vllmocr 0.3.2

OCR project using LLMs

vllmocr

vllmocr is a command-line tool that performs Optical Character Recognition (OCR) on images and PDFs using Large Language Models (LLMs). It supports multiple LLM providers, including OpenAI, Anthropic, Google, and local models via Ollama.

Features

• Image and PDF OCR: Extracts text from both images (PNG, JPG, JPEG) and PDF files.
• Multiple LLM Providers: Supports a variety of LLMs:
  • OpenAI: GPT-4o
  • Anthropic: Claude 3 Haiku, Claude 3 Sonnet
  • Google: Gemini 1.5 Pro
  • Ollama: (Local models) Llama3, MiniCPM, and other models supported by Ollama.
• Configurable: Settings, including the LLM provider and model, can be adjusted via a configuration file or environment variables.
• Image Preprocessing: Includes optional image rotation for improved OCR accuracy.

Installation

It is recommended to install vllmocr using uv:

uv pip install vllmocr

If you don't have uv installed, you can install it with:

pipx install uv

You may need to restart your shell session for uv to be available.

Alternatively, you can use pip:

pip install vllmocr

Usage

The vllmocr command-line tool has two main subcommands: image and pdf.

1. Process a Single Image:

vllmocr image <image_path> [options]

• <image_path>: The path to the image file (PNG, JPG, JPEG).

Options:

• --provider: The LLM provider to use (openai, anthropic, google, ollama). Defaults to openai.
• --model: The specific model to use (e.g., gpt-4o, haiku, gemini-1.5-pro-002, llama3). Defaults to the provider's default model.
• --config: Path to a TOML configuration file.
• --help: Show the help message and exit.

Example:

vllmocr image my_image.jpg --provider anthropic --model haiku

2. Process a PDF:

vllmocr pdf <pdf_path> [options]

• <pdf_path>: The path to the PDF file.

Options: (Same as image subcommand)

Example:

vllmocr pdf my_document.pdf --provider openai --model gpt-4o

Configuration

vllmocr can be configured using a TOML file or environment variables. The configuration file is searched for in the following locations (in order of precedence):

1. A path specified with the --config command-line option.
2. ./config.toml (current working directory)
3. ~/.config/vllmocr/config.toml (user's home directory)
4. /etc/vllmocr/config.toml (system-wide)

config.toml (Example):

[llm]
provider = "anthropic"  # Default provider
model = "haiku"        # Default model for the provider

[image_processing]
rotation = 0           # Image rotation in degrees (optional)

[api_keys]
openai = "YOUR_OPENAI_API_KEY"
anthropic = "YOUR_ANTHROPIC_API_KEY"
google = "YOUR_GOOGLE_API_KEY"
# Ollama doesn't require an API key

Environment Variables:

You can also set API keys using environment variables:

• VLLM_OCR_OPENAI_API_KEY
• VLLM_OCR_ANTHROPIC_API_KEY
• VLLM_OCR_GOOGLE_API_KEY

Environment variables override settings in the configuration file. This is the recommended way to set API keys for security reasons.

Development

To set up a development environment:

1. Clone the repository:

   git clone https://github.com/<your-username>/vllmocr.git
   cd vllmocr
   

2. Create and activate a virtual environment (using uv):

   uv venv
   uv pip install -e .[dev]
   

   This installs the package in editable mode (-e) along with development dependencies (like pytest and pytest-mock).

3. Run tests:

   uv pip install pytest pytest-mock  # if not already installed as dev dependencies
   pytest
   

License

This project is licensed under the MIT License (see pyproject.toml for details).