DocForge 🔨

Forge perfect documents from any format with precision, power, and simplicity.

DocForge is a comprehensive document processing toolkit built on proven implementations with a modern modular architecture. Born from real-world needs and battle-tested algorithms, DocForge transforms how you work with documents.

✨ Features

• 🔍 OCR Processing: Convert scanned PDFs to searchable documents with precision
• 🗜️ Smart Optimization: Reduce file sizes without compromising quality
• ⚙️ Batch Processing: Handle hundreds of documents efficiently
• 🔧 Document Analysis: Extract insights and metadata
• 🎯 Modular Design: Use only what you need, extend easily

🚀 Why DocForge?

• Battle-tested OCR algorithms with Windows compatibility
• Advanced optimization techniques from real-world usage
• Memory-efficient batch processing for large-scale operations
• Clean, modular codebase that's easy to understand and extend
• Comprehensive error handling and logging
• Both programmatic API and command-line interface

📦 Installation

Option 1: Install from PyPI (when available)

pip install docforge

Option 2: Install from source

git clone https://github.com/oscar2song/docforge.git
cd docforge
pip install -e .

System Dependencies

Ubuntu/Debian:

sudo apt-get install tesseract-ocr poppler-utils

macOS:

brew install tesseract poppler

Windows: Download Tesseract from: https://github.com/tesseract-ocr/tesseract

🎯 Quick Start

Command Line Interface

After installation, use the docforge command:

# Get help
docforge --help

# OCR a scanned PDF
docforge enhanced-ocr -i scanned_document.pdf -o searchable_document.pdf

# Batch OCR processing
docforge enhanced-batch-ocr -i scanned_folder/ -o searchable_folder/

# Standard OCR processing
docforge ocr -i document.pdf -o output.pdf --language eng

# Batch optimization
docforge batch-ocr -i input_folder/ -o output_folder/

# Test the interface
docforge test-rich

# Run performance benchmarks
docforge benchmark --test-files document.pdf

Programmatic API

from docforge import DocumentProcessor

# Initialize the processor
processor = DocumentProcessor(verbose=True)

# OCR a scanned PDF
result = processor.ocr_pdf(
    "scanned_document.pdf",
    "searchable_document.pdf", 
    language='eng'
)

# Optimize PDF size
result = processor.optimize_pdf(
    "large_document.pdf",
    "optimized_document.pdf",
    optimization_type="aggressive"
)

# Batch processing
result = processor.batch_ocr_pdfs(
    "scanned_folder/",
    "searchable_folder/"
)

🏗️ Architecture

DocForge is built with a clean, modular architecture:

docforge/
├── core/           # Core processing engine
├── pdf/            # PDF operations (proven implementations)  
├── cli/            # Command-line interface
├── utils/          # Shared utilities
└── main.py         # CLI entry point

📋 Available Commands

🧪 Examples

Run the examples to see DocForge in action:

# Basic usage examples (if you have example files)
python examples/basic_usage.py

# Test the CLI interface
docforge test-rich

# Test error handling
docforge test-errors

# Test validation system  
docforge test-validation

🤝 Contributing

We welcome contributions! The modular architecture makes it easy to add new features.

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

🗺️ Roadmap

• ✅ Core PDF processing with proven implementations
• ✅ OCR and optimization capabilities
• ✅ Command-line interface
• ✅ Comprehensive documentation
• 📄 Word document processing (Word ↔ PDF conversion)
• 🎨 Modern GUI interface
• 🚀 Performance optimizations
• 📊 Excel and PowerPoint support
• 🤖 AI-powered document analysis
• 🌐 Web interface

📄 License

This project is licensed under the MIT License.

🏆 Acknowledgments

Built with proven implementations and enhanced with modern architecture for the open source community.

⭐ If DocForge helped you, please give it a star! ⭐

Built by craftsmen, for craftsmen. 🔨