--- title: onnxocr_model canonical_url: "https://www.modelscope.cn/models/supersong/onnxocr_model" md_url: "https://www.modelscope.cn/models/supersong/onnxocr_model.md" repository: supersong/onnxocr_model chinese_name: "onnxocr模型" last_updated: 2026-05-15 pipeline_tag: ocr-recognition tasks: - ocr-recognition library_name: - onnx - other frameworks: - other downloads: 251 stars: 1 --- # onnxocr_model > onnxocr_model - supersong 在 ModelScope 开源的模型。onnxocr模型 - **Repository**: supersong/onnxocr_model - **Tasks**: ocr-recognition - **Downloads**: 251 - **Stars**: 1 - **Last updated**: 2026-05-15 Source: https://www.modelscope.cn/models/supersong/onnxocr_model --- # OnnxOCR If this project helps you, please consider giving it a **Star**. ![onnx_logo](onnxocr/test_images/onnxocr_logo.png) **A high-performance multilingual OCR project based on ONNXRuntime** ![GitHub stars](https://img.shields.io/github/stars/jingsongliujing/OnnxOCR?style=social) ![GitHub forks](https://img.shields.io/github/forks/jingsongliujing/OnnxOCR?style=social) ![GitHub license](https://img.shields.io/github/license/jingsongliujing/OnnxOCR) ![Python Version](https://img.shields.io/badge/python-%3E%3D3.8-blue.svg) English | [简体中文](./Readme_cn.md) ## Version Updates - **2026.05.01** 1. Added ONNX license plate detection and recognition. 2. Added RapidTable-based ONNX table recognition. 3. Added RapidLayout-based Chinese and English layout analysis. 4. Added RapidDoc-based document layout analysis and Markdown export. 5. Added `/plate`, `/table`, `/layout`, `/layout_markdown`, and related HTTP endpoints. - **2025.05.21** 1. Added PP-OCRv5 models, supporting Simplified Chinese, Traditional Chinese, Chinese Pinyin, English, and Japanese in one model. 2. Improved overall recognition accuracy compared with PP-OCRv4. 3. Recognition accuracy is consistent with PaddleOCR 3.0. ## Core Advantages 1. **Deep learning framework free**: a general OCR project ready for deployment. 2. **Cross-architecture support**: PaddleOCR-converted ONNX models can run on ARM and x86 devices. 3. **Unified inference engine**: all ONNX models create ONNXRuntime sessions through `onnxocr/inference_engine.py`. 4. **Multilingual support**: one model supports 5 text types. 5. **Source-level integration**: `rapid_layout`, `rapid_table`, and `rapid_doc` live under the `onnxocr/` package, with no dependency on `rapidocr==3.4.3` or `rapid-orientation`. 6. **Hardware adaptation friendly**: downstream vendors can adapt GPU/NPU providers by modifying the unified inference engine. ## Environment Setup ```bash python>=3.8 pip install -i https://pypi.tuna.tsinghua.edu.cn/simple -r requirements.txt ``` Notes: - The Mobile model is used by default. - Mobile models are already stored in `onnxocr/models/ppocrv5`. - Larger PP-OCRv5 Server ONNX models can be downloaded separately and used to replace det/rec models under `onnxocr/models/ppocrv5/`. ## One-Click Run ```bash python test_ocr.py ``` Feature-specific tests: ```bash python tests/test_general_ocr.py python tests/test_license_plate_ocr.py python tests/test_table_ocr.py python tests/test_layout_analysis.py python tests/test_layout_markdown.py ``` Generated files are written to `result_img/`, which is ignored by git. ## General OCR ```python import cv2 from onnxocr.onnx_paddleocr import ONNXPaddleOcr img = cv2.imread("onnxocr/test_images/715873facf064583b44ef28295126fa7.jpg") model = ONNXPaddleOcr(use_angle_cls=True, use_gpu=False) result = model.ocr(img) print(result) ``` ## License Plate Recognition License plate recognition is integrated into `ONNXPaddleOcr` as an optional mode. Existing general OCR usage is unchanged. ```python from onnxocr.onnx_paddleocr import ONNXPaddleOcr, sav2PlateImg plate_model = ONNXPaddleOcr( use_angle_cls=True, use_gpu=False, use_plate_recognition=True, plate_min_score=0.4, ) plate_result = plate_model.ocr(img) sav2PlateImg(img, plate_result, name="./result_img/test_plate_vis.jpg") ``` Model files: ```text onnxocr/models/license_plate/car_plate_detect.onnx onnxocr/models/license_plate/plate_rec.onnx ``` ## Table Recognition Table recognition is integrated from RapidTable. It reuses general OCR detection/recognition results, restores table structure, and outputs HTML, cell boxes, and logical row/column coordinates. ```python from onnxocr.onnx_paddleocr import ONNXPaddleOcr, sav2TableImg table_model = ONNXPaddleOcr( use_angle_cls=True, use_gpu=False, use_table_recognition=True, table_model_type="slanet_plus", ) table_result = table_model.ocr(img) print(table_result["html"]) sav2TableImg(img, table_result, name="./result_img/test_table_vis.jpg") ``` Model files: ```text onnxocr/models/table/slanet-plus.onnx onnxocr/models/table/ch_ppstructure_mobile_v2_SLANet.onnx onnxocr/models/table/en_ppstructure_mobile_v2_SLANet.onnx ``` ## Chinese / English Layout Analysis Layout analysis is integrated from RapidLayout. It locates document elements such as titles, text blocks, tables, figures, headers, and footers. ```python from onnxocr.onnx_paddleocr import ONNXPaddleOcr, sav2LayoutImg layout_model = ONNXPaddleOcr( use_gpu=False, use_layout_analysis=True, layout_model_type="pp_layout_cdla", ) layout_result = layout_model.ocr(img) sav2LayoutImg(img, layout_result, name="./result_img/test_layout_vis.jpg") ``` For English layout analysis, set `layout_model_type` to `pp_layout_publaynet`. Model files: ```text onnxocr/models/layout/layout_cdla.onnx onnxocr/models/layout/layout_publaynet.onnx ``` ## Document To Markdown Document-to-Markdown export is integrated from RapidDoc. It analyzes titles, paragraphs, tables, figures, and other layout elements, then saves the result as a Markdown file. ```python from onnxocr.layout_markdown import LayoutMarkdownConverter converter = LayoutMarkdownConverter( layout_model_type="pp_doclayoutv2", formula_enable=False, table_enable=True, ) result = converter.convert_file( "onnxocr/test_images/layout_cdla.jpg", output_md_path="./result_img/test_layout_markdown.md", ) print(result["markdown_path"]) ``` RapidDoc model files: ```text onnxocr/models/rapid_doc/layout/pp_doclayoutv2.onnx onnxocr/models/rapid_doc/table/q_cls.onnx onnxocr/models/rapid_doc/table/unet.onnx onnxocr/models/rapid_doc/table/slanet-plus.onnx ``` ## Inference Engine Adaptation General OCR, license plate recognition, table recognition, layout analysis, and RapidDoc document parsing all create ONNXRuntime sessions through `onnxocr/inference_engine.py`. To adapt a downstream GPU/NPU provider, start from: ```python from onnxocr.inference_engine import create_session ``` Main extension points: - `create_session(model_path, providers=None, use_gpu=False, gpu_id=0, sess_options=None)` - `build_providers(use_gpu=False, gpu_id=0, providers=None)` - `build_providers_from_engine_cfg(engine_cfg)` - `ProviderConfig(engine_cfg)` Only `onnxocr/inference_engine.py` imports `onnxruntime` directly. Feature modules do not call ONNXRuntime APIs directly. ## API Service Start service: ```bash python app-service.py ``` Main endpoints: - `/ocr`: general OCR. - `/plate`: license plate recognition. - `/table`: table recognition. - `/layout`: layout analysis. - `/layout_markdown`: image/PDF to Markdown. WebUI: ```bash python webui.py ``` ## Docker Image ```bash docker build -t ocr-service . docker run -itd --name onnxocr-service -p 5006:5005 ocr-service ``` ## Project Layout ```text onnxocr/ inference_engine.py # single ONNXRuntime entry onnx_paddleocr.py # public user API predict_det.py # general OCR detection predict_rec.py # general OCR recognition orientation.py # local RapidOrientation ONNX adapter license_plate.py # license plate OCR table_recognition.py # table recognition wrapper layout_recognition.py # layout analysis wrapper layout_markdown.py # RapidDoc Markdown wrapper rapid_layout/ # source-level RapidLayout ONNX integration rapid_table/ # source-level RapidTable ONNX integration rapid_doc/ # source-level RapidDoc ONNX integration models/ # local ONNX models tests/ # feature-specific tests ``` ## Effect Demonstration | Example 1 | Example 2 | |-----------|-----------| | ![](result_img/r1.png) | ![](result_img/r2.png) | | Example 3 | Example 4 | |-----------|-----------| | ![](result_img/r3.png) | ![](result_img/draw_ocr4.jpg) | ## Contact & Communication ### OnnxOCR Community ![WeChat Group](onnxocr/test_images/微信群.jpg) ![QQ Group](onnxocr/test_images/QQ群.jpg) ## Acknowledgments Thanks to [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR) for technical support and model references. Thanks to the [RapidAI](https://github.com/RapidAI) open-source community, including [RapidTable](https://github.com/RapidAI/RapidTable), [RapidLayout](https://github.com/RapidAI/RapidLayout), [RapidDoc](https://github.com/RapidAI/RapidDoc), and [RapidOrientation](https://github.com/RapidAI/RapidOrientation), for excellent models, code, and engineering references. ## Open Source & Donations If you recognize this project, you can support it via Alipay or WeChat Pay. WeChat Pay Alipay ## Star History [![Star History Chart](https://api.star-history.com/svg?repos=jingsongliujing/OnnxOCR&type=Date)](https://star-history.com/#jingsongliujing/OnnxOCR&Date) ## Contribution Guidelines Issues and Pull Requests are welcome.