RapidOCR is a multi-platform, multi-lingual OCR toolkit designed for fast, offline deployment, built upon ONNXRuntime, OpenVINO, PaddlePaddle, and PyTorch. It targets developers and researchers needing efficient OCR capabilities across various programming languages, offering a simplified inference experience compared to its predecessor, PaddleOCR.

How It Works

This project converts PaddleOCR models to the ONNX format, enabling cross-platform compatibility and integration into Python, C++, Java, and C# environments. This approach prioritizes lightness, velocity, and intelligence, leveraging deep learning with compact models for swift, effective OCR without compromising performance.

Quick Start & Requirements

• Install via pip: pip install rapidocr onnxruntime
• Requires Python. GPU/CUDA support is implied for optimal performance but not explicitly mandated for basic installation.
• Documentation is available in Chinese: docs

Highlighted Details

• Supports multiple programming languages (Python, C++, Java, C#).
• Based on ONNXRuntime, OpenVINO, PaddlePaddle, and PyTorch.
• Facilitates swift offline deployments.
• Offers customization by refining PaddleOCR models with user data.

Maintenance & Community

The project acknowledges numerous contributors and sponsors, including Quicker and Compshare. Further details on contributors and sponsors can be found on the GitHub repository.

Licensing & Compatibility

Released under the Apache 2.0 license. The OCR model copyright is held by Baidu, while engineering scripts are owned by the repository owner. This license generally permits commercial use and linking with closed-source projects.

Limitations & Caveats

The project's full documentation is primarily in Chinese. While it supports Chinese and English inherently, self-service conversion is required for additional languages, with specific details available via a provided link.