)51617-TELEX123456 +SAPORSLANE-BOOLE.DORSET-BH258ER SAPORSLANE-BOOLE.DORSET-BH258ER TELEPHONEB0OLB(94513)51617-TELEX123456 TELEPHONEB0OLE(94513)51617-TELEX123456 -TELEPHONEB0OLB(94513)51617-TELEX123456TELEPHONEB0OLE(94513)51617-TELEX123456 +TELEPHONEB0OLB(94513)51617-TELEX123456 TELEPHONEB0OLE(94513)51617-TELEX123456 18th January,1972. From a1b7855e8c937771961423bd5818af0c8c0e53b0 Mon Sep 17 00:00:00 2001 From: artiz Date: Mon, 29 Jun 2026 18:17:11 +0200 Subject: [PATCH 40/42] =?UTF-8?q?refactor:=20rename=20tests/pdf=5Fsnapshot?= =?UTF-8?q?s=20=E2=86=92=20tests/snapshots?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The snapshot baseline covers every format the PDF/image pipeline emits output for (pdf, scanned, tiff, webp, odf, latex/html-embedded images, mets…), not just PDF, so the directory name was misleading. Rename it and update pdf_conformance.sh's references. The snapshot example takes the output dir as an argument, so nothing else changes. 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snapshots}/scanned/sources/sample_with_rotation_mismatch.pdf.md (100%) rename tests/{pdf_snapshots => snapshots}/tiff/sources/2206.01062.tif.md (100%) rename tests/{pdf_snapshots => snapshots}/webp/sources/webp-test.webp.md (100%) diff --git a/scripts/pdf_conformance.sh b/scripts/pdf_conformance.sh index ca160c1f..f21a0716 100755 --- a/scripts/pdf_conformance.sh +++ b/scripts/pdf_conformance.sh @@ -1,6 +1,6 @@ #!/usr/bin/env bash # Regenerate PDF output for the test corpus and diff it against the committed -# snapshot baseline (tests/pdf_snapshots/). The pipeline is deterministic, so a +# snapshot baseline (tests/snapshots/). The pipeline is deterministic, so a # clean checkout should report every fixture EXACT; a non-zero diff means the # output drifted. Run scripts/pdf_setup.sh first to fetch the libs/models. set -euo pipefail @@ -23,7 +23,7 @@ cargo run --release -q -p fleischwolf-pdf --example snapshot -- tests/data "$tmp exact=0; drift=0; tot=0 while IFS= read -r snap; do - rel="${snap#tests/pdf_snapshots/}" + rel="${snap#tests/snapshots/}" gen="$tmp/$rel" tot=$((tot + 1)) if [ -f "$gen" ] && diff -q "$snap" "$gen" >/dev/null 2>&1; then @@ -33,7 +33,7 @@ while IFS= read -r snap; do d=$(diff "$snap" "$gen" 2>/dev/null | grep -cE '^[<>]' || true) printf " %-55s %s\n" "$rel" "${d:-MISSING}" fi -done < <(find tests/pdf_snapshots -name '*.md' | sort) +done < <(find tests/snapshots -name '*.md' | sort) echo "PDF snapshot conformance: $exact/$tot exact ($drift drifted)" [ "$drift" -eq 0 ] diff --git a/tests/pdf_snapshots/html/sources/example_image_01.png.md b/tests/snapshots/html/sources/example_image_01.png.md similarity index 100% rename from tests/pdf_snapshots/html/sources/example_image_01.png.md rename to tests/snapshots/html/sources/example_image_01.png.md diff --git a/tests/pdf_snapshots/latex/sources/1706.03762/Figures/ModalNet-19.png.md b/tests/snapshots/latex/sources/1706.03762/Figures/ModalNet-19.png.md similarity index 100% rename from tests/pdf_snapshots/latex/sources/1706.03762/Figures/ModalNet-19.png.md rename to tests/snapshots/latex/sources/1706.03762/Figures/ModalNet-19.png.md diff --git a/tests/pdf_snapshots/latex/sources/1706.03762/Figures/ModalNet-20.png.md b/tests/snapshots/latex/sources/1706.03762/Figures/ModalNet-20.png.md similarity index 100% rename from tests/pdf_snapshots/latex/sources/1706.03762/Figures/ModalNet-20.png.md rename to tests/snapshots/latex/sources/1706.03762/Figures/ModalNet-20.png.md diff --git a/tests/pdf_snapshots/latex/sources/1706.03762/Figures/ModalNet-21.png.md b/tests/snapshots/latex/sources/1706.03762/Figures/ModalNet-21.png.md similarity index 100% rename from tests/pdf_snapshots/latex/sources/1706.03762/Figures/ModalNet-21.png.md rename 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diff --git a/tests/pdf_snapshots/scanned/sources/ocr_test_rotated_180.pdf.md b/tests/snapshots/scanned/sources/ocr_test_rotated_180.pdf.md similarity index 100% rename from tests/pdf_snapshots/scanned/sources/ocr_test_rotated_180.pdf.md rename to tests/snapshots/scanned/sources/ocr_test_rotated_180.pdf.md diff --git a/tests/pdf_snapshots/scanned/sources/ocr_test_rotated_270.pdf.md b/tests/snapshots/scanned/sources/ocr_test_rotated_270.pdf.md similarity index 100% rename from tests/pdf_snapshots/scanned/sources/ocr_test_rotated_270.pdf.md rename to tests/snapshots/scanned/sources/ocr_test_rotated_270.pdf.md diff --git a/tests/pdf_snapshots/scanned/sources/ocr_test_rotated_90.pdf.md b/tests/snapshots/scanned/sources/ocr_test_rotated_90.pdf.md similarity index 100% rename from tests/pdf_snapshots/scanned/sources/ocr_test_rotated_90.pdf.md rename to tests/snapshots/scanned/sources/ocr_test_rotated_90.pdf.md diff --git a/tests/pdf_snapshots/scanned/sources/old_newspaper.png.md b/tests/snapshots/scanned/sources/old_newspaper.png.md similarity index 100% rename from tests/pdf_snapshots/scanned/sources/old_newspaper.png.md rename to tests/snapshots/scanned/sources/old_newspaper.png.md diff --git a/tests/pdf_snapshots/scanned/sources/qr_bill_example.jpg.md b/tests/snapshots/scanned/sources/qr_bill_example.jpg.md similarity index 100% rename from tests/pdf_snapshots/scanned/sources/qr_bill_example.jpg.md rename to tests/snapshots/scanned/sources/qr_bill_example.jpg.md diff --git a/tests/pdf_snapshots/scanned/sources/sample_with_rotation_mismatch.pdf.md b/tests/snapshots/scanned/sources/sample_with_rotation_mismatch.pdf.md similarity index 100% rename from tests/pdf_snapshots/scanned/sources/sample_with_rotation_mismatch.pdf.md rename to tests/snapshots/scanned/sources/sample_with_rotation_mismatch.pdf.md diff --git a/tests/pdf_snapshots/tiff/sources/2206.01062.tif.md b/tests/snapshots/tiff/sources/2206.01062.tif.md similarity index 100% rename from tests/pdf_snapshots/tiff/sources/2206.01062.tif.md rename to tests/snapshots/tiff/sources/2206.01062.tif.md diff --git a/tests/pdf_snapshots/webp/sources/webp-test.webp.md b/tests/snapshots/webp/sources/webp-test.webp.md similarity index 100% rename from tests/pdf_snapshots/webp/sources/webp-test.webp.md rename to tests/snapshots/webp/sources/webp-test.webp.md From be31a15e5f186135f7dcbfd0a4c1e2a9cb22b23a Mon Sep 17 00:00:00 2001 From: artiz Date: Mon, 29 Jun 2026 18:18:52 +0200 Subject: [PATCH 41/42] docs: refresh PDF conformance notes in COMPARING.md The PDF groundtruth is regenerated from live docling (padded tables), so the stale "compact table / predates padded serializer" footnote is wrong. Update it, and describe the remaining gaps as model-level (TableFormer structure, layout classification, reading order, RTL doubles). Co-Authored-By: Claude Opus 4.8 (1M context) --- COMPARING.md | 17 ++++++++++------- 1 file changed, 10 insertions(+), 7 deletions(-) diff --git a/COMPARING.md b/COMPARING.md index 0c064aea..a09e5f74 100644 --- a/COMPARING.md +++ b/COMPARING.md @@ -179,11 +179,12 @@ case — see the divergence table below. | **HTML** | **28 / 33** | 28 / 33 | | **PDF** | **4 / 14** † | 5 / 14 | -> † The pure-parse backends above are scored against **live** docling. **PDF** is -> scored against the committed groundtruth corpus (`tests/data/pdf/groundtruth`) -> instead: it is a discriminative ML reconstruction pipeline (not a deterministic -> parse), and the corpus predates docling-core's padded table serializer, so PDF -> output uses the compact `| a | b |` table form. +> † The pure-parse backends above are scored against **live** docling. **PDF** is a +> discriminative ML reconstruction pipeline (not a deterministic parse), so it is +> scored against a committed groundtruth corpus (`tests/data/pdf/groundtruth`) that +> is **regenerated from live docling** and therefore matches `scripts/conformance.sh +> pdf` (padded GitHub tables, current docling text). Within-one adds +> `right_to_left_01` (a 2-line diff). **PDF** (`*.pdf`) ports docling's *standard* (discriminative) PDF pipeline. pdfium extracts the text layer (glyph cells + bounding boxes) and renders each page to a @@ -201,8 +202,10 @@ that previously capped conformance (inter-run spacing like `LABEL :`, justified double-spacing, lam-alef ordering). Byte-exact today: `picture_classification`, `code_and_formula`, `2305.03393v1-pg9` (**including its TableFormer-reconstructed table, cell for cell**), and `multi_page`. The rest are structurally correct but -not yet byte-exact — the remaining gaps are justified RTL double-spaces -(`right_to_left_01`, within one line) and table reading-order on dense papers. +not yet byte-exact; the remaining gaps are model-level — TableFormer multi-row +header/span structure on dense papers, layout classification (a TOC read as a +picture, a survey read as tables), title-page reading order, and justified RTL +double-spaces (`right_to_left_01`, within one line). **DOCX** (`*.docx`) is a core port of `MsWordDocumentBackend` (`roxmltree` over the `ooxml` helper): paragraphs, headings (by style, incl. Title), **numbered From 99cd43824599827632b1687966d9ac0256aae3d1 Mon Sep 17 00:00:00 2001 From: artiz Date: Mon, 29 Jun 2026 18:25:00 +0200 Subject: [PATCH 42/42] test(pdf): regenerate the tiff snapshot the timed-out regen missed MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The earlier bulk regen timed out before reaching tests/snapshots/tiff; its baseline was stale (pre escaping/dehyph/reading-order). Refresh it — the diff is the same intended improvements (title to reading-order top, wrap dehyphenation, `&`→`&`). Snapshot conformance is now 91/91 exact. Co-Authored-By: Claude Opus 4.8 (1M context) --- tests/snapshots/tiff/sources/2206.01062.tif.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/tests/snapshots/tiff/sources/2206.01062.tif.md b/tests/snapshots/tiff/sources/2206.01062.tif.md index f0639e7f..5eac0bf8 100644 --- a/tests/snapshots/tiff/sources/2206.01062.tif.md +++ b/tests/snapshots/tiff/sources/2206.01062.tif.md @@ -1,14 +1,16 @@ +## DocLayNet:ALarge Human-Annotated Datasetfor Document-LayoutAnalysis + Birgit Pfitzmann BMResearch RueschllkonSwitzerland bpf@zurich.ibm.com AhmedS.Nassar IBMResearch Rueschlikon, Switzerland ahn@zurich.ibm.com ## ABSTRACT -Accurate document layout analysis is a key requirement for high- quality PDF document conversion. With the recent availability od public, large ground-truth datasets such as PubLayNet and DocBank deep-learning models have proven to be very effective at layouf detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet, a new, publicly available, document-layout annotation dataset in COco format. It contains Soss3 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNef also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Fur- thermore, we provide evidence that DocLayNet is of sufficient size Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNet- trained models are more robust and thus the preferred choice for general-purpose document-layout analysis +Accurate document layout analysis is a key requirement for highquality PDF document conversion. With the recent availability od public, large ground-truth datasets such as PubLayNet and DocBank deep-learning models have proven to be very effective at layouf detection and segmentation. While these datasets are of adequate size to train such models, they severely lack in layout variability since they are sourced from scientific article repositories such as PubMed and arXiv only. Consequently, the accuracy of the layout segmentation drops significantly when these models are applied on more challenging and diverse layouts. In this paper, we present DocLayNet, a new, publicly available, document-layout annotation dataset in COco format. It contains Soss3 manually annotated pages from diverse data sources to represent a wide variability in layouts. For each PDF page, the layout annotations provide labelled bounding-boxes with a choice of 11 distinct classes. DocLayNef also provides a subset of double- and triple-annotated pages to determine the inter-annotator agreement. In multiple experiments, we provide baseline accuracy scores (in mAP) for a set of popular object detection models. We also demonstrate that these models fall approximately 10% behind the inter-annotator agreement. Furthermore, we provide evidence that DocLayNet is of sufficient size Lastly, we compare models trained on PubLayNet, DocBank and DocLayNet, showing that layout predictions of the DocLayNettrained models are more robust and thus the preferred choice for general-purpose document-layout analysis ## CCS CONCEPTS --Informationsystems→Documentstructure;Appliedcom- puting → Document analysis, - Computing methodologies → Machine learning Computer vision; Object detection +-Informationsystems→Documentstructure;Appliedcomputing → Document analysis, - Computing methodologies → Machine learning Computer vision; Object detection Peemissiom to make digital or hard copies of part er all of this woek foe personal or classuoom use Is granted without fee prowided that copies are not made or distrnutee tor protit oe commercial advantage and that oopies bear this notice and the tull citation on the tirst page. Copyrights foe thind-party oemponents of this work mist be honored Foraocherusescomtacttheowre/authors @@ -18,8 +20,6 @@ KDD 22 Aueaat 24, 2022, Washimgton, DC USA 7@01.8r/10.1145/35346783539043 -## DocLayNet:ALarge Human-Annotated Datasetfor Document-LayoutAnalysis - Michele Dolfi IBMResearch Rueschlikon, Switzerland dol@zurich.ibm.com Christoph Auer BMResearch Rueschlikon, Switzerland cau@zurich.ibmcom @@ -44,4 +44,4 @@ PDF document conversion, layout segmentation, object-detection, data set, Machin ## ACMRefereneeFormat: -Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for Documen Layout Analysis. In Proceedings of the 28th ACAd SIGKDD Conference on Ksowledge Discovery &td Date Adining (KDD 'z2), Augast J41&, 2022, wasi ingtow, DC, U/SA. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/ 353467.3539043 +Birgit Pfitzmann, Christoph Auer, Michele Dolfi, Ahmed S. Nassar, and Peter Staar. 2022. DocLayNet: A Large Human-Annotated Dataset for Documen Layout Analysis. In Proceedings of the 28th ACAd SIGKDD Conference on Ksowledge Discovery &td Date Adining (KDD 'z2), Augast J41&, 2022, wasi ingtow, DC, U/SA. ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/ 353467.3539043