RapidOCR is an offline deep-learning OCR engine that detects and recognizes text in images using ONNX Runtime, operating entirely without an internet connection. It provides a unified inference pipeline that runs across multiple platforms including Windows, Linux, macOS, Android, and Raspberry Pi, with programming language bindings for Python, C++, Java, and C#.
The engine separates text detection and recognition into independent modules that can be swapped or fine-tuned individually, and abstracts the inference backend behind a unified interface allowing seamless switching between ONNX Runtime, OpenVINO, PaddlePaddle, PyTorch, MNN, and TensorRT. It supports over 80 languages by combining language-specific recognition models with a unified text detection backbone, and offers both lightweight mobile-optimized and higher-accuracy server-grade model variants selected at runtime.
The project includes a command-line tool for extracting text from images and URLs with bounding boxes and confidence scores, and provides structured programmatic output with separate fields for bounding boxes, recognized text, and confidence scores. It can classify text line orientation before recognition to improve accuracy, and visualize results by drawing detected text regions onto the original image.
For deployment, the OCR engine can be packaged into a Docker container for consistent environments across platforms, or bundled into a standalone executable using PyInstaller that removes the Python runtime dependency. The project also includes utilities for converting PaddleOCR models to ONNX format and fine-tuning them on custom data for specialized text recognition scenarios.
