[SCIENCE CHINA Information Science] MonkeyOCR: Document Parsing with a Structure-Recognition-Relation Triplet Paradigm
Zhang Li, Yuliang Liu, Qiang Liu, Zhiyin Ma, Ziyang Zhang, Shuo Zhang, Zidun Guo, Jiarui Zhang, Xinyu Wang, Xiang Bai
News
[!IMPORTANT]
🚀 MonkeyOCRv2 is here (July 2026) — a document-native vision backbone plus a
0.7B parser that is the new open-source SOTA on multilingual document parsing
(17 languages), now released under Apache-2.0.
👉 Yuliang-Liu/MonkeyOCRv2 · Paper · Demo
2026.07.12🚀 MonkeyOCR is accepted by SCIENCE CHINA Information Sciences.2026.04.01🚀 dots.mocr achieves the best open-source score on MDPBench, a 17-language document parsing benchmark!2026.03.15🚀 We release dots.mocr Multimodal OCR: Parse Anything from Documents.2026.01.30🚀 We release MonkeyDoc and provide the necessary details of our data generation pipeline.2025.11.14🚀 We release MonkeyOCR-v1.5 Technical Report, achieving the best document parsing performance to date. Demo.2025.07.10🚀 We release MonkeyOCR-pro-1.2B, a leaner and faster version model that outperforms our previous 3B version in accuracy, speed, and efficiency.2025.06.12🚀 The model’s trending on Hugging Face #2.2025.06.05🚀 We release MonkeyOCR, an English and Chinese documents parsing model.
Introduction
MonkeyOCR adopts a Structure-Recognition-Relation (SRR) triplet paradigm, which simplifies the multi-tool pipeline of modular approaches while avoiding the inefficiency of using large multimodal models for full-page document processing.
- MonkeyOCR-pro-1.2B surpasses MonkeyOCR-3B by 7.4% on Chinese documents.
- MonkeyOCR-pro-1.2B delivers approximately a 36% speed improvement over MonkeyOCR-pro-3B, with approximately 1.6% drop in performance.
- On olmOCR-Bench, MonkeyOCR-pro-1.2B outperforms Nanonets-OCR-3B by 7.3%.
- On OmniDocBench, MonkeyOCR-pro-3B achieves the best overall performance on both English and Chinese documents, outperforming even closed-source and extra-large open-source VLMs such as Gemini 2.0-Flash, Gemini 2.5-Pro, Qwen2.5-VL-72B, GPT-4o, and InternVL3-78B.
See detailed results below.
Comparing MonkeyOCR with closed-source and extra large open-source VLMs.
Example for formula document
Example for table document
Example for newspaper
Example for financial report
Related Projects
MonkeyOCR v1.5 Technical Report: Unlocking Robust Document Parsing for Complex Patterns
Jiarui Zhang, Yuliang Liu, Zijun Wu, Guosheng Pang, Zhili Ye, Yupei Zhong, Junteng Ma, Tao Wei, Haiyang Xu, Weikai Chen, Zeen Wang, Qiangjun Ji, Fanxi Zhou, Qi Zhang, Yuanrui Hu, Jiahao Liu, Zhang Li, Ziyang Zhang, Qiang Liu, Xiang Bai
Multimodal OCR: Parse Anything from Documents
Handong Zheng, Yumeng Li, Kaile Zhang, Liang Xin, Guangwei Zhao, Hao Liu, Jiayu Chen, Jie Lou, Qi Fu, Rui Yang, Shuo Jiang, Weijian Luo, Weijie Su, Weijun Zhang, Xingyu Zhu, Yabin Li, Yiwei ma, Yu Chen, Yuqiu Ji, Zhaohui Yu, Guang Yang, Colin Zhang, Lei Zhang, Yuliang Liu, Xiang Bai
Citing MonkeyOCR
If you wish to refer to the baseline results published here, please use the following BibTeX entries:
@misc{li2025monkeyocrdocumentparsingstructurerecognitionrelation,
title={MonkeyOCR: Document Parsing with a Structure-Recognition-Relation Triplet Paradigm},
author={Zhang Li and Yuliang Liu and Qiang Liu and Zhiyin Ma and Ziyang Zhang and Shuo Zhang and Zidun Guo and Jiarui Zhang and Xinyu Wang and Xiang Bai},
year={2025},
eprint={2506.05218},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2506.05218},
}
@misc{zheng2026multimodalocrparsedocuments,
title={Multimodal OCR: Parse Anything from Documents},
author={Handong Zheng and Yumeng Li and Kaile Zhang and Liang Xin and Guangwei Zhao and Hao Liu and Jiayu Chen and Jie Lou and Qi Fu and Rui Yang and Shuo Jiang and Weijian Luo and Weijie Su and Weijun Zhang and Xingyu Zhu and Yabin Li and Yiwei ma and Yu Chen and Yuqiu Ji and Zhaohui Yu and Guang Yang and Colin Zhang and Lei Zhang and Yuliang Liu and Xiang Bai},
year={2026},
eprint={2603.13032},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2603.13032},
}Acknowledgments
We would like to thank MinerU, DocLayout-YOLO, PyMuPDF, layoutreader, Qwen2.5-VL, LMDeploy, PP-StructureV3, PP-DocLayout_plus-L, and InternVL3 for providing base code and models, as well as their contributions to this field. We also thank M6Doc, DocLayNet, CDLA, D4LA, DocGenome, PubTabNet, and UniMER-1M for providing valuable datasets. We also thank everyone who contributed to this open-source effort.
Limitation
Currently, MonkeyOCR do not yet fully support for photographed text, handwritten content, Traditional Chinese characters, or multilingual text. We plan to consider adding support for these features in future public releases. Additionally, our model is deployed on a single GPU, so if too many users upload files at the same time, issues like “This application is currently busy” may occur. The processing time shown on the demo page does not reflect computation time alone—it also includes result uploading and other overhead. During periods of high traffic, this time may be longer. The inference speeds of MonkeyOCR, MinerU, and Qwen2.5 VL-7B were measured on an H800 GPU.
License and Commercial Use
The source code in this repository is released under the Apache License 2.0.
The MonkeyOCR v1 model weights are provided for academic research and non-commercial evaluation only. Commercial deployment, SaaS integration, redistribution, or production use requires a separate written commercial license.
For commercial licensing inquiries, please contact [email protected] or [email protected].