RapidOCR
PaddleOCR's models converted to ONNX and shipped without the PaddlePaddle framework. Apache 2.0, a 27.3 MB wheel, PP-OCRv6 small by default and fully offline CPU deployment.
Overview
RapidOCR exists because of one specific piece of friction. Baidu's models are excellent and Baidu's framework is a nuisance, so the RapidAI maintainers convert the trained PaddleOCR weights into ONNX and ship them behind a small Python API with no PaddlePaddle required. Version 3.9.2, released on 21 July 2026, has PP-OCRv6 det small and rec small as its defaults and the entire wheel comes to 27.3 MB. Python 3.8 through 3.13 are supported and installation is one pip command.
Backend flexibility is the other reason to pick it. The same API runs on ONNX Runtime, OpenVINO, PaddlePaddle and PyTorch, with MNN and TensorRT paths available, so a fleet with Intel edge hardware at one site and NVIDIA servers at another shares a single integration rather than two. Ports exist for C++, Java and C#, which is genuinely useful when the surrounding system is a Java service and standing up a Python sidecar just for OCR is not worth the operational cost. CJK and Latin accuracy is inherited from PP-OCRv6, so mixed Chinese and English documents behave the same way they would under PaddleOCR proper.
Two limits deserve stating plainly. First, RapidOCR trains nothing of its own, and the README is explicit that copyright in the OCR models remains Baidu's while Apache 2.0 covers the engineering scripts; that is a distinction worth surfacing in a licence audit even though it constrains almost nobody in practice. Second, it does detection and recognition only, so layout, tables, formulas and reading order need MinerU or PP-StructureV3 layered above it. Documentation runs Chinese-first and the English site trails, which is the usual tax on this corner of the ecosystem. Against that, the maintainers keep pace with upstream well: PP-OCRv6 shipped in PaddleOCR on 11 June 2026 and was RapidOCR's default about six weeks later, with the wheel growing from 15 MB to 29 MB to carry the larger models.
Key Features
- ✓ Baidu's PaddleOCR weights converted to ONNX, so no PaddlePaddle framework install is required at any point
- ✓ Version 3.9.2, released 21 July 2026, defaults to PP-OCRv6 det small and rec small; the wheel is 27.3 MB
- ✓ Backend choice across ONNX Runtime, OpenVINO, PaddlePaddle and PyTorch, with MNN and TensorRT paths available behind one API
- ✓ Python 3.8 through 3.13 supported, installed with a single pip install rapidocr onnxruntime
- ✓ Ports maintained for C++, Java and C# alongside the Python reference implementation
- ✓ Runs fully offline on CPU with no GPU requirement and no outbound network call at inference time
Where it holds
- • Removes the PaddlePaddle dependency, which is the single most common operational complaint about running PaddleOCR in production
- • Under 30 MB installed, so it drops into a slim container or an edge device without a fight over image size
- • One API across ONNX Runtime, OpenVINO and PyTorch is useful when hardware differs between sites, for example Intel edge boxes in one factory and NVIDIA servers in the data centre
- • Tracks upstream closely: PP-OCRv6 reached PaddleOCR on 11 June 2026 and became RapidOCR's default about six weeks later
Where it breaks
- • No original models. The accuracy ceiling is whatever Baidu publishes, and the README is explicit that model copyright stays with Baidu while Apache 2.0 covers only the engineering scripts
- • Documentation is Chinese-first. The English pages on rapidai.github.io exist but trail, and most substantive issue and discussion threads are in Chinese
- • ONNX Runtime detection has been reported slower than native Paddle inference in some configurations, so benchmark on your own images rather than assuming the ONNX path is automatically faster
- • Text detection and recognition only. Layout, tables, formulas and reading order are out of scope, so a document pipeline still needs MinerU or PP-StructureV3 above it
My Take
It trains nothing. RapidOCR takes Baidu's PaddleOCR weights, converts them to ONNX and ships them without the PaddlePaddle framework, which is exactly why people reach for it. Version 3.9.2 landed on 21 July 2026 with PP-OCRv6 det small and rec small as defaults, and the whole wheel is 27.3 MB. The value is the deployment footprint: an ONNX Runtime process in a CPU-only container, no CUDA version pinning and no heavyweight framework install to justify in a security review.
Quick Info
- Pricing:
- open-source
- Openness:
- Open source
- Licence:
- Apache 2.0 (engineering code; underlying OCR model copyright remains Baidu's)
- Starting at:
- Free and open source. Apache 2.0 covers the engineering scripts, and the README states plainly that copyright in the OCR models themselves stays with Baidu, since the weights are converted PaddleOCR models. No API key, no account, no network call at inference. Runs on CPU, so the marginal cost of an extra deployment is a container slot rather than GPU hours.
- Added:
- Aug 2026
- Updated:
- Aug 2026
Use Cases
Judge it on your own work
The notes above say where RapidOCR holds and where it breaks. The fastest check is your own workload.
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