New Text Document.txt

Product Name
**LibraDigit AI** – AI-Based Digitization & Digital Archive Builder

Product Type
Web + Desktop hybrid app (offline-friendly for libraries)

Target Users

* Librarians
* Archivists
* Digitization teams
* Universities & public libraries

Core Goal
Convert scanned documents into **searchable, clean, metadata-rich digital archives** using a guided workflow.

This app replaces:

* Manual OCR tools
* Separate PDF editors
* Excel metadata sheets
* Folder chaos

One pipeline. One system.

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PRD – Product Requirements Document

1. Problem Statement
   Libraries have scanned files but:

* They are not searchable
* No metadata
* No workflow
* No quality control
* No standard archive structure

Current digitization = storage
Needed digitization = searchable knowledge

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2. Key Features (MVP)

2.1 OCR Module
Function:

* Upload PDF or image
* Run OCR using:

  * Tesseract engine
  * OCRmyPDF wrapper

UI:

* Upload → Process → Download
* Before/After preview
* Search test button

Output:

* Searchable PDF

---

2.2 OCR Cleanup Module
Function:

* Show extracted text
* Highlight OCR errors
* Allow:

  * Text correction
  * Formatting cleanup
  * Remove garbage characters

UI:

* Side-by-side:

  * Original image
  * OCR text editor

---

2.3 Metadata Module
Function:

* Manual metadata entry:

  * Title
  * Author
  * Year
  * Subject
  * Keywords
* AI suggestions for keywords (optional)
* Save as:

  * JSON
  * CSV
  * MARC-ready format (future)

---

2.4 Digital Archive Builder
Function:

* Standard file naming
* Auto folder structure:

  ```
  /Archive/
     /Subject/
        /Year/
            Author_Title.pdf
  ```
* Attach metadata to file

---

2.5 Workflow Tracker
Function:
Show:

* Step 1: Upload
* Step 2: OCR
* Step 3: Cleanup
* Step 4: Metadata
* Step 5: Archive Ready

Progress indicator per file.

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3. Optional AI Enhancements (Phase 2)

* Auto keyword extraction (NLP)
* Document classification by subject
* Language detection
* Duplicate document detection
* Quality score for OCR accuracy

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4. User Flow

5. Upload scanned document

6. Click “Run OCR”

7. Validate searchability

8. Clean OCR text

9. Enter metadata

10. Save to archive

11. Export searchable PDF + metadata

Simple and linear.

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5. Non-Functional Requirements

* Works offline (important for libraries)
* No cloud dependency
* Data privacy
* Lightweight deployment
* Cross-platform support (Windows/Linux)

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6. Tech Stack (Suggested)

Backend:

* Python
* Tesseract OCR
* OCRmyPDF
* spaCy (future)

Frontend:

* Electron / Tauri + React
* Or Web UI + local backend

Storage:

* Local file system
* SQLite for metadata

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7. MVP Scope (Strict)

Must have:

* OCR
* Searchable PDF
* Cleanup editor
* Metadata form
* Archive export
* Workflow steps

Exclude:

* Chatbot
* Search engines
* Recommendation systems
* Plagiarism tools

Keep it focused.

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8. Monetization Model

* Free: OCR + basic PDF
* Pro:

  * Cleanup editor
  * Metadata
  * Archive builder
* Enterprise:

  * Batch processing
  * Institutional branding
  * Training mode

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9. Training Mode (Your Unique Advantage)

Add a “Training Mode”:

* Guided steps
* Progress tracking
* Certificate generation
* Project report export

Your app becomes:

> A learning platform + a digitization system

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10. Success Metrics

* Time to convert 10-page scan → searchable PDF < 3 minutes
* Metadata creation < 2 minutes
* Zero dependency on external SaaS
* Librarian can complete workflow without tech help

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This is a real product.
Not a demo app.
Not an AI toy.

It directly supports:

* Your training
* Your certification
* Your branding
* Your commercial offering