llama-ocr-py 0.1.0

A package for document extraction using Ollama Vision model

llama_ocr

A Python package for document text extraction using Ollama Vision models, with a focus on OCR (Optical Character Recognition) capabilities.

Features

• 🔍 Text extraction from images using Ollama Vision models
• 🖼️ Advanced image preprocessing for better OCR results
• 🛠️ Configurable settings and parameters
• 🔧 Extensible architecture with dependency injection
• 📝 Comprehensive logging and error handling

Installation

pip install llama_ocr

Or install from source:

git clone https://github.com/princexoleo/llama_ocr.git
cd llama_ocr
pip install -e .

Quick Start

from llama_ocr import DocumentExtractor

# Initialize extractor
extractor = DocumentExtractor()

# Extract text from an image
result = extractor.extract_from_image(
    "path/to/image.jpg",
    prompt="Extract all text from this document"
)

# Print extracted text
print(result["text"])

Advanced Usage

Custom Configuration

from llama_ocr import DocumentExtractor, OCRConfig

config = OCRConfig(
    model_name="llama3.2-vision",
    preprocess_images=True,
    optimize_for_ocr=True,
    log_level="INFO"
)

extractor = DocumentExtractor(config=config)

Image Processing Options

# Extract with OCR optimization
result = extractor.extract_from_image(
    "path/to/image.jpg",
    save_processed=True  # Save processed image for inspection
)

Architecture

The package follows SOLID principles and uses dependency injection for flexibility:

• DocumentExtractor: Main class orchestrating the extraction process
• VisionClient: Abstract base class for vision model interactions
• ImagePreprocessor: Abstract base class for image processing
• OCRConfig: Configuration management using dataclasses

Components

1. Core Module

   • extractor.py: Main document extraction logic
   • vision_client.py: Ollama Vision API integration
   • image_processor.py: Image preprocessing utilities
   • base.py: Abstract base classes and interfaces

2. Configuration

   • config.py: Configuration management using dataclasses

Dependencies

• ollama>=0.1.27
• Pillow>=10.1.0
• python-dotenv>=1.0.0
• opencv-python>=4.8.1.78
• numpy>=1.21.0

Development

Setting up Development Environment

# Create virtual environment
python -m venv venv
source venv/bin/activate  # Linux/Mac
# or
.\venv\Scripts\activate  # Windows

# Install development dependencies
pip install -e ".[dev]"

Running Tests

python -m unittest discover -s tests

Contributing

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

License

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

Acknowledgments

• Thanks to the Ollama team for providing the vision models
• Built with ❤️ by Mazharul Islam Leon