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

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.

1. Compared with the pipeline-based method MinerU, our approach achieves an average improvement of 5.1% across nine types of Chinese and English documents, including a 15.0% gain on formulas and an 8.6% gain on tables.
2. Compared to end-to-end models, our 3B-parameter model achieves the best average performance on English documents, outperforming models such as Gemini 2.5 Pro and Qwen2.5 VL-72B.
3. For multi-page document parsing, our method reaches a processing speed of 0.84 pages per second, surpassing MinerU (0.65) and Qwen2.5 VL-7B (0.12).

MonkeyOCR currently does not support photographed documents, but we will continue to improve it in future updates. Stay tuned! Currently, 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. We're actively working on supporting Ollama and other deployment solutions to ensure a smoother experience for more users. Additionally, please note that 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.

• 2025.06.12 🚀 The model’s trending on Hugging Face. Thanks for the love!
• 2025.06.05 🚀 We release MonkeyOCR, an English and Chinese documents parsing model.

See the installation guide to set up your environment.

Download our model from Huggingface.

pip install huggingface_hub

python tools/download_model.py

You can also download our model from ModelScope.

pip install modelscope

python tools/download_model.py -t modelscope

You can parse a file or a directory containing PDFs or images using the following commands:

# Make sure you are in the MonkeyOCR directory
# Replace input_path with the path to a PDF or image or directory
# End-to-end parsing
python parse.py input_path

# Single-task recognition (outputs markdown only)
python parse.py input_path -t text/formula/table

# Specify output directory and model config file
python parse.py input_path -o ./output -c config.yaml

For Chinese scenarios, or cases where text, tables, etc. are mistakenly recognized as images, you can try using the following structure detection model: layout_zh.pt. (If the model is not found in model_weight/Structure/, you can download it manually.)

To use this model, update the configuration file as follows:
In model_configs.yaml, replace:

Structure/doclayout_yolo_docstructbench_imgsz1280_2501.pt

with:

Structure/layout_zh.pt

MonkeyOCR generates three types of output files:

1. Processed Markdown File (your.md): The final parsed document content in markdown format, containing text, formulas, tables, and other structured elements.
2. Layout Results (your_layout.pdf): The layout results drawed on origin PDF.
3. Intermediate Block Results (your_middle.json): A JSON file containing detailed information about all detected blocks, including:
   • Block coordinates and positions
   • Block content and type information
   • Relationship information between blocks

These files provide both the final formatted output and detailed intermediate results for further analysis or processing.

# Start demo
python demo/demo_gradio.py

1. Navigate to the docker directory:

   cd docker

2. Prerequisite: Ensure NVIDIA GPU support is available in Docker (via nvidia-docker2). If GPU support is not enabled, run the following to set up the environment:

   bash env.sh

3. Build the Docker image:

   docker compose build monkeyocr

   Note: If your GPU is from the 30-series, 40-series, or similar, build the patched image for LMDeploy compatibility:

   docker compose build monkeyocr-fix

4. Run the container with the Gradio demo (accessible on port 7860):

   docker compose up monkeyocr-demo

   Alternatively, start an interactive development environment:

   docker compose run --rm monkeyocr-dev

5. Run the FastAPI service (accessible on port 7861):

   docker compose up monkeyocr-api

Here are the evaluation results of our model on OmniDocBench. MonkeyOCR-3B uses DocLayoutYOLO as the structure detection model, while MonkeyOCR-3B* uses our trained structure detection model with improved Chinese performance.

Get a Quick Hands-On Experience with Our Demo: http://vlrlabmonkey.xyz:7685

Our demo is simple and easy to use:

1. Upload a PDF or image.
2. Click “Parse (解析)” to let the model perform structure detection, content recognition, and relationship prediction on the input document. The final output will be a markdown-formatted version of the document.
3. Select a prompt and click “Test by prompt” to let the model perform content recognition on the image based on the selected prompt.

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}, 
}

We would like to thank MinerU, DocLayout-YOLO, PyMuPDF, layoutreader, Qwen2.5-VL, LMDeploy, 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.

If you find that our model doesn’t fully meet your needs, feel free to try out the following two recently released awesome models:

PP-StructureV3

MinerU 2.0

Please don’t hesitate to share your valuable feedback — it’s a key motivation that drives us to continuously improve our framework. The current technical report only presents the results of the 3B model. Our model is intended for non-commercial use. If you are interested in larger one, please contact us at xbai@hust.edu.cn or ylliu@hust.edu.cn.