Welcome to the Epstein Documents Browser

Project Overview

Welcome to the Epstein Documents Browser - an open-source document management system designed to make congressional records and documents easily accessible and searchable. This project serves as a reference implementation for document processing and OCR capabilities.

What We've Built

The main homepage showing the document browser interface

Core Features

• Document Indexing: Automated scanning and indexing of image files with metadata extraction
• OCR Processing: Advanced optical character recognition using Tesseract for text extraction
• Web Interface: Clean, responsive Bootstrap-based UI for document browsing
• Search Capabilities: Full-text search through extracted OCR content
• Navigation: Sequential document browsing with keyboard shortcuts

Technical Implementation

• Backend: Flask web framework with SQLite database
• OCR Engine: Tesseract for memory-efficient text extraction
• Database: SQLite with proper schema management and idempotent operations
• Process Management: Screen-based background processing for indexing and OCR
• Production Ready: Robust error handling and production environment protocols

The document viewer showing OCR text extraction and navigation controls

Documentation and Support

Comprehensive help page with usage instructions and keyboard shortcuts

The system includes extensive documentation and help features:
- Interactive help page with detailed usage instructions
- Keyboard shortcuts for power users
- FAQ section addressing common questions
- Technical documentation for developers
- User guides for different skill levels

Project Blog and Updates

The project blog showcasing updates, features, and technical insights

Stay informed about the latest developments through our project blog:
- Feature announcements and technical updates
- Behind-the-scenes development insights
- Performance improvements and optimizations
- Community contributions and feedback
- RSS feed for automatic updates

Key Technical Achievements

Idempotent Operations

Both the image indexer and OCR processor are designed to be idempotent, meaning they can be safely re-run without losing progress or corrupting data. This is crucial for production environments where files are continuously being uploaded.

Dynamic Navigation

Implemented smart navigation that automatically adapts to the actual document range in the database, ensuring users always start with the first available document.

Memory Optimization

Switched from memory-intensive EasyOCR to lightweight Tesseract to handle large document collections efficiently.

SEO & Social Integration

Added comprehensive SEO meta tags, Open Graph, Twitter Cards, and Schema.org structured data for better social media sharing and search engine indexing.

Project Structure

epstein-browser/
├── app.py                 # Main Flask application
├── index_images.py        # Idempotent image indexer
├── ocr_processor_lite.py  # Lightweight OCR processor
├── start_app.sh          # Web server management
├── start_ocr.sh          # OCR process management
├── templates/            # Jinja2 templates
├── static/              # CSS, JS, images
└── images.db            # SQLite database

Getting Started

This is a reference implementation of the public repository at github.com/actuallyrizzn/epstein-browser. The codebase demonstrates best practices for:

• Document processing pipelines
• OCR integration
• Web application development
• Production deployment
• Database management

What's Next

We're continuously improving the system with:
- Enhanced OCR accuracy
- Better search algorithms
- Performance optimizations
- Additional document formats

Stay tuned for updates as we continue to develop this open-source document management platform!

This project is developed by Mark Rizzn Hopkins as part of the open-source community effort to make government documents more accessible.