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| title: TextLens - AI-Powered OCR | |
| emoji: 🔍 | |
| colorFrom: blue | |
| colorTo: purple | |
| sdk: gradio | |
| sdk_version: 4.0.0 | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| # 🔍 TextLens - AI-Powered OCR | |
| A modern Vision-Language Model (VLM) based OCR application that extracts text from images using Microsoft Florence-2 model with intelligent fallback systems. | |
| ## ✨ Features | |
| - **🤖 Advanced VLM OCR**: Uses Microsoft Florence-2 for state-of-the-art text extraction | |
| - **🔄 Smart Fallback System**: Automatically falls back to EasyOCR if Florence-2 fails | |
| - **🧪 Demo Mode**: Test mode for demonstration when other methods are unavailable | |
| - **🎨 Modern UI**: Clean, responsive Gradio interface with excellent UX | |
| - **📱 Multiple Input Methods**: Upload, webcam, clipboard support | |
| - **⚡ Real-time Processing**: Automatic text extraction on image upload | |
| - **📋 Copy Functionality**: Easy text copying from results | |
| - **🚀 GPU Acceleration**: Supports CUDA, MPS, and CPU inference | |
| - **🛡️ Error Handling**: Robust error handling and user-friendly messages | |
| ## 🏗️ Architecture | |
| ``` | |
| textlens-ocr/ | |
| ├── app.py # Main Gradio application | |
| ├── requirements.txt # Python dependencies | |
| ├── README.md # Project documentation | |
| ├── models/ # OCR processing modules | |
| │ ├── __init__.py | |
| │ └── ocr_processor.py # Advanced OCR class with fallbacks | |
| ├── utils/ # Utility functions | |
| │ ├── __init__.py | |
| │ └── image_utils.py # Image preprocessing utilities | |
| └── ui/ # User interface components | |
| ├── __init__.py | |
| ├── interface.py # Gradio interface | |
| ├── handlers.py # Event handlers | |
| └── styles.py # CSS styling | |
| ``` | |
| ## 🚀 Quick Start | |
| ### Local Development | |
| 1. **Clone the repository** | |
| ```bash | |
| git clone https://github.com/KumarAmrit30/textlens-ocr.git | |
| cd textlens-ocr | |
| ``` | |
| 2. **Set up Python environment** | |
| ```bash | |
| python3 -m venv textlens_env | |
| source textlens_env/bin/activate # On Windows: textlens_env\Scripts\activate | |
| ``` | |
| 3. **Install dependencies** | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| 4. **Run the application** | |
| ```bash | |
| python app.py | |
| ``` | |
| 5. **Open your browser** | |
| Navigate to `http://localhost:7860` | |
| ### Quick Test | |
| Run the test suite to verify everything works: | |
| ```bash | |
| python test_ocr.py | |
| ``` | |
| ## 🔧 Technical Details | |
| ### OCR Processing Pipeline | |
| 1. **Primary**: Microsoft Florence-2 VLM | |
| - State-of-the-art vision-language model | |
| - Supports both basic OCR and region-based extraction | |
| - GPU accelerated inference | |
| 2. **Fallback**: EasyOCR | |
| - Traditional OCR with good accuracy | |
| - Works when Florence-2 fails to load | |
| - Multi-language support | |
| 3. **Demo Mode**: Test Mode | |
| - Demonstration functionality | |
| - Shows interface working correctly | |
| - Used when other methods are unavailable | |
| ### Model Loading Strategy | |
| The application uses an intelligent loading strategy: | |
| ```python | |
| try: | |
| # Try Florence-2 with specific revision | |
| model = AutoModelForCausalLM.from_pretrained( | |
| "microsoft/Florence-2-base", | |
| revision='refs/pr/6', | |
| trust_remote_code=True | |
| ) | |
| except: | |
| # Fall back to default Florence-2 | |
| model = AutoModelForCausalLM.from_pretrained( | |
| "microsoft/Florence-2-base", | |
| trust_remote_code=True | |
| ) | |
| ``` | |
| ### Device Detection | |
| Automatically detects and uses the best available device: | |
| - **CUDA**: NVIDIA GPUs with CUDA support | |
| - **MPS**: Apple Silicon Macs (M1/M2/M3) | |
| - **CPU**: Fallback for all systems | |
| ## 📊 Performance | |
| | Model | Size | Speed | Accuracy | Use Case | | |
| | ---------------- | ------ | ------ | --------- | --------------------- | | |
| | Florence-2-base | 230M | Fast | High | General OCR | | |
| | Florence-2-large | 770M | Medium | Very High | High accuracy needs | | |
| | EasyOCR | ~100MB | Medium | Good | Fallback/Multilingual | | |
| ## 🔍 Supported Image Formats | |
| - **JPEG** (.jpg, .jpeg) | |
| - **PNG** (.png) | |
| - **WebP** (.webp) | |
| - **BMP** (.bmp) | |
| - **TIFF** (.tiff, .tif) | |
| - **GIF** (.gif) | |
| ## 🎯 Use Cases | |
| - **📄 Document Digitization**: Convert physical documents to text | |
| - **🏪 Receipt Processing**: Extract data from receipts and invoices | |
| - **📱 Screenshot Text Extraction**: Get text from app screenshots | |
| - **🚗 License Plate Reading**: Extract text from vehicle plates | |
| - **📚 Book/Article Scanning**: Digitize printed materials | |
| - **🌐 Multilingual Text**: Process text in various languages | |
| ## 🛠️ Configuration | |
| ### Model Selection | |
| Change the model in `models/ocr_processor.py`: | |
| ```python | |
| # For faster inference | |
| ocr = OCRProcessor(model_name="microsoft/Florence-2-base") | |
| # For higher accuracy | |
| ocr = OCRProcessor(model_name="microsoft/Florence-2-large") | |
| ``` | |
| ### UI Customization | |
| Modify the Gradio interface in `app.py`: | |
| - Update colors and styling in the CSS section | |
| - Change layout in the `create_interface()` function | |
| - Add new features or components | |
| ## 🧪 Testing | |
| The project includes comprehensive tests: | |
| ```bash | |
| # Run all tests | |
| python test_ocr.py | |
| # Test specific functionality | |
| python -c "from models.ocr_processor import OCRProcessor; ocr = OCRProcessor(); print(ocr.get_model_info())" | |
| ``` | |
| ## 🚀 Deployment | |
| ### HuggingFace Spaces | |
| 1. Fork this repository | |
| 2. Create a new Space on HuggingFace | |
| 3. Connect your repository | |
| 4. The app will automatically deploy | |
| ### Docker Deployment | |
| ```dockerfile | |
| FROM python:3.9-slim | |
| WORKDIR /app | |
| COPY requirements.txt . | |
| RUN pip install -r requirements.txt | |
| COPY . . | |
| EXPOSE 7860 | |
| CMD ["python", "app.py"] | |
| ``` | |
| ### Local Server | |
| ```bash | |
| # Production server | |
| pip install gunicorn | |
| gunicorn -w 4 -b 0.0.0.0:7860 app:create_interface().app | |
| ``` | |
| ## 🔐 Environment Variables | |
| | Variable | Description | Default | | |
| | ---------------------- | --------------------- | ---------------------- | | |
| | `GRADIO_SERVER_PORT` | Server port | 7860 | | |
| | `TRANSFORMERS_CACHE` | Model cache directory | `~/.cache/huggingface` | | |
| | `CUDA_VISIBLE_DEVICES` | GPU device selection | All available | | |
| ## 🤝 Contributing | |
| 1. Fork the repository | |
| 2. Create a feature branch | |
| 3. Make your changes | |
| 4. Add tests for new functionality | |
| 5. Submit a pull request | |
| ## 📝 API Reference | |
| ### OCRProcessor Class | |
| ```python | |
| from models.ocr_processor import OCRProcessor | |
| # Initialize | |
| ocr = OCRProcessor(model_name="microsoft/Florence-2-base") | |
| # Extract text | |
| text = ocr.extract_text(image) | |
| # Extract with regions | |
| result = ocr.extract_text_with_regions(image) | |
| # Get model info | |
| info = ocr.get_model_info() | |
| ``` | |
| ## 🐛 Troubleshooting | |
| ### Common Issues | |
| 1. **Model Loading Errors** | |
| ```bash | |
| # Install missing dependencies | |
| pip install einops timm | |
| ``` | |
| 2. **CUDA Out of Memory** | |
| ```python | |
| # Use CPU instead | |
| ocr = OCRProcessor() | |
| ocr.device = "cpu" | |
| ``` | |
| 3. **SSL Certificate Errors** | |
| ```bash | |
| # Update certificates (macOS) | |
| /Applications/Python\ 3.x/Install\ Certificates.command | |
| ``` | |
| ## 📄 License | |
| This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. | |
| ## 🙏 Acknowledgments | |
| - **Microsoft** for the Florence-2 model | |
| - **HuggingFace** for the transformers library | |
| - **Gradio** for the web interface framework | |
| - **EasyOCR** for fallback OCR capabilities | |
| ## 📞 Support | |
| - Create an issue for bug reports | |
| - Start a discussion for feature requests | |
| - Check existing issues before posting | |
| --- | |
| **Made with ❤️ for the AI community** | |