LibraDigit AI

AI-Based Digitization & Digital Archive Builder for Libraries

A production-grade desktop application that converts scanned documents into searchable, metadata-rich digital archives using a guided workflow.

🎯 Overview

LibraDigit AI is an offline-first desktop application designed for librarians, archivists, and digitization teams to:

• ✅ Convert scanned PDFs/images to searchable documents using OCR.
• ✅ Automatic Scanned PDF Detection - Intelligently detects image-based PDFs and applies OCR automatically.
• ✅ Advanced OCR with AI-powered layout analysis - Detect tables, forms, signatures, and page structure.
• ✅ Handwritten text to PDF conversion - Transform handwritten notes into formatted, searchable PDFs.
• ✅ Clean and improve OCR text accuracy.
• ✅ Add comprehensive metadata (title, author, year, subject, keywords).
• ✅ Generate structured digital archives with organized folder hierarchies.
• ✅ Search across an entire archive using a dedicated Full-Text Search engine.
• ✅ Analyze digitization progress with a built-in statistics dashboard.

🚀 Key Features

🤖 Advanced OCR & AI Analysis

• Scanned PDF OCR with Handwritten Support: Automatically detects PDFs with embedded images and applies intelligent OCR. Switches to handwritten mode (LSTM) when handwriting is detected on any page.
• Intelligent Layout Understanding: Automatically detects page structure including headers, footers, stamps, and signatures.
• Table & Form Extraction: Identifies and extracts structured data from tables and form fields with checkbox detection.
• Auto-Orientation Correction: Automatically detects and corrects page rotation (0°, 90°, 180°, 270°).
• Handwritten Text Recognition: Specialized LSTM neural network for improved handwriting accuracy (75-92%).
• Enhanced Preprocessing: CLAHE enhancement, adaptive thresholding, and advanced denoising for better accuracy.
• Handwritten to PDF: Convert handwritten notes directly to professionally formatted, searchable PDF documents.

🔍 Extensive Search Facility

• Full-Text Search (FTS5): Powered by SQLite's FTS5, search instantly through thousands of archived documents.
• Content-Aware Snippets: Search results show exactly where terms appear with keyword highlighting.
• Universal Metadata Search: Find documents by Title, Author, Keywords, or any content within the text.

📊 Analytics & Statistics

• Workflow Visualization: Track project distribution across Upload, OCR, Cleanup, Metadata, and Archived stages.
• Storage Metrics: Real-time tracking of disk space usage by your digital collection.
• Activity Trends: Weekly activity charts showing your digitization team's productivity.
• Top Subjects: Bar charts showcasing the most represented subjects in your archive.

🔒 Secure & Private

• Secure Offline Auth: Implements bcryptjs hashing for local authentication.
• First-Run Setup: Guided password setup on the first launch.
• Privacy-First: Zero cloud dependency; all data, hashes, and files stay exclusively on your local machine.

📱 Responsive & Modern UI

• Responsive Design: Optimized for everything from desktop monitors to mobile devices.
• Multi-tab Synchronization: Log out or delete a project in one browser tab, and all other tabs will instantly synchronize.
• Premium Aesthetics: High-end dark theme with smooth gradients and micro-animations.

📦 Archival Standards

• BagIt Packaging: Implements the international BagIt standard for robust, verifiable data packages.
• XMP Metadata Embedding: Metadata (Title, Author, etc.) is embedded directly into the PDF binary, traveling with the file even when shared.
• MD5 Manifests: Automatic integrity checks to ensure files remain uncorrupted over decades.

📋 Prerequisites

Required Software

1. Node.js (v18 or higher)

   • Download: https://nodejs.org/

2. Python (v3.8 or higher)

   • Download: https://www.python.org/downloads/

3. Tesseract OCR (for OCR functionality)

   • Windows: Download installer from https://github.com/UB-Mannheim/tesseract/wiki
   • macOS: brew install tesseract
   • Linux: sudo apt-get install tesseract-ocr

Additional Dependencies for Advanced Features

4. OpenCV (for advanced image processing)

   • Installed automatically via requirements.txt
   • Required for: Advanced OCR, handwritten text recognition, table detection

5. ReportLab (for PDF generation)

   • Installed automatically via requirements.txt
   • Required for: Handwritten to PDF conversion

🛠️ Installation

1. Clone or Download the Project

cd "LibraDigit AI"

2. Install Dependencies

# Install frontend packages
npm install

# Install backend packages (includes OpenCV, NumPy, ReportLab)
cd backend
pip install -r requirements.txt
cd ..

🎮 Running the Application

Development Mode

The easiest way to run the application is using the combined dev script:

npm run dev

This will:

• Start the React frontend (Vite)
• Start the Python backend (Flask)
• Launch the Electron desktop window

📖 Usage Guide

Creating Your First Project

1. Launch & Setup: On first run, create your master password.
2. Upload Document: Drag and drop a PDF or image file (PDF, PNG, JPEG, TIFF). Scanned PDFs are automatically detected.
3. Choose OCR Method:
   • Standard OCR: Fast text extraction for printed documents and scanned PDFs
   • Advanced OCR: AI-powered analysis with table detection, form recognition, and layout understanding (images only)
   • Handwritten to PDF: Convert handwritten notes to formatted, searchable PDFs (images only)
4. Run OCR: Tesseract converts image text into a searchable layer. For scanned PDFs, pages are automatically rendered as images at 300 DPI.
5. Clean Text: Use the side-by-side rich text editor to correct OCR typos.
6. Add Metadata: Add descriptive details (Subject, Year, Author).
7. Generate Archive: The system builds the BagIt package and embeds your metadata.

🤖 Using Advanced OCR

For documents with complex layouts:

1. Upload your document (image format recommended)
2. Toggle "Advanced OCR Analysis" switch
3. Click "Run Advanced OCR"
4. View comprehensive results including:
   • Detected tables and their contents
   • Form fields and checkboxes (with fill status)
   • Page orientation corrections
   • Headers, footers, stamps, and signatures
   • Enhanced text extraction with layout preservation

✍️ Converting Handwritten Notes to PDF

For handwritten documents:

1. Upload a clear image of handwritten notes (300+ DPI recommended)
2. Select the appropriate language
3. Click "Convert Handwritten to PDF"
4. Receive a professionally formatted PDF with:
   • Extracted and structured text
   • Detected headings and paragraphs
   • Bullet points and lists
   • Diagrams and technical content
   • Complete metadata

📚 Installation Guide

For a detailed step-by-step visual guide on installing the Electron desktop application, please refer to: public/install_guide.html (included in the distribution package).

This guide covers:

• System Requirements (Tesseract OCR)
• SmartScreen Security Bypass (for internal tools)
• First-time Account Setup

Searching the Archive

Click "Archive Search" in the sidebar to perform lightning-fast keyword searches across your entire processed collection.

Archive Structure (BagIt Standard)

Documents are organized using a standard preservation hierarchy:

Archive/
  └── Subject/
      └── Year/
          └── Author_Year_Title/
              ├── data/
              │   └── Author_Year_Title.pdf   (Final PDF with embedded metadata)
              ├── bag-info.txt                (Archive package metadata)
              └── manifest-md5.txt            (Checksums for file integrity)

🔧 Technology Stack

Frontend & UI

• React 18 (Vite)
• Lucide React (Icons)
• Recharts (Analytics)
• Bcryptjs (Local Auth)
• Axios (API)

Desktop

• Electron (Cross-platform desktop engine)

Backend & Engine

• Flask (Python API)
• SQLite 3 (Database & FTS5 Search Engine)
• Tesseract OCR (Text Extraction with LSTM neural networks)
• PyMuPDF (fitz) (PDF rendering for scanned PDF OCR at 300 DPI)
• OpenCV (Advanced image processing & computer vision)
• NumPy (Numerical operations for image analysis)
• PyPDF2 & ReportLab (PDF Metadata, Generation & Manipulation)
• Bagit-Python (Packaging standard)

🎨 Project Structure

LibraDigit AI/
├── backend/                    # Flask server & OCR engines
│   ├── advanced_ocr_processor.py    # Advanced OCR with layout analysis
│   ├── handwritten_to_pdf.py        # Handwritten text converter
│   ├── metadata_extractor.py        # Metadata extraction
│   ├── batch_processor.py           # Batch operations
│   └── server.py                    # Main Flask API
├── src/
│   ├── components/             # UI elements (Charts, Loaders, Sidebar)
│   │   └── AdvancedOCRResults.jsx   # Advanced OCR results display
│   ├── pages/                  # Full views (Analytics, Search, Dashboard)
│   ├── context/                # Multi-tab sync & Global state
│   └── index.css               # Design system & Desktop/Mobile styles
├── Archive/                    # Final BagIt collections
├── Documentation/              # Feature documentation
│   ├── ADVANCED_OCR_DOCUMENTATION.md
│   ├── HANDWRITTEN_TO_PDF_DOCUMENTATION.md
│   └── QUICK_START_ADVANCED_OCR.md
├── package.json                # Frontend scripts
└── README.md                   # This guide

📚 Additional Documentation

• Advanced OCR Documentation - Complete guide to advanced OCR features
• Handwritten to PDF Guide - Handwritten text conversion documentation
• Quick Start Guide - Get started with advanced features quickly
• JSON Serialization Fix - Technical troubleshooting guide

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