algoryn/dots-ocr-idcard

KYB Dots.OCR Text Extraction

This Hugging Face Space provides a FastAPI endpoint for text extraction from identity documents using Dots.OCR with ROI (Region of Interest) support. Built as a Docker Space for maximum flexibility and performance.

🚀 Quick Start

Using the API

1. 1.Upload an image (JPEG, PNG, or other supported formats)
2. 2.Optionally specify ROI coordinates for targeted extraction
3. 3.Get structured results with confidence scores and field mapping

Test the API

# Basic OCR test
curl -X POST https://algoryn-dots-ocr-idcard.hf.space/v1/id/ocr \
  -F "file=@test_image.jpg"

# With ROI (region of interest)
curl -X POST https://algoryn-dots-ocr-idcard.hf.space/v1/id/ocr \
  -F "file=@test_image.jpg" \
  -F 'roi={"x1":0.1,"y1":0.1,"x2":0.9,"y2":0.9}'

✨ Features

• —🔍 Text Extraction: Extract text from identity documents using Dots.OCR
• —📐 ROI Support: Process pre-cropped images or full images with ROI coordinates
• —📋 Field Mapping: Structured field extraction with confidence scores
• —🆔 MRZ Detection: Machine Readable Zone data extraction
• —🔌 Standardized API: Consistent response format for integration
• —🐳 Docker-based: Full control over dependencies and environment
• —⚡ GPU Support: Optimized for Hugging Face Spaces GPU instances

📡 API Endpoints

Health Check

GET /health

Returns service status and version information.

Text Extraction

POST /v1/id/ocr
Content-Type: multipart/form-data

file: <image_file>
roi: {"x1": 0.0, "y1": 0.0, "x2": 1.0, "y2": 1.0} (optional)

Parameters:

• —file: Image file to process (required)
• —roi: JSON string with normalized coordinates (optional)
• —x1, y1: Top-left corner (0.0 to 1.0)
• —x2, y2: Bottom-right corner (0.0 to 1.0)

📄 Response Format

{
  "request_id": "uuid",
  "media_type": "image",
  "processing_time": 0.456,
  "detections": [
    {
      "mrz_data": {
        "document_type": "TD3",
        "issuing_country": "NLD",
        "surname": "MULDER",
        "given_names": "THOMAS",
        "document_number": "NLD123456789",
        "nationality": "NLD",
        "date_of_birth": "1990-01-01",
        "gender": "M",
        "date_of_expiry": "2030-01-01",
        "personal_number": "123456789",
        "raw_mrz": "P<NLDMULDER<<THOMAS<<<<<<<<<<<<<<<<<<<<<<<<<",
        "confidence": 0.95
      },
      "extracted_fields": {
        "document_number": {
          "field_name": "document_number",
          "value": "NLD123456789",
          "confidence": 0.92,
          "source": "ocr"
        },
        "surname": {
          "field_name": "surname",
          "value": "MULDER",
          "confidence": 0.96,
          "source": "ocr"
        }
      }
    }
  ]
}

🛠️ Deployment to Hugging Face Spaces

Prerequisites

• —Hugging Face CLI installed
• —Docker installed locally (for testing)

1. Create HF Space

# Login to Hugging Face
huggingface-cli login

# Create a new Docker Space
huggingface-cli repo create dots-ocr-idcard --type space --space_sdk docker --organization algoryn

2. Clone and Setup

# Clone the space locally
git clone https://huggingface.co/spaces/algoryn/dots-ocr-idcard
cd dots-ocr-idcard

# Copy required files
cp /path/to/kybtech-ml-pipelines/docker/hf/dots-ocr/* .

# Copy field extraction module
mkdir -p src/idcard_api
cp /path/to/kybtech-ml-pipelines/src/idcard_api/field_extraction.py src/idcard_api/
touch src/idcard_api/__init__.py

3. Deploy

git add .
git commit -m "Deploy Dots-OCR text extraction service"
git push

4. Test Deployment

The Space will be available at https://algoryn-dots-ocr-idcard.hf.space after deployment (usually 5-10 minutes).

⚙️ Configuration

Environment Variables

• —HF_DOTS_MODEL_PATH: Path to Dots.OCR model weights
• —HF_DOTS_CONFIDENCE_THRESHOLD: Confidence threshold for field extraction
• —HF_DOTS_DEVICE: Device to use (auto, cpu, cuda)
• —HF_DOTS_MAX_IMAGE_SIZE: Maximum image size for processing
• —HF_DOTS_MRZ_ENABLED: Enable MRZ detection

Hugging Face Spaces Settings

• —SDK: Docker
• —Port: 7860 (default)
• —Hardware: CPU (upgradeable to GPU)
• —Storage: Persistent storage available for model caching

📊 Performance

🔒 Privacy & Security

• —No Data Storage: Images are processed temporarily and not stored
• —Privacy Protection: All field values are redacted in logs
• —Secure Processing: Runs in isolated Docker containers
• —No Tracking: No user data or usage analytics collected

🐳 Local Development

Quick Start with uv

# Set up development environment
make setup

# Activate virtual environment
source .venv/bin/activate  # On Unix/macOS
# or
.venv\Scripts\activate     # On Windows

# Run the application
make run-dev

Docker Development

# Build and run with Docker
make build
make run-docker

# View logs
make logs

Development Commands

# Run tests
make test

# Format code
make format

# Run linting
make lint

# Test API endpoints
make test-local
make test-production

For detailed development instructions, see the documentation in docs/.

📚 Documentation

• —Hugging Face Spaces Documentation
• —Docker Spaces Guide
• —FastAPI Documentation

🤝 Contributing

1. 1.Fork the repository
2. 2.Create a feature branch
3. 3.Make your changes
4. 4.Test thoroughly
5. 5.Submit a pull request

📄 License

This project is licensed under a private license. See the license file for details.

🆘 Support

• —Issues: Report bugs and request features via GitHub Issues
• —Discussions: Join the community discussions
• —Email: Contact us at website@huggingface.co for advanced support

Built with ❤️ using Hugging Face Spaces and FastAPI