RapidOCR is an open-source OCR toolkit that delivers fast, accurate text recognition across many programming languages by running models through lightweight inference runtimes instead of heavy frameworks. It has earned more than 8,200 stars on GitHub and is actively maintained, with updates in October 2026. It is a go-to choice when OCR needs to run offline or on modest hardware.
RapidOCR provides awesome OCR toolkits based on ONNX Runtime, OpenVINO, MNN, PaddlePaddle, TensorRT, and PyTorch. It converts well-known OCR model pipelines (detection with DBNet, classification, and recognition with CRNN) into portable ONNX models, so the same pipeline runs efficiently on CPUs, edge devices, servers, and mobile hardware. Language packages exist for Python and several other ecosystems, and the models handle Chinese and English documents especially well, reflecting the project’s roots in the PaddleOCR and ChineseOCR communities. Because there is no dependency on a giant deep-learning stack at inference time, packages stay small and startup stays fast, which is why RapidOCR shows up inside many document-processing tools and RAG ingestion pipelines.
RapidOCR is free and open source under the Apache-2.0 license. There are no per-page fees, API keys, or usage caps: install the package for your language, download the models, and run OCR on your own machines at whatever volume you need.
RapidOCR is for developers who need embedded text recognition: document digitization, invoice and receipt processing, screenshot-to-text tools, and GenAI data ingestion. Teams that cannot send documents to cloud OCR APIs, or that run on edge hardware, will appreciate its speed-to-size ratio most.