mirror-epstein-docs

Epstein Files Archive

An automatically processed, OCR'd, searchable archive of publicly released documents related to the Jeffrey Epstein case.

About

This project automatically processes thousands of scanned document pages using AI-powered OCR to:

• Extract and preserve all text (printed and handwritten)
• Identify and index entities (people, organizations, locations, dates)
• Reconstruct multi-page documents from individual scans
• Provide a searchable web interface to explore the archive

This is a public service project. All documents are from public releases. This archive makes them more accessible and searchable.

Features

• Full OCR: Extracts both printed and handwritten text from all documents
• Entity Extraction: Automatically identifies and indexes:
  • People mentioned
  • Organizations
  • Locations
  • Dates
  • Reference numbers
• Document Reconstruction: Groups scanned pages back into complete documents
• Searchable Interface: Browse by person, organization, location, date, or document type
• Static Site: Fast, lightweight, works anywhere

Project Structure

.
├── process_images.py       # Python script to OCR images using AI
├── requirements.txt         # Python dependencies
├── .env.example            # Example environment configuration
├── downloads/              # Place document images here
├── results/                # Extracted JSON data per document
├── src/                    # 11ty source files for website
├── .eleventy.js            # Static site generator configuration
└── _site/                  # Generated static website (after build)

Setup

1. Install Dependencies

Python (for OCR processing):

pip install -r requirements.txt

Node.js (for website generation):

npm install

2. Configure API

Copy .env.example to .env and configure your OpenAI-compatible API endpoint:

cp .env.example .env
# Edit .env with your API details

3. Process Documents

Place document images in the downloads/ directory, then run:

python process_images.py

# Options:
# --limit N          # Process only N images (for testing)
# --workers N        # Number of parallel workers (default: 5)
# --no-resume        # Process all files, ignore index

The script will:

• Process each image through the OCR API
• Extract text, entities, and metadata
• Save results to ./results/{folder}/{imagename}.json
• Track progress in processing_index.json (resume-friendly)

4. Generate Website

Build the static site from the processed data:

npm run build    # Build static site to _site/
npm start        # Development server with live reload

How It Works

1. Document Processing: Images are sent to an AI vision model that extracts:

   • All text in reading order
   • Document metadata (page numbers, document numbers, dates)
   • Named entities (people, orgs, locations)
   • Text type annotations (printed, handwritten, stamps)

2. Document Grouping: Individual page scans are automatically grouped by document number and sorted by page number to reconstruct complete documents

3. Static Site Generation: 11ty processes the JSON data to create:

   • Index pages for all entities
   • Individual document pages with full text
   • Search and browse interfaces

Performance

• Processes ~2,000 pages into ~400 multi-page documents
• Handles LLM inconsistencies in document number formatting
• Resume-friendly processing (skip already-processed files)
• Parallel processing with configurable workers

Contributing

This is an open archive project. Contributions welcome:

• Report issues with OCR accuracy
• Suggest UI improvements
• Add additional document sources
• Improve entity extraction

Support This Project

If you find this archive useful, consider supporting its maintenance and hosting:

Bitcoin: bc1qmahlh5eql05w30cgf5taj3n23twmp0f5xcvnnz

Deployment

The site is automatically deployed to GitHub Pages on every push to the main branch.

GitHub Pages Setup

1. Push this repository to GitHub: https://github.com/epstein-docs/epstein-docs
2. Go to Settings → Pages
3. Source: GitHub Actions
4. The workflow will automatically build and deploy the site

The site will be available at: https://epstein-docs.github.io/epstein-docs/

License

This project is licensed under the MIT License - see the LICENSE file for details.

The code in this repository is open source and free to use. The documents themselves are public records.

Repository: https://github.com/epstein-docs/epstein-docs

Disclaimer

This is an independent archival project. Documents are sourced from public releases. The maintainers make no representations about completeness or accuracy of the archive.