🧭 Quick Return to Map
You are in a sub-page of DocumentAI_OCR.
To reorient, go back here:
- DocumentAI_OCR — document parsing and optical character recognition
- WFGY Global Fix Map — main Emergency Room, 300+ structured fixes
- WFGY Problem Map 1.0 — 16 reproducible failure modes
Think of this page as a desk within a ward.
If you need the full triage and all prescriptions, return to the Emergency Room lobby.
Use this page when ABBYY OCR powers ingestion of scanned PDFs, complex layouts, forms, or multilingual documents.
ABBYY is enterprise-grade, but still prone to schema drift, field misalignment, and unstable table anchors.
- Visual map and recovery: RAG Architecture & Recovery
- Retrieval knobs: Retrieval Playbook
- Citation schema: Retrieval Traceability
- Schema stability: Data Contracts
- Embedding vs meaning: Embedding ≠ Semantic
- Hallucination and entropy drift: Hallucination, Entropy Collapse
- Chunk boundaries: Chunking Checklist
- ΔS(question, retrieved) ≤ 0.45
- Coverage ≥ 0.70 across fields and tokens
- λ convergent on three paraphrases and two seeds
- Form fields ≥ 95% aligned with schema contract
-
Form fields drift across runs (invoice totals, line items misaligned)
→ Data Contracts, Retrieval Traceability -
Table anchors collapse (multi-column invoices, receipts)
→ Chunking Checklist, clamp with BBMC -
Handwriting extraction unstable
→ Entropy Collapse -
Injected payload in OCR notes layer
→ Prompt Injection -
Multilingual contract fields mismatched
→ Embedding ≠ Semantic
- Enforce field schema: require
field_id,bbox,confidence,revision_id. - Compute ΔS on critical fields (e.g.
total_amount,invoice_date). - Apply λ probes with different template libraries.
- Clamp instability with BBAM and log coverage.
- Rebuild index if ΔS ≥ 0.60 persists.
I uploaded TXTOS and the WFGY Problem Map.
OCR provider: ABBYY (FineReader / FlexiCapture).
Symptoms: field drift, unstable tables, ΔS ≥ 0.60.
Steps:
1. Identify failing layer (contracts, chunking, retrieval).
2. Point to the WFGY fix page.
3. Return JSON:
{ "fields_checked": [...], "answer": "...", "ΔS": 0.xx, "λ_state": "<>", "next_fix": "..." }
Keep it reproducible and auditable.- Field coverage < 0.70 even after re-chunk → Data Contracts
- Persistent anchor drift → Chunking Checklist
- Handwriting ΔS unstable across seeds → Entropy Collapse
| Tool | Link | 3-Step Setup |
|---|---|---|
| WFGY 1.0 PDF | Engine Paper | 1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + <your question>” |
| TXT OS (plain-text OS) | TXTOS.txt | 1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” — OS boots instantly |
| Layer | Page | What it’s for |
|---|---|---|
| ⭐ Proof | WFGY Recognition Map | External citations, integrations, and ecosystem proof |
| ⚙️ Engine | WFGY 1.0 | Original PDF tension engine and early logic sketch (legacy reference) |
| ⚙️ Engine | WFGY 2.0 | Production tension kernel for RAG and agent systems |
| ⚙️ Engine | WFGY 3.0 | TXT based Singularity tension engine (131 S class set) |
| 🗺️ Map | Problem Map 1.0 | Flagship 16 problem RAG failure taxonomy and fix map |
| 🗺️ Map | Problem Map 2.0 | Global Debug Card for RAG and agent pipeline diagnosis |
| 🗺️ Map | Problem Map 3.0 | Global AI troubleshooting atlas and failure pattern map |
| 🧰 App | TXT OS | .txt semantic OS with fast bootstrap |
| 🧰 App | Blah Blah Blah | Abstract and paradox Q&A built on TXT OS |
| 🧰 App | Blur Blur Blur | Text to image generation with semantic control |
| 🏡 Onboarding | Starter Village | Guided entry point for new users |
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