Instructions to use PaddlePaddle/PP-DocLayoutV3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PaddleOCR
How to use PaddlePaddle/PP-DocLayoutV3 with PaddleOCR:
# 1. See https://www.paddlepaddle.org.cn/en/install to install paddlepaddle # 2. pip install paddleocr from paddleocr import LayoutDetection model = LayoutDetection(model_name="PP-DocLayoutV3") output = model.predict(input="path/to/image.png", batch_size=1) for res in output: res.print() res.save_to_img(save_path="./output/") res.save_to_json(save_path="./output/res.json") - Notebooks
- Google Colab
- Kaggle
OCR-Format
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by BigTiger78 - opened
README.md
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<h1 align="center">
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</h1>
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[](./LICENSE)
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**🔥 [Official Website](https://www.paddleocr.com)** |
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**📝 [Technical Report](https://arxiv.org/abs/
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</div>
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**PP-DocLayoutV3 is specifically engineered to handle non-planar document images. It can directly predict multi-point bounding boxes for layout elements—as opposed to standard two-point boxes—and determine logical reading orders for skewed and curved surfaces within a single forward pass, significantly reducing cascading errors.** This model is an essential component of PaddleOCR-VL-1.5, providing crucial layout analysis for the high-precision parsing of various real-world documents in PaddleOCR-VL.
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This work has been accepted to ECCV 2026! 🎉
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### **Model Architecture**
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If you find PP-DocLayoutV3 helpful, feel free to give us a star and citation.
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```bibtex
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@misc{
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title={
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author={Cheng Cui and
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year={2026},
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eprint={
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archivePrefix={arXiv},
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primaryClass={cs.CV},
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url={https://arxiv.org/abs/
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}
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```
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<h1 align="center">
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Layout Analysis Module of PaddleOCR-VL-1.5
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</h1>
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[](./LICENSE)
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**🔥 [Official Website](https://www.paddleocr.com)** |
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**📝 [Technical Report](https://arxiv.org/abs/2601.21957)**
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</div>
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**PP-DocLayoutV3 is specifically engineered to handle non-planar document images. It can directly predict multi-point bounding boxes for layout elements—as opposed to standard two-point boxes—and determine logical reading orders for skewed and curved surfaces within a single forward pass, significantly reducing cascading errors.** This model is an essential component of PaddleOCR-VL-1.5, providing crucial layout analysis for the high-precision parsing of various real-world documents in PaddleOCR-VL.
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### **Model Architecture**
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If you find PP-DocLayoutV3 helpful, feel free to give us a star and citation.
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```bibtex
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@misc{cui2026paddleocrvl15multitask09bvlm,
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title={PaddleOCR-VL-1.5: Towards a Multi-Task 0.9B VLM for Robust In-the-Wild Document Parsing},
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author={Cheng Cui and Ting Sun and Suyin Liang and Tingquan Gao and Zelun Zhang and Jiaxuan Liu and Xueqing Wang and Changda Zhou and Hongen Liu and Manhui Lin and Yue Zhang and Yubo Zhang and Yi Liu and Dianhai Yu and Yanjun Ma},
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year={2026},
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eprint={2601.21957},
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archivePrefix={arXiv},
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primaryClass={cs.CV},
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url={https://arxiv.org/abs/2601.21957},
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}
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```
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