# Nanonets OCR2 Cookbooks

A comprehensive collection of examples and configurations for getting the best results from the Nanonets-OCR2 model. This repository provides practical implementations for various OCR use cases including financial documents, complex tables, and multilingual content.

## 📚 Documentation

### Cookbooks & Examples

- **[Image to Markdown Converter](Nanonets-OCR2-Cookbook/image2md.ipynb)**: Comprehensive notebook showcasing the best practices for using Nanonets-OCR2-3B model across various document types including:
  - Bank statements and financial documents
  - Complex tables and structured data
  - Photos captured from mobile devices
  - Multilingual images and documents

## 🛠️ Installation

### Prerequisites

- Python 3.11
- CUDA-compatible GPU (recommended for optimal performance)
- `uv` package manager

### Setup Instructions

1. **Install uv package manager**:
   ```bash
   curl -LsSf https://astral.sh/uv/install.sh | sh
   ```

2. **Create virtual environment and install dependencies**:
   ```bash
   # Create virtual environment with Python 3.11
   uv venv --python=3.11
   
   # Activate the virtual environment
   source .venv/bin/activate
   
   # Install dependencies
   uv pip install -r requirements.txt
   ```

3. **Start Jupyter Lab**:
   ```bash
   jupyter lab
   ```

## 📦 Dependencies

This project uses the following key dependencies:

- **PyTorch**: Deep learning framework with CUDA support
- **Transformers**: Hugging Face transformers library for model inference
- **Jupyter**: Interactive notebook environment
- **PDF2Image**: PDF document processing
- **Accelerate**: Model acceleration and optimization

## 🎯 Quick Start

1. Clone this repository
2. Follow the installation steps above
3. Open the Jupyter notebook in `Nanonets-OCR2-Cookbook/image2md.ipynb`
4. Run the cells to see examples of OCR processing

## 🤝 Contributing

Contributions are welcome! Please feel free to submit pull requests or open issues for bugs and feature requests.