🔒 DocShield

A sophisticated Streamlit application that automatically detects and redacts sensitive information from PDF documents using OCR and pattern recognition.

Made by Enamul Hasan Shagato ❤️

🌟 Features

• OCR Text Extraction: Advanced text extraction from PDF documents
• Intelligent Redaction: Automatically detects and redacts:
  • Social Security Numbers (SSN)
  • Credit Card Numbers
  • Street Addresses
  • ZIP Codes
• Dual Processing Modes:
  • Conservative (labeled data only)
  • Aggressive (pattern matching)
• Multiple Export Formats: PDF and Word document outputs
• Modern UI: Responsive design with dark mode support
• Real-time Statistics: Processing metrics and redaction breakdown
• Privacy-First: All processing done locally

📁 Project Structure

document_redaction/
├── main.py                 # Main Streamlit application
├── requirements.txt        # Python dependencies
├── README.md              # This file
├── config/
│   ├── __init__.py
│   └── settings.py        # Configuration and patterns
├── core/
│   ├── __init__.py
│   ├── ocr_processor.py   # OCR functionality
│   ├── redactor.py        # Redaction logic
│   └── file_handler.py    # File operations
├── ui/
│   ├── __init__.py
│   ├── components.py      # UI components
│   └── styles.py          # CSS styling
└── utils/
    ├── __init__.py
    └── helpers.py         # Utility functions

🚀 Installation

1. Clone the repository:

   git clone https://github.com/shagatomte19/docshield.git
   cd docshield

2. Install dependencies:

   pip install -r requirements.txt

3. Run the application:

   streamlit run main.py

📖 Usage

1. Upload a PDF: Use the file uploader to select your PDF document
2. Choose Redaction Mode:
   • Conservative: Only redacts explicitly labeled data
   • Aggressive: Uses pattern matching for broader detection
3. Process Document: Click the "Process Document" button
4. Review Results: View redacted text and statistics
5. Download Files: Export as PDF or Word document

🔧 Configuration

Modify config/settings.py to customize:

• Regex patterns for detection
• Redaction labels
• File size limits
• OCR settings

🎨 Customization

Adding New Patterns

Add new regex patterns in config/settings.py:

NEW_PATTERNS = [
    r"new-regex-pattern-here"
]

UI Modifications

Update styles in ui/styles.py or add new components in ui/components.py.

Processing Logic

Extend redaction logic in core/redactor.py for new sensitive data types.

📊 Architecture

Core Components

• OCRProcessor: Handles PDF to text conversion using EasyOCR
• TextRedactor: Implements redaction logic with multiple strategies
• FileHandler: Manages PDF and Word export functionality
• UIComponents: Modular UI components for Streamlit interface

Processing Pipeline

1. PDF Upload → OCR Processing → Text Extraction
2. Pattern Detection → Redaction → Statistics Generation
3. File Export → Download → User Interface Updates

🛡️ Security & Privacy

• Local Processing: All data processing happens locally
• No Data Storage: Documents are not stored permanently
• Temporary Files: Output files are created in temporary directories
• Privacy-First: No external API calls for sensitive data processing

📝 Dependencies

• Streamlit: Web application framework
• EasyOCR: OCR text extraction
• PyMuPDF: PDF processing
• FPDF2: PDF generation
• python-docx: Word document creation
• PyTorch: ML backend for OCR

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Make your changes
4. Commit your changes (git commit -m 'Add some AmazingFeature')
5. Push to the branch (git push origin feature/AmazingFeature)
6. Open a Pull Request

📋 Roadmap

• Add support for more file formats (DOCX, images)
• Implement custom redaction patterns via UI
• Add batch processing capabilities
• Enhance OCR accuracy with preprocessing
• Add redaction confidence scores
• Implement audit logging

🐛 Known Issues

• OCR quality depends on PDF text clarity
• Complex layouts may affect accuracy
• Large files may require processing time
• Handwritten text detection is limited

📄 License

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

👨‍💻 Author

Enamul Hasan Shagato

• AI/ML Engineer
• GitHub | LinkedIn

⭐ Show your support

Give a ⭐️ if this project helped you!

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Made with ❤️ using Python and Streamlit