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MonkeyOCR

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

arXiv

HuggingFace

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

arXiv

Source_code

Model Weight

Model Weight

Public Courses

Demo

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.

Inference Speed (Pages/s) on Different GPUs and PDF Page Counts

Model	GPU	50 Pages	100 Pages	300 Pages	500 Pages	1000 Pages
MonkeyOCR-pro-3B	3090	0.492	0.484	0.497	0.492	0.496
A6000	0.585	0.587	0.609	0.598	0.608
H800	0.923	0.768	0.897	0.930	0.891
4090	0.972	0.969	1.006	0.986	1.006
MonkeyOCR-pro-1.2B	3090	0.615	0.660	0.677	0.687	0.683
A6000	0.709	0.786	0.825	0.829	0.825
H800	0.965	1.082	1.101	1.145	1.015
4090	1.194	1.314	1.436	1.442	1.434

VLM OCR Speed (Pages/s) on Different GPUs and PDF Page Counts

Model	GPU	50 Pages	100 Pages	300 Pages	500 Pages	1000 Pages
MonkeyOCR-pro-3B	3090	0.705	0.680	0.711	0.700	0.724
A6000	0.885	0.860	0.915	0.892	0.934
H800	1.371	1.135	1.339	1.433	1.509
4090	1.321	1.300	1.384	1.343	1.410
MonkeyOCR-pro-1.2B	3090	0.919	1.086	1.166	1.182	1.199
A6000	1.177	1.361	1.506	1.525	1.569
H800	1.466	1.719	1.763	1.875	1.650
4090	1.759	1.987	2.260	2.345	2.415

Supported Hardware

Due to the limited types of GPUs available to us, we may not be able to provide highly accurate hardware specifications. We've tested the model on GPUs such as the 3090, 4090, A6000, H800, A100, and even the 4060 with 8GB of VRAM (suitable for deploying quantized 3B model and 1.2B model). We are very grateful for the feedback and contributions from the open-source community, who have also successfully run the model on 50-series GPUs, H200, L20, V100, 2080 Ti and npu.

News

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. Thanks for the love!
2025.06.05 🚀 We release MonkeyOCR, an English and Chinese documents parsing model.

Quick Start

Locally Install

1. Install MonkeyOCR

See the installation guide to set up your environment.

2. Download Model Weights

Download our model from Huggingface.

python

pip install huggingface_hub

python tools/download_model.py -n MonkeyOCR-pro-3B  # or MonkeyOCR

You can also download our model from ModelScope.

python

pip install modelscope

python tools/download_model.py -t modelscope -n MonkeyOCR-pro-3B  # or MonkeyOCR

3. Inference

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

bash

# Replace input_path with the path to a PDF or image or directory

# End-to-end parsing
python parse.py input_path

# Parse files in a dir with specific group page num
python parse.py input_path -g 20

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

# Parse PDFs in input_path and split results by pages
python parse.py input_path -s

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

More usage examples

bash

# Single file processing
python parse.py input.pdf                           # Parse single PDF file
python parse.py input.pdf -o ./output               # Parse with custom output dir
python parse.py input.pdf -s                        # Parse PDF with page splitting
python parse.py image.jpg                           # Parse single image file

# Single task recognition
python parse.py image.jpg -t text                   # Text recognition from image
python parse.py image.jpg -t formula                # Formula recognition from image
python parse.py image.jpg -t table                  # Table recognition from image
python parse.py document.pdf -t text                # Text recognition from all PDF pages

# Folder processing (all files individually)
python parse.py /path/to/folder                     # Parse all files in folder
python parse.py /path/to/folder -s                  # Parse with page splitting
python parse.py /path/to/folder -t text             # Single task recognition for all files

# Multi-file grouping (batch processing by page count)
python parse.py /path/to/folder -g 5                # Group files with max 5 total pages
python parse.py /path/to/folder -g 10 -s            # Group files with page splitting
python parse.py /path/to/folder -g 8 -t text        # Group files for single task recognition

# Advanced configurations
python parse.py input.pdf -c model_configs.yaml     # Custom model configuration
python parse.py /path/to/folder -g 15 -s -o ./out   # Group files, split pages, custom output
python parse.py input.pdf --pred-abandon            # Enable predicting abandon elements
  python parse.py /path/to/folder -g 10 -m            # Group files and merge text blocks in output

Output Results

MonkeyOCR mainly generates three types of output files:

Processed Markdown File (your.md): The final parsed document content in markdown format, containing text, formulas, tables, and other structured elements.
Layout Results (your_layout.pdf): The layout results drawed on origin PDF.
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.

4. Gradio Demo

python demo/demo_gradio.py

Once the demo is running, you can access it at http://localhost:7860.

5. Fast API

You can start the MonkeyOCR FastAPI service with the following command:

uvicorn api.main:app --port 8000

Once the API service is running, you can access the API documentation at http://localhost:8000/docs to explore available endpoints.

[!TIP] To improve API concurrency performance, consider configuring the inference backend as lmdeploy_queue or vllm_queue.

Docker Deployment

Navigate to the docker directory:

cd docker
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
Build the Docker image:

docker compose build monkeyocr

[!IMPORTANT]

If your GPU is from the 20/30/40-series, V100, L20/L40 or similar, please build the patched Docker image for LMDeploy compatibility:

docker compose build monkeyocr-fix

Otherwise, you may encounter the following error: triton.runtime.errors.OutOfResources: out of resource: shared memory

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
Run the FastAPI service (accessible on port 7861):

docker compose up monkeyocr-api

Once the API service is running, you can access the API documentation at http://localhost:7861/docs to explore available endpoints.

Windows Support

See the windows support guide for details.

Quantization

This model can be quantized using AWQ. Follow the instructions in the quantization guide.

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↓	Table^TEDS↑	Table^Edit↓	Read Order^Edit↓
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	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	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	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	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	64.8	27.5	0.284	0.639	0.595	0.641
PP-StructureV3	0.145	0.206	0.058	0.088	0.295	0.535	-	-	0.159	0.109	0.069	0.091
Expert VLMs	GOT-OCR	0.287	0.411	0.189	0.315	0.360	0.528	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	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	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	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	44.9	16.5	0.729	0.907	0.227	0.522
Dolphin	0.206	0.306	0.107	0.197	0.447	0.580	77.3	67.2	0.180	0.285	0.091	0.162
MinerU 2	0.139	0.240	0.047	0.109	0.297	0.536	82.5	79.0	0.141	0.195	0.069	0.118
OCRFlux	0.195	0.281	0.064	0.183	0.379	0.613	71.6	81.3	0.253	0.139	0.086	0.187
General VLMs	GPT4o	0.233	0.399	0.144	0.409	0.425	0.606	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	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	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	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	78.2	76.2	0.182	0.262	0.105	0.183
MonkeyOCR-pro-3B [Weight]	0.138	0.206	0.067	0.107	0.246	0.421	81.5	87.5	0.139	0.111	0.100	0.185
MonkeyOCR-pro-1.2B [Weight]	0.153	0.223	0.066	0.123	0.272	0.449	76.5	83.7	0.176	0.131	0.097	0.187

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
Dolphin	0.091	0.131	0.057	0.146	0.231	0.121	0.074	0.363	0.307	0.177
OCRFlux	0.068	0.125	0.092	0.102	0.119	0.083	0.047	0.223	0.536	0.149
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
MonkeyOCR-pro-3B [Weight]	0.084	0.129	0.060	0.090	0.107	0.073	0.050	0.171	0.107	0.100
MonkeyOCR-pro-1.2B [Weight]	0.087	0.142	0.059	0.093	0.115	0.085	0.045	0.226	0.122	0.112

3. The evaluation results of olmOCR-bench.

Model	ArXiv	Old Scans Math	Tables	Old Scans	Headers and Footers	Multi column	Long Tiny Text	Base	Overall
GOT OCR	52.7	52.0	0.2	22.1	93.6	42.0	29.9	94.0	48.3 ± 1.1
Marker	76.0	57.9	57.6	27.8	84.9	72.9	84.6	99.1	70.1 ± 1.1
MinerU	75.4	47.4	60.9	17.3	96.6	59.0	39.1	96.6	61.5 ± 1.1
Mistral OCR	77.2	67.5	60.6	29.3	93.6	71.3	77.1	99.4	72.0 ± 1.1
Nanonets OCR	67.0	68.6	77.7	39.5	40.7	69.9	53.4	99.3	64.5 ± 1.1
GPT-4o (No Anchor)	51.5	75.5	69.1	40.9	94.2	68.9	54.1	96.7	68.9 ± 1.1
GPT-4o (Anchored)	53.5	74.5	70.0	40.7	93.8	69.3	60.6	96.8	69.9 ± 1.1
Gemini Flash 2 (No Anchor)	32.1	56.3	61.4	27.8	48.0	58.7	84.4	94.0	57.8 ± 1.1
Gemini Flash 2 (Anchored)	54.5	56.1	72.1	34.2	64.7	61.5	71.5	95.6	63.8 ± 1.2
Qwen 2 VL (No Anchor)	19.7	31.7	24.2	17.1	88.9	8.3	6.8	55.5	31.5 ± 0.9
Qwen 2.5 VL (No Anchor)	63.1	65.7	67.3	38.6	73.6	68.3	49.1	98.3	65.5 ± 1.2
olmOCR v0.1.75 (No Anchor)	71.5	71.4	71.4	42.8	94.1	77.7	71.0	97.8	74.7 ± 1.1
olmOCR v0.1.75 (Anchored)	74.9	71.2	71.0	42.2	94.5	78.3	73.3	98.3	75.5 ± 1.0
MonkeyOCR-pro-3B [Weight]	83.8	68.8	74.6	36.1	91.2	76.6	80.1	95.3	75.8 ± 1.0
MonkeyOCR-pro-1.2B [Weight]	80.5	62.9	71.1	32.9	92.2	68.3	74.0	92.6	71.8 ± 1.1

Visualization Demo

Get a Quick Hands-On Experience with Our Demo: http://vlrlabmonkey.xyz:7685 (The latest model is available for selection)

Our demo is simple and easy to use:

Upload a PDF or image.

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.

Select a prompt and click “Test by prompt” to let the model perform content recognition on the image based on the selected prompt.

Example for formula document

Example for table document

Example for newspaper

Example for financial report

Citing MonkeyOCR

If you wish to refer to the baseline results published here, please use the following BibTeX entries:

BibTeX

@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, 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.

Copyright

Please don’t hesitate to share your valuable feedback — it’s a key motivation that drives us to continuously improve our framework. Note: Our model is intended for academic research and non-commercial use only. If you are interested in faster (smaller) or stronger one, please contact us at xbai@hust.edu.cn or ylliu@hust.edu.cn.

echo840/MonkeyOCR