MonkeyOCR: Document Parsing with a Structure-Recognition-Relation Triplet Paradigm

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

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.

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).

News

• 2025.06.05 🚀 We release MonkeyOCR, which supports the parsing of various types of Chinese and English documents.

Quick Start

1. Install MonkeyOCR

conda create -n MonkeyOCR python=3.10
conda activate MonkeyOCR

git clone https://github.com/Yuliang-Liu/MonkeyOCR.git
cd MonkeyOCR

# Install pytorch, see https://pytorch.org/get-started/previous-versions/ for your cuda version
pip install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu124 
pip install .

2. Download Model Weights

pip install huggingface_hub

python download_model.py

3. Inference

# Make sure in MonkeyOCR directory
python parse.py path/to/your.pdf
# Specify MonkeyChat path and model configs path
python parse.py path/to/your.pdf -m model_weight/Recognition -c config.yaml

4. Gradio demo

# Prepare your env for gradio
pip install gradio==5.23.3
pip install pdf2image==1.17.0

# Start demo
python demo/demo_gradio.py

Using the LMDeploy, our model can run efficiently on an NVIDIA 3090 GPU.

Benchmark Results

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.

1. The end-to-end evaluation results of different tasks.

Model Type	Methods	Overall Edit↓		Text Edit↓		Formula Edit↓		Formula CDM↑		Table TEDS↑		Table Edit↓		Read Order Edit↓
		EN	ZH	EN	ZH	EN	ZH	EN	ZH	EN	ZH	EN	ZH	EN	ZH
Pipeline Tools	MinerU	0.150	0.357	0.061	0.215	0.278	0.577	57.3	42.9	78.6	62.1	0.180	0.344	0.079	0.292
	Marker	0.336	0.556	0.080	0.315	0.530	0.883	17.6	11.7	67.6	49.2	0.619	0.685	0.114	0.340
	Mathpix	0.191	0.365	0.105	0.384	0.306	0.454	62.7	62.1	77.0	67.1	0.243	0.320	0.108	0.304
	Docling	0.589	0.909	0.416	0.987	0.999	1	-	-	61.3	25.0	0.627	0.810	0.313	0.837
	Pix2Text	0.320	0.528	0.138	0.356	0.276	0.611	78.4	39.6	73.6	66.2	0.584	0.645	0.281	0.499
	Unstructured	0.586	0.716	0.198	0.481	0.999	1	-	-	0	0.06	1	0.998	0.145	0.387
	OpenParse	0.646	0.814	0.681	0.974	0.996	1	0.11	0	64.8	27.5	0.284	0.639	0.595	0.641
Expert VLMs	GOT-OCR	0.287	0.411	0.189	0.315	0.360	0.528	74.3	45.3	53.2	47.2	0.459	0.520	0.141	0.280
	Nougat	0.452	0.973	0.365	0.998	0.488	0.941	15.1	16.8	39.9	0	0.572	1.000	0.382	0.954
	Mistral OCR	0.268	0.439	0.072	0.325	0.318	0.495	64.6	45.9	75.8	63.6	0.600	0.650	0.083	0.284
	OLMOCR-sglang	0.326	0.469	0.097	0.293	0.455	0.655	74.3	43.2	68.1	61.3	0.608	0.652	0.145	0.277
	SmolDocling-256M	0.493	0.816	0.262	0.838	0.753	0.997	32.1	0.55	44.9	16.5	0.729	0.907	0.227	0.522
General VLMs	GPT4o	0.233	0.399	0.144	0.409	0.425	0.606	72.8	42.8	72.0	62.9	0.234	0.329	0.128	0.251
	Qwen2.5-VL-7B	0.312	0.406	0.157	0.228	0.351	0.574	79.0	50.2	76.4	72.2	0.588	0.619	0.149	0.203
	InternVL3-8B	0.314	0.383	0.134	0.218	0.417	0.563	78.3	49.3	66.1	73.1	0.586	0.564	0.118	0.186
Mix	MonkeyOCR-3B [Weight]	0.140	0.297	0.058	0.185	0.238	0.506	78.7	51.4	80.2	77.7	0.170	0.253	0.093	0.244
	MonkeyOCR-3B* [Weight]	0.154	0.277	0.073	0.134	0.255	0.529	78.5	50.8	78.2	76.2	0.182	0.262	0.105	0.183

2. The end-to-end text recognition performance across 9 PDF page types.

Model Type	Models	Book	Slides	Financial Report	Textbook	Exam Paper	Magazine	Academic Papers	Notes	Newspaper	Overall
Pipeline Tools	MinerU	0.055	0.124	0.033	0.102	0.159	0.072	0.025	0.984	0.171	0.206
	Marker	0.074	0.340	0.089	0.319	0.452	0.153	0.059	0.651	0.192	0.274
	Mathpix	0.131	0.220	0.202	0.216	0.278	0.147	0.091	0.634	0.690	0.300
Expert VLMs	GOT-OCR	0.111	0.222	0.067	0.132	0.204	0.198	0.179	0.388	0.771	0.267
	Nougat	0.734	0.958	1.000	0.820	0.930	0.830	0.214	0.991	0.871	0.806
General VLMs	GPT4o	0.157	0.163	0.348	0.187	0.281	0.173	0.146	0.607	0.751	0.316
	Qwen2.5-VL-7B	0.148	0.053	0.111	0.137	0.189	0.117	0.134	0.204	0.706	0.205
	InternVL3-8B	0.163	0.056	0.107	0.109	0.129	0.100	0.159	0.150	0.681	0.188
Mix	MonkeyOCR-3B [Weight]	0.046	0.120	0.024	0.100	0.129	0.086	0.024	0.643	0.131	0.155
	MonkeyOCR-3B* [Weight]	0.054	0.203	0.038	0.112	0.138	0.111	0.032	0.194	0.136	0.120

3. Comparing MonkeyOCR with closed-source and extra large open-source VLMs.

Visualization Demo

Demo Link: 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 “Chat (对话)” to let the model perform content recognition on the image based on the selected prompt.

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}, 
}

Acknowledgments

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.

Copyright

MonkeyDoc dataset was collected from public datasets, crawled from the internet, and obtained through our own photography. The current technical report only presents the results of the 3B model. If you are interested in larger one, please contact Prof. Yuliang Liu at ylliu@hust.edu.cn.