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Open LPR v1.0.0 Release Notes

We're excited to announce the first major release of Open LPR - a powerful, open-source License Plate Recognition system powered by AI!

🎉 What is Open LPR?

Open LPR is a Django-based web application that uses the Qwen3-VL AI model to detect and recognize license plates in images with advanced OCR capabilities. The system provides both a user-friendly web interface and a comprehensive REST API for integration into other applications.

✨ Key Features

  • 🤖 AI-Powered Detection: Uses qwen3-vl-4b-instruct vision-language model for accurate license plate recognition
  • 🔍 Advanced OCR Integration: Extracts text from detected license plates with confidence scores
  • 🎯 Bounding Box Visualization: Draws colored boxes around detected plates and OCR text
  • 📤 Drag & Drop Upload: Modern, user-friendly file upload interface
  • 💾 Permanent Storage: All uploaded and processed images are saved permanently
  • 🔄 Side-by-Side Comparison: View original and processed images together
  • 🔎 Search & Filter: Browse and search through processing history
  • 📱 Responsive Design: Works on desktop, tablet, and mobile devices
  • 🐳 Docker Support: Easy deployment with Docker and Docker Compose
  • 🔌 REST API: Full API for programmatic access

🚀 Deployment Options

This release provides multiple deployment options:

  1. Standard Docker Deployment: Traditional deployment with cloud-based AI services
  2. LlamaCpp CPU Deployment: Local inference using CPU resources
  3. LlamaCpp GPU Deployment: Local inference with AMD GPU acceleration using Vulkan

📦 What's New in v1.0.0

Core Application

  • Complete Django web application with license plate recognition capabilities
  • RESTful API with comprehensive endpoints for image processing
  • User-friendly web interface with drag-and-drop upload functionality
  • Image processing pipeline with bounding box visualization
  • Search and filter functionality for processed images
  • Responsive design that works across all devices

Docker Deployment

  • Multi-stage optimized Dockerfile for production use
  • Automated CI/CD pipeline with GitHub Actions
  • Multi-architecture support (linux/amd64, linux/arm64)
  • Automatic publishing to GitHub Container Registry
  • Software Bill of Materials (SBOM) generation for security scanning

LlamaCpp Integration

  • Local inference support with LlamaCpp server
  • CPU and GPU deployment options
  • Comprehensive documentation for local deployment
  • Environment configuration templates
  • Model download automation

Documentation

  • Comprehensive README with visual showcase
  • Detailed API documentation
  • Docker deployment guide
  • LlamaCpp and ROCm resources collection
  • Troubleshooting guides and best practices

🔧 Technical Improvements

Performance

  • Optimized Docker image for production use
  • Gunicorn WSGI server with 3 workers
  • Efficient image processing pipeline
  • Configurable batch processing

Security

  • Non-root Docker user for security
  • Environment-based configuration
  • Secure file upload handling
  • Input validation and sanitization

Developer Experience

  • Well-structured codebase following Django best practices
  • Comprehensive documentation
  • Environment configuration templates
  • Management commands for project setup

📋 API Endpoints

Web Endpoints

  • GET / - Home page with upload form
  • POST /upload/ - Upload and process image
  • GET /result/<int:image_id>/ - View processing results
  • GET /images/ - Browse image history with search and filtering
  • GET /image/<int:image_id>/ - View detailed image information
  • POST /progress/ - Check processing status (AJAX endpoint)
  • GET /download/<int:image_id>/<str:image_type>/ - Download images
  • GET /health/ - API health check endpoint

REST API Endpoints

  • POST /api/v1/ocr/ - Upload an image and receive OCR results

🐳 Docker Images

The v1.0.0 release includes Docker images published to GitHub Container Registry:

  • ghcr.io/faisalthaheem/open-lpr:latest - Latest stable version
  • ghcr.io/faisalthaheem/open-lpr:v1.0.0 - Version 1.0.0
  • ghcr.io/faisalthaheem/open-lpr:v1.0 - Version 1.0.x series
  • ghcr.io/faisalthaheem/open-lpr:v1 - Version 1.x series

Images are available for both linux/amd64 and linux/arm64 architectures.

📚 Documentation

🔄 Migration Notes

This is the initial release, so there are no migration requirements. Future releases will include migration guides as needed.

🐛 Known Issues

  • Large images (>10MB) may require increased memory allocation
  • GPU acceleration is currently limited to AMD GPUs with Vulkan support
  • Concurrent processing is limited by Django's development server (use Gunicorn in production)

🛣️ Roadmap

Future releases will include:

  • Support for additional GPU types (NVIDIA CUDA)
  • Batch processing capabilities
  • Real-time video stream processing
  • Additional language support for license plates
  • Performance optimizations
  • Mobile application

🤝 Contributing

We welcome contributions! Please see our contributing guidelines for details.

📄 License

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

🙏 Acknowledgments

  • Qwen3-VL for the powerful vision-language model
  • Django for the robust web framework
  • Bootstrap for the responsive UI components
  • LlamaCpp for local inference capabilities
  • All contributors who help improve this project

📥 Installation

Quick Start with Docker

# Clone the repository
git clone https://github.com/faisalthaheem/open-lpr.git
cd open-lpr

# Copy environment file
cp .env.example .env

# Edit the environment file with your configuration
nano .env

# Run with Docker Compose
docker-compose up -d

Quick Start with LlamaCpp (CPU)

# Clone the repository
git clone https://github.com/faisalthaheem/open-lpr.git
cd open-lpr

# Copy LlamaCpp environment file
cp .env.llamacpp.example .env.llamacpp

# Edit the environment file with your HuggingFace token
nano .env.llamacpp

# Run with Docker Compose
docker-compose -f docker-compose-llamacpp-cpu.yml up -d

For more detailed installation instructions, see the README.

Thank you for using Open LPR! 🎉

If you encounter any issues or have questions, please file an issue on GitHub.