MonkeyOCR

LICENSE

@@ -1,52 +0,0 @@
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README.md

@@ -1,11 +1,10 @@
 ---
+license: apache-2.0
 pipeline_tag: image-text-to-text
 library_name: monkeyocr
 language:
 - zh
 - en
-tags:
-- OCR
 ---
 <div align="center" xmlns="http://www.w3.org/1999/html">
 <h1 align="center">
@@ -16,7 +15,6 @@ MonkeyOCR: Document Parsing with a Structure-Recognition-Relation Triplet Paradi
 [![HuggingFace](https://img.shields.io/badge/HuggingFace%20Weights-black.svg?logo=HuggingFace)](https://huggingface.co/echo840/MonkeyOCR)
 [![GitHub issues](https://img.shields.io/github/issues/Yuliang-Liu/MonkeyOCR?color=critical&label=Issues)](https://github.com/Yuliang-Liu/MonkeyOCR/issues?q=is%3Aopen+is%3Aissue)
 [![GitHub closed issues](https://img.shields.io/github/issues-closed/Yuliang-Liu/MonkeyOCR?color=success&label=Issues)](https://github.com/Yuliang-Liu/MonkeyOCR/issues?q=is%3Aissue+is%3Aclosed)
-[![License](https://img.shields.io/badge/License-Apache%202.0-yellow)](https://github.com/Yuliang-Liu/MonkeyOCR/blob/main/LICENSE.txt)
 [![GitHub views](https://komarev.com/ghpvc/?username=Yuliang-Liu&repo=MonkeyOCR&color=brightgreen&label=Views)](https://github.com/Yuliang-Liu/MonkeyOCR)
 </div>
 
@@ -24,10 +22,9 @@ MonkeyOCR: Document Parsing with a Structure-Recognition-Relation Triplet Paradi
 > **MonkeyOCR: Document Parsing with a Structure-Recognition-Relation Triplet Paradigm**<br>
 > Zhang Li, Yuliang Liu, Qiang Liu, Zhiyin Ma, Ziyang Zhang, Shuo Zhang, Zidun Guo, Jiarui Zhang, Xinyu Wang, Xiang Bai <br>
 [![arXiv](https://img.shields.io/badge/Arxiv-b31b1b.svg?logo=arXiv)](https://arxiv.org/abs/2506.05218) 
-[![Source_code](https://img.shields.io/badge/Code-Available-white)](README.md)
+[![Source_code](https://img.shields.io/badge/Code-Available-white)](https://github.com/Yuliang-Liu/MonkeyOCR)
 [![Model Weight](https://img.shields.io/badge/HuggingFace-gray)](https://huggingface.co/echo840/MonkeyOCR)
 [![Model Weight](https://img.shields.io/badge/ModelScope-green)](https://modelscope.cn/models/l1731396519/MonkeyOCR)
-[![Public Courses](https://img.shields.io/badge/Openbayes-yellow)](https://openbayes.com/console/public/tutorials/91ESrGvEvBq)
 [![Demo](https://img.shields.io/badge/Demo-blue)](http://vlrlabmonkey.xyz:7685/)
 
 
@@ -35,273 +32,59 @@ MonkeyOCR: Document Parsing with a Structure-Recognition-Relation Triplet Paradi
 ## 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. MonkeyOCR-pro-1.2B surpasses MonkeyOCR-3B by 7.4% on Chinese documents.
-2. MonkeyOCR-pro-1.2B delivers approximately a 36% speed improvement over MonkeyOCR-pro-3B, with approximately 1.6% drop in performance.
-3. On olmOCR-Bench, MonkeyOCR-pro-1.2B outperforms Nanonets-OCR-3B by 7.3%.
-4. 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.
-<a href="https://zimgs.com/i/EKhkhY"><img src="https://v1.ax1x.com/2025/07/15/EKhkhY.png" alt="EKhkhY.png" border="0" /></a>
-
-## Inference Speed (Pages/s) on Different GPUs and [PDF](https://drive.google.com/drive/folders/1geumlJmVY7UUKdr8324sYZ0FHSAElh7m?usp=sharing) Page Counts
-
-<table>
-    <thead>
-		<tr align='center'>
-    		<th>Model</th>
-        	<th>GPU</th>
-        	<th>50 Pages</th>
-        	<th>100 Pages</th>
-        	<th>300 Pages</th>
-        	<th>500 Pages</th>
-        	<th>1000 Pages</th>
-    	</tr>
-    </thead>
-    <tbody>
-    	<tr align='center'>
-    		<td rowspan='4'>MonkeyOCR-pro-3B</td>
-        	<td>3090</td>
-        	<td>0.492</td>
-        	<td>0.484</td>
-        	<td>0.497</td>
-        	<td>0.492</td>
-        	<td>0.496</td>
-    	</tr>
-    	<tr align='center'>
-        	<td>A6000</td>
-        	<td>0.585</td>
-        	<td>0.587</td>
-        	<td>0.609</td>
-        	<td>0.598</td>
-        	<td>0.608</td>
-    	</tr>
-    	<tr align='center'>
-        	<td>H800</td>
-        	<td>0.923</td>
-        	<td>0.768</td>
-        	<td>0.897</td>
-        	<td>0.930</td>
-        	<td>0.891</td>
-    	</tr>
-    	<tr align='center'>
-        	<td>4090</td>
-        	<td>0.972</td>
-        	<td>0.969</td>
-        	<td>1.006</td>
-        	<td>0.986</td>
-        	<td>1.006</td>
-    	</tr>
-    	<tr align='center'>
-    		<td rowspan='4'>MonkeyOCR-pro-1.2B</td>
-        	<td>3090</td>
-        	<td>0.615</td>
-        	<td>0.660</td>
-        	<td>0.677</td>
-        	<td>0.687</td>
-        	<td>0.683</td>
-    	</tr>
-    	<tr align='center'>
-        	<td>A6000</td>
-        	<td>0.709</td>
-        	<td>0.786</td>
-        	<td>0.825</td>
-        	<td>0.829</td>
-        	<td>0.825</td>
-   		</tr>
-    	<tr align='center'>
-        	<td>H800</td>
-        	<td>0.965</td>
-        	<td>1.082</td>
-        	<td>1.101</td>
-        	<td>1.145</td>
-        	<td>1.015</td>
-    	</tr>
-    	<tr align='center'>
-        	<td>4090</td>
-        	<td>1.194</td>
-        	<td>1.314</td>
-        	<td>1.436</td>
-        	<td>1.442</td>
-        	<td>1.434</td>
-    	</tr>
-    </tbody>
-</table>
-
-## VLM OCR Speed (Pages/s) on Different GPUs and [PDF](https://drive.google.com/drive/folders/1geumlJmVY7UUKdr8324sYZ0FHSAElh7m?usp=sharing) Page Counts
-
-<table>
-    <thead>
-		<tr align='center'>
-    		<th>Model</th>
-        	<th>GPU</th>
-        	<th>50 Pages</th>
-        	<th>100 Pages</th>
-        	<th>300 Pages</th>
-        	<th>500 Pages</th>
-        	<th>1000 Pages</th>
-    	</tr>
-    </thead>
-    <tbody>
-    	<tr align='center'>
-    		<td rowspan='4'>MonkeyOCR-pro-3B</td>
-        	<td>3090</td>
-        	<td>0.705</td>
-        	<td>0.680</td>
-        	<td>0.711</td>
-        	<td>0.700</td>
-        	<td>0.724</td>
-    	</tr>
-    	<tr align='center'>
-        	<td>A6000</td>
-        	<td>0.885</td>
-        	<td>0.860</td>
-        	<td>0.915</td>
-        	<td>0.892</td>
-        	<td>0.934</td>
-    	</tr>
-    	<tr align='center'>
-        	<td>H800</td>
-        	<td>1.371</td>
-        	<td>1.135</td>
-        	<td>1.339</td>
-        	<td>1.433</td>
-        	<td>1.509</td>
-    	</tr>
-    	<tr align='center'>
-        	<td>4090</td>
-        	<td>1.321</td>
-        	<td>1.300</td>
-        	<td>1.384</td>
-        	<td>1.343</td>
-        	<td>1.410</td>
-    	</tr>
-    	<tr align='center'>
-    		<td rowspan='4'>MonkeyOCR-pro-1.2B</td>
-        	<td>3090</td>
-        	<td>0.919</td>
-        	<td>1.086</td>
-        	<td>1.166</td>
-        	<td>1.182</td>
-        	<td>1.199</td>
-    	</tr>
-    	<tr align='center'>
-        	<td>A6000</td>
-        	<td>1.177</td>
-        	<td>1.361</td>
-        	<td>1.506</td>
-        	<td>1.525</td>
-        	<td>1.569</td>
-   		</tr>
-    	<tr align='center'>
-        	<td>H800</td>
-        	<td>1.466</td>
-        	<td>1.719</td>
-        	<td>1.763</td>
-        	<td>1.875</td>
-        	<td>1.650</td>
-    	</tr>
-    	<tr align='center'>
-        	<td>4090</td>
-        	<td>1.759</td>
-        	<td>1.987</td>
-        	<td>2.260</td>
-        	<td>2.345</td>
-        	<td>2.415</td>
-    	</tr>
-    </tbody>
-</table>
-
+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).
 
-## 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](https://github.com/Yuliang-Liu/MonkeyOCR/issues/90), [H200](https://github.com/Yuliang-Liu/MonkeyOCR/issues/151), [L20](https://github.com/Yuliang-Liu/MonkeyOCR/issues/133), [V100](https://github.com/Yuliang-Liu/MonkeyOCR/issues/144), [2080 Ti](https://github.com/Yuliang-Liu/MonkeyOCR/pull/1) and [npu](https://github.com/Yuliang-Liu/MonkeyOCR/pull/226/files).
+<img src="https://v1.ax1x.com/2025/06/05/7jQ3cm.png" alt="7jQ3cm.png" border="0" />
 
+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.
 
 ## News 
-* ```2025.07.10 ``` 🚀 We release [MonkeyOCR-pro-1.2B](https://huggingface.co/echo840/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](https://huggingface.co/models?sort=trending). Thanks for the love!
-* ```2025.06.05 ``` 🚀 We release [MonkeyOCR](https://huggingface.co/echo840/MonkeyOCR), an English and Chinese documents parsing model.
+* ```2025.06.05 ``` 🚀 We release MonkeyOCR, which supports the parsing of various types of Chinese and English documents.
 
 
-# Quick Start
-## Locally Install
+## Quick Start
+
 ### 1. Install MonkeyOCR
-See the [installation guide](https://github.com/Yuliang-Liu/MonkeyOCR/blob/main/docs/install_cuda_pp.md#install-with-cuda-support) to set up your environment.
+```bash
+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 -e .
+```
 ### 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
+python tools/download_model.py
 ```
 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
+python tools/download_model.py -t modelscope
 ```
 ### 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
-```
-
-<details>
-<summary><b>More usage examples</b></summary>
-
 ```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
+# Make sure in MonkeyOCR directory
+python parse.py path/to/your.pdf
+# or with image as input
+pyhton parse.py path/to/your/image
+# Specify output path and model configs path
+python parse.py path/to/your.pdf -o ./output -c config.yaml
 ```
 
-</details>
-
-<details>
-<summary><b>Output Results</b></summary>
-
-MonkeyOCR mainly generates three types of output files:
+#### Output Results
+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.
@@ -312,882 +95,562 @@ MonkeyOCR mainly generates three types of output files:
 
 These files provide both the final formatted output and detailed intermediate results for further analysis or processing.
 
-</details>
-
 ### 4. Gradio Demo
 ```bash
-python demo/demo_gradio.py
+# Prepare your env for gradio
+pip install gradio==5.23.3
+pip install pdf2image==1.17.0
 ```
-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:
 ```bash
-uvicorn api.main:app --port 8000
+# Start demo
+python demo/demo_gradio.py
 ```
-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
+### Fix **shared memory error** on **RTX 3090 / 4090 / ...** GPUs (Optional)
 
-1. Navigate to the `docker` directory:
-
-   ```bash
-   cd docker
-   ```
+Our 3B model runs efficiently on NVIDIA RTX 3090. However, when using **LMDeploy** as the inference backend, you may encounter compatibility issues on **RTX 3090 / 4090** GPUs — particularly the following error:
 
-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:
+```
+triton.runtime.errors.OutOfResources: out of resource: shared memory
+```
 
-   ```bash
-   bash env.sh
-   ```
+To work around this issue, you can apply the patch below:
 
-3. Build the Docker image:
+```bash
+python tools/lmdeploy_patcher.py patch
+```
 
-   ```bash
-   docker compose build monkeyocr
-   ```
+> ⚠️ **Note:** This command will modify LMDeploy's source code in your environment.
+> To revert the changes, simply run:
 
-> [!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:
->
-> ```bash
-> docker compose build monkeyocr-fix
-> ```
->
-> Otherwise, you may encounter the following error: `triton.runtime.errors.OutOfResources: out of resource: shared memory`
+```bash
+python tools/lmdeploy_patcher.py restore
+```
 
-4. Run the container with the Gradio demo (accessible on port 7860):
+**Special thanks to [@pineking](https://github.com/pineking) for the solution!**
 
-   ```bash
-   docker compose up monkeyocr-demo
-   ```
-
-   Alternatively, start an interactive development environment:
+### Switch inference backend (Optional)
 
-   ```bash
-   docker compose run --rm monkeyocr-dev
-   ```
+You can switch inference backend to `transformers` following the steps below:
 
-5. Run the FastAPI service (accessible on port 7861):
+1. Install required dependency (if not already installed):
    ```bash
-   docker compose up monkeyocr-api
+   # install flash attention 2, you can download the corresponding version from https://github.com/Dao-AILab/flash-attention/releases/
+   pip install flash-attn==2.7.4.post1 --no-build-isolation
    ```
-   Once the API service is running, you can access the API documentation at http://localhost:7861/docs to explore available endpoints.
+2. Open the `model_configs.yaml` file
+3. Set `chat_config.backend` to `transformers`
+4. Adjust the `batch_size` according to your GPU's memory capacity to ensure stable performance
 
-## Windows Support 
+Example configuration:
 
-See the [windows support guide](docs/windows_support.md) for details.
-
-## Quantization
+```yaml
+chat_config:
+  backend: transformers
+  batch_size: 10  # Adjust based on your available GPU memory
+```
 
-This model can be quantized using AWQ. Follow the instructions in the [quantization guide](docs/Quantization.md).
 
 ## 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.
 
-<table>
-<thead>
-<tr>
-<th rowspan="2"><strong>Model<br>Type</strong></th>
-<th rowspan="2"><strong>Methods</strong></th>
-<th colspan="2"><strong>Overall<sup>Edit</sup>↓</strong></th>
-<th colspan="2"><strong>Text<sup>Edit</sup>↓</strong></th>
-<th colspan="2"><strong>Formula<sup>Edit</sup>↓</strong></th>
-<th colspan="2"><strong>Table<sup>TEDS</sup>↑</strong></th>
-<th colspan="2"><strong>Table<sup>Edit</sup>↓</strong></th>
-<th colspan="2"><strong>Read Order<sup>Edit</sup>↓</strong></th>
-</tr>
-<tr>
-<th><em>EN</em></th>
-<th><em>ZH</em></th>
-<th><em>EN</em></th>
-<th><em>ZH</em></th>
-<th><em>EN</em></th>
-<th><em>ZH</em></th>
-<th><em>EN</em></th>
-<th><em>ZH</em></th>
-<th><em>EN</em></th>
-<th><em>ZH</em></th>
-<th><em>EN</em></th>
-<th><em>ZH</em></th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td rowspan="8"><strong>Pipeline<br>Tools</strong></td>
-<td>MinerU</td>
-<td>0.150</td>
-<td>0.357</td>
-<td>0.061</td>
-<td>0.215</td>
-<td>0.278</td>
-<td>0.577</td>
-<td>78.6</td>
-<td>62.1</td>
-<td>0.180</td>
-<td>0.344</td>
-<td>0.079</td>
-<td>0.292</td>
-</tr>
-<tr>
-<td>Marker</td>
-<td>0.336</td>
-<td>0.556</td>
-<td>0.080</td>
-<td>0.315</td>
-<td>0.530</td>
-<td>0.883</td>
-<td>67.6</td>
-<td>49.2</td>
-<td>0.619</td>
-<td>0.685</td>
-<td>0.114</td>
-<td>0.340</td>
-</tr>
-<tr>
-<td>Mathpix</td>
-<td>0.191</td>
-<td>0.365</td>
-<td>0.105</td>
-<td>0.384</td>
-<td>0.306</td>
-<td><strong>0.454</strong></td>
-<td>77.0</td>
-<td>67.1</td>
-<td>0.243</td>
-<td>0.320</td>
-<td>0.108</td>
-<td>0.304</td>
-</tr>
-<tr>
-<td>Docling</td>
-<td>0.589</td>
-<td>0.909</td>
-<td>0.416</td>
-<td>0.987</td>
-<td>0.999</td>
-<td>1</td>
-<td>61.3</td>
-<td>25.0</td>
-<td>0.627</td>
-<td>0.810</td>
-<td>0.313</td>
-<td>0.837</td>
-</tr>
-<tr>
-<td>Pix2Text</td>
-<td>0.320</td>
-<td>0.528</td>
-<td>0.138</td>
-<td>0.356</td>
-<td>0.276</td>
-<td>0.611</td>
-<td>73.6</td>
-<td>66.2</td>
-<td>0.584</td>
-<td>0.645</td>
-<td>0.281</td>
-<td>0.499</td>
-</tr>
-<tr>
-<td>Unstructured</td>
-<td>0.586</td>
-<td>0.716</td>
-<td>0.198</td>
-<td>0.481</td>
-<td>0.999</td>
-<td>1</td>
-<td>0</td>
-<td>0.06</td>
-<td>1</td>
-<td>0.998</td>
-<td>0.145</td>
-<td>0.387</td>
-</tr>
-<tr>
-<td>OpenParse</td>
-<td>0.646</td>
-<td>0.814</td>
-<td>0.681</td>
-<td>0.974</td>
-<td>0.996</td>
-<td>1</td>
-<td>64.8</td>
-<td>27.5</td>
-<td>0.284</td>
-<td>0.639</td>
-<td>0.595</td>
-<td>0.641</td>
-</tr>
-<tr>
-<td>PP-StructureV3</td>
-<td>0.145</td>
-<td><strong>0.206</strong></td>
-<td>0.058</td>
-<td><strong>0.088</strong></td>
-<td>0.295</td>
-<td>0.535</td>
-<td>-</td>
-<td>-</td>
-<td>0.159</td>
-<td><strong>0.109</strong></td>
-<td><strong>0.069</strong></td>
-<td><strong>0.091</strong></td>
-</tr>
-<tr>
-<td rowspan="8"><strong>Expert<br>VLMs</strong></td>
-<td>GOT-OCR</td>
-<td>0.287</td>
-<td>0.411</td>
-<td>0.189</td>
-<td>0.315</td>
-<td>0.360</td>
-<td>0.528</td>
-<td>53.2</td>
-<td>47.2</td>
-<td>0.459</td>
-<td>0.520</td>
-<td>0.141</td>
-<td>0.280</td>
-</tr>
-<tr>
-<td>Nougat</td>
-<td>0.452</td>
-<td>0.973</td>
-<td>0.365</td>
-<td>0.998</td>
-<td>0.488</td>
-<td>0.941</td>
-<td>39.9</td>
-<td>0</td>
-<td>0.572</td>
-<td>1.000</td>
-<td>0.382</td>
-<td>0.954</td>
-</tr>
-<tr>
-<td>Mistral OCR</td>
-<td>0.268</td>
-<td>0.439</td>
-<td>0.072</td>
-<td>0.325</td>
-<td>0.318</td>
-<td>0.495</td>
-<td>75.8</td>
-<td>63.6</td>
-<td>0.600</td>
-<td>0.650</td>
-<td>0.083</td>
-<td>0.284</td>
-</tr>
-<tr>
-<td>OLMOCR-sglang</td>
-<td>0.326</td>
-<td>0.469</td>
-<td>0.097</td>
-<td>0.293</td>
-<td>0.455</td>
-<td>0.655</td>
-<td>68.1</td>
-<td>61.3</td>
-<td>0.608<td>0.652</td>
-<td>0.145</td>
-<td>0.277</td>
-</tr>
-<tr>
-<td>SmolDocling-256M</td>
-<td>0.493</td>
-<td>0.816</td>
-<td>0.262</td>
-<td>0.838</td>
-<td>0.753</td>
-<td>0.997</td>
-<td>44.9</td>
-<td>16.5</td>
-<td>0.729</td>
-<td>0.907</td>
-<td>0.227</td>
-<td>0.522</td>
-</tr>
-<tr>
-<td>Dolphin</td>
-<td>0.206</td>
-<td>0.306</td>
-<td>0.107</td>
-<td>0.197</td>
-<td>0.447</td>
-<td>0.580</td>
-<td>77.3</td>
-<td>67.2</td>
-<td>0.180</td>
-<td>0.285</td>
-<td>0.091</td>
-<td>0.162</td>
-</tr>
-<tr>
-<td>MinerU 2</td>
-<td>0.139</td>
-<td>0.240</td>
-<td><strong>0.047</strong></td>
-<td>0.109</td>
-<td>0.297</td>
-<td>0.536</td>
-<td><strong>82.5</strong></td>
-<td>79.0</td>
-<td>0.141</td>
-<td>0.195</td>
-<td><strong>0.069</strong></td>
-<td>0.118</td>
-</tr>
-<tr>
-<td>OCRFlux</td>
-	
-<td>0.195</td>
-<td>0.281</td>
-<td>0.064</td>
-<td>0.183</td>
-<td>0.379</td>
-<td>0.613</td>
-<td>71.6</td>
-<td>81.3</td>
-<td>0.253</td>
-<td>0.139</td>
-<td>0.086</td>
-<td>0.187</td>
-
-
-</tr>
-<tr>
-<td rowspan="3"><strong>General<br>VLMs</strong></td>
-<td>GPT4o</td>
-<td>0.233</td>
-<td>0.399</td>
-<td>0.144</td>
-<td>0.409</td>
-<td>0.425</td>
-<td>0.606</td>
-<td>72.0</td>
-<td>62.9</td>
-<td>0.234</td>
-<td>0.329</td>
-<td>0.128</td>
-<td>0.251</td>
-</tr>
-<tr>
-<td>Qwen2.5-VL-7B</td>
-<td>0.312</td>
-<td>0.406</td>
-<td>0.157</td>
-<td>0.228</td>
-<td>0.351</td>
-<td>0.574</td>
-<td>76.4</td>
-<td>72.2</td>
-<td>0.588</td>
-<td>0.619</td>
-<td>0.149</td>
-<td>0.203</td>
-</tr>
-<tr>
-<td>InternVL3-8B</td>
-<td>0.314</td>
-<td>0.383</td>
-<td>0.134</td>
-<td>0.218</td>
-<td>0.417</td>
-<td>0.563</td>
-<td>66.1</td>
-<td>73.1</td>
-<td>0.586</td>
-<td>0.564</td>
-<td>0.118</td>
-<td>0.186</td>
-</tr>
-<tr>
-<td rowspan="4"><strong>Mix</strong></td>
-<td><strong>MonkeyOCR-3B <a href="https://huggingface.co/echo840/MonkeyOCR/blob/main/Structure/doclayout_yolo_docstructbench_imgsz1280_2501.pt">[Weight]</a></strong></td>
-<td>0.140</td>
-<td>0.297</td>
-<td>0.058</td>
-<td>0.185</td>
-<td>0.238</td>
-<td>0.506</td>
-<td>80.2</td>
-<td>77.7</td>
-<td>0.170</td>
-<td>0.253</td>
-<td>0.093</td>
-<td>0.244</td>
-</tr>
-<tr>
-<td><strong>MonkeyOCR-3B* <a href="https://huggingface.co/echo840/MonkeyOCR/blob/main/Structure/layout_zh.pt">[Weight]</a></strong></td>
-<td>0.154</td>
-<td>0.277</td>
-<td>0.073</td>
-<td>0.134</td>
-<td>0.255</td>
-<td>0.529</td>
-<td>78.2</td>
-<td>76.2</td>
-<td>0.182</td>
-<td>0.262</td>
-<td>0.105</td>
-<td>0.183</td>
-</tr>
-<tr>
-<td><strong>MonkeyOCR-pro-3B <a href="https://huggingface.co/echo840/MonkeyOCR-pro-3B">[Weight]</a></strong></td>
-<td><strong>0.138</strong></td>
-<td><strong>0.206</strong></td>
-<td>0.067</td>
-<td>0.107</td>
-<td><strong>0.246</strong></td>
-<td><strong>0.421</strong></td>
-<td>81.5</td>
-<td><strong>87.5</strong></td>
-<td><strong>0.139</strong></td>
-<td>0.111</td>
-<td>0.100</td>
-<td>0.185</td>
-</tr>
-<tr>
-<td><strong>MonkeyOCR-pro-1.2B <a href="https://huggingface.co/echo840/MonkeyOCR-pro-1.2B">[Weight]</a></strong></td>
-<td>0.153</td>
-<td>0.223</td>
-<td>0.066</td>
-<td>0.123</td>
-<td>0.272</td>
-<td>0.449</td>
-<td>76.5</td>
-<td>83.7</td>
-<td>0.176</td>
-<td>0.131</td>
-<td>0.097</td>
-<td>0.187</td>
-</tr>
-</tbody>
+<table style="width:100%; border-collapse:collapse; text-align:center;" border="0">
+  <thead>
+    <tr>
+      <th rowspan="2">Model Type</th>
+      <th rowspan="2">Methods</th>
+      <th colspan="2">Overall Edit↓</th>
+      <th colspan="2">Text Edit↓</th>
+      <th colspan="2">Formula Edit↓</th>
+      <th colspan="2">Formula CDM↑</th>
+      <th colspan="2">Table TEDS↑</th>
+      <th colspan="2">Table Edit↓</th>
+      <th colspan="2">Read Order Edit↓</th>
+    </tr>
+    <tr>
+      <th>EN</th>
+      <th>ZH</th>
+      <th>EN</th>
+      <th>ZH</th>
+      <th>EN</th>
+      <th>ZH</th>
+      <th>EN</th>
+      <th>ZH</th>
+      <th>EN</th>
+      <th>ZH</th>
+      <th>EN</th>
+      <th>ZH</th>
+      <th>EN</th>
+      <th>ZH</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td rowspan="7">Pipeline Tools</td>
+      <td>MinerU</td>
+      <td>0.150</td>
+      <td>0.357</td>
+      <td>0.061</td>
+      <td>0.215</td>
+      <td>0.278</td>
+      <td>0.577</td>
+      <td>57.3</td>
+      <td>42.9</td>
+      <td>78.6</td>
+      <td>62.1</td>
+      <td>0.180</td>
+      <td>0.344</td>
+      <td><strong>0.079</strong></td>
+      <td>0.292</td>
+    </tr>
+    <tr>
+      <td>Marker</td>
+      <td>0.336</td>
+      <td>0.556</td>
+      <td>0.080</td>
+      <td>0.315</td>
+      <td>0.530</td>
+      <td>0.883</td>
+      <td>17.6</td>
+      <td>11.7</td>
+      <td>67.6</td>
+      <td>49.2</td>
+      <td>0.619</td>
+      <td>0.685</td>
+      <td>0.114</td>
+      <td>0.340</td>
+    </tr>
+    <tr>
+      <td>Mathpix</td>
+      <td>0.191</td>
+      <td>0.365</td>
+      <td>0.105</td>
+      <td>0.384</td>
+      <td>0.306</td>
+      <td><strong>0.454</strong></td>
+      <td>62.7</td>
+      <td><strong>62.1</strong></td>
+      <td>77.0</td>
+      <td>67.1</td>
+      <td>0.243</td>
+      <td>0.320</td>
+      <td>0.108</td>
+      <td>0.304</td>
+    </tr>
+    <tr>
+      <td>Docling</td>
+      <td>0.589</td>
+      <td>0.909</td>
+      <td>0.416</td>
+      <td>0.987</td>
+      <td>0.999</td>
+      <td>1</td>
+      <td>-</td>
+      <td>-</td>
+      <td>61.3</td>
+      <td>25.0</td>
+      <td>0.627</td>
+      <td>0.810</td>
+      <td>0.313</td>
+      <td>0.837</td>
+    </tr>
+    <tr>
+      <td>Pix2Text</td>
+      <td>0.320</td>
+      <td>0.528</td>
+      <td>0.138</td>
+      <td>0.356</td>
+      <td>0.276</td>
+      <td>0.611</td>
+      <td>78.4</td>
+      <td>39.6</td>
+      <td>73.6</td>
+      <td>66.2</td>
+      <td>0.584</td>
+      <td>0.645</td>
+      <td>0.281</td>
+      <td>0.499</td>
+    </tr>
+    <tr>
+      <td>Unstructured</td>
+      <td>0.586</td>
+      <td>0.716</td>
+      <td>0.198</td>
+      <td>0.481</td>
+      <td>0.999</td>
+      <td>1</td>
+      <td>-</td>
+      <td>-</td>
+      <td>0</td>
+      <td>0.06</td>
+      <td>1</td>
+      <td>0.998</td>
+      <td>0.145</td>
+      <td>0.387</td>
+    </tr>
+    <tr>
+      <td>OpenParse</td>
+      <td>0.646</td>
+      <td>0.814</td>
+      <td>0.681</td>
+      <td>0.974</td>
+      <td>0.996</td>
+      <td>1</td>
+      <td>0.11</td>
+      <td>0</td>
+      <td>64.8</td>
+      <td>27.5</td>
+      <td>0.284</td>
+      <td>0.639</td>
+      <td>0.595</td>
+      <td>0.641</td>
+    </tr>
+    <tr>
+      <td rowspan="5">Expert VLMs</td>
+      <td>GOT-OCR</td>
+      <td>0.287</td>
+      <td>0.411</td>
+      <td>0.189</td>
+      <td>0.315</td>
+      <td>0.360</td>
+      <td>0.528</td>
+      <td>74.3</td>
+      <td>45.3</td>
+      <td>53.2</td>
+      <td>47.2</td>
+      <td>0.459</td>
+      <td>0.520</td>
+      <td>0.141</td>
+      <td>0.280</td>
+    </tr>
+    <tr>
+      <td>Nougat</td>
+      <td>0.452</td>
+      <td>0.973</td>
+      <td>0.365</td>
+      <td>0.998</td>
+      <td>0.488</td>
+      <td>0.941</td>
+      <td>15.1</td>
+      <td>16.8</td>
+      <td>39.9</td>
+      <td>0</td>
+      <td>0.572</td>
+      <td>1.000</td>
+      <td>0.382</td>
+      <td>0.954</td>
+    </tr>
+    <tr>
+      <td>Mistral OCR</td>
+      <td>0.268</td>
+      <td>0.439</td>
+      <td>0.072</td>
+      <td>0.325</td>
+      <td>0.318</td>
+      <td>0.495</td>
+      <td>64.6</td>
+      <td>45.9</td>
+      <td>75.8</td>
+      <td>63.6</td>
+      <td>0.600</td>
+      <td>0.650</td>
+      <td>0.083</td>
+      <td>0.284</td>
+    </tr>
+    <tr>
+      <td>OLMOCR-sglang</td>
+      <td>0.326</td>
+      <td>0.469</td>
+      <td>0.097</td>
+      <td>0.293</td>
+      <td>0.455</td>
+      <td>0.655</td>
+      <td>74.3</td>
+      <td>43.2</td>
+      <td>68.1</td>
+      <td>61.3</td>
+      <td>0.608</td>
+      <td>0.652</td>
+      <td>0.145</td>
+      <td>0.277</td>
+    </tr>
+    <tr>
+      <td>SmolDocling-256M</td>
+      <td>0.493</td>
+      <td>0.816</td>
+      <td>0.262</td>
+      <td>0.838</td>
+      <td>0.753</td>
+      <td>0.997</td>
+      <td>32.1</td>
+      <td>0.55</td>
+      <td>44.9</td>
+      <td>16.5</td>
+      <td>0.729</td>
+      <td>0.907</td>
+      <td>0.227</td>
+      <td>0.522</td>
+    </tr>
+    <tr>
+      <td rowspan="3">General VLMs</td>
+      <td>GPT4o</td>
+      <td>0.233</td>
+      <td>0.399</td>
+      <td>0.144</td>
+      <td>0.409</td>
+      <td>0.425</td>
+      <td>0.606</td>
+      <td>72.8</td>
+      <td>42.8</td>
+      <td>72.0</td>
+      <td>62.9</td>
+      <td>0.234</td>
+      <td>0.329</td>
+      <td>0.128</td>
+      <td>0.251</td>
+    </tr>
+    <tr>
+      <td>Qwen2.5-VL-7B</td>
+      <td>0.312</td>
+      <td>0.406</td>
+      <td>0.157</td>
+      <td>0.228</td>
+      <td>0.351</td>
+      <td>0.574</td>
+      <td><strong>79.0</strong></td>
+      <td>50.2</td>
+      <td>76.4</td>
+      <td>72.2</td>
+      <td>0.588</td>
+      <td>0.619</td>
+      <td>0.149</td>
+      <td>0.203</td>
+    </tr>
+    <tr>
+      <td>InternVL3-8B</td>
+      <td>0.314</td>
+      <td>0.383</td>
+      <td>0.134</td>
+      <td>0.218</td>
+      <td>0.417</td>
+      <td>0.563</td>
+      <td>78.3</td>
+      <td>49.3</td>
+      <td>66.1</td>
+      <td>73.1</td>
+      <td>0.586</td>
+      <td>0.564</td>
+      <td>0.118</td>
+      <td>0.186</td>
+    </tr>
+    <tr>
+      <td rowspan="2">Mix</td>
+      <td>MonkeyOCR-3B <a href="https://huggingface.co/echo840/MonkeyOCR/blob/main/Structure/doclayout_yolo_docstructbench_imgsz1280_2501.pt">[Weight]</a></td>
+      <td><strong>0.140</strong></td>
+      <td>0.297</td>
+      <td><strong>0.058</strong></td>
+      <td>0.185</td>
+      <td><strong>0.238</strong></td>
+      <td>0.506</td>
+      <td>78.7</td>
+      <td>51.4</td>
+      <td><strong>80.2</strong></td>
+      <td><strong>77.7</strong></td>
+      <td><strong>0.170</strong></td>
+      <td><strong>0.253</strong></td>
+      <td>0.093</td>
+      <td>0.244</td>
+    </tr>
+    <tr>
+      <td>MonkeyOCR-3B* <a href="https://huggingface.co/echo840/MonkeyOCR/blob/main/Structure/layout_zh.pt">[Weight]</a></td>
+      <td>0.154</td>
+      <td><strong>0.277</strong></td>
+      <td>0.073</td>
+      <td><strong>0.134</strong></td>
+      <td>0.255</td>
+      <td>0.529</td>
+      <td>78.5</td>
+      <td>50.8</td>
+      <td>78.2</td>
+      <td>76.2</td>
+      <td>0.182</td>
+      <td>0.262</td>
+      <td>0.105</td>
+      <td><strong>0.183</strong></td>
+    </tr>
+  </tbody>
 </table>
 
 
-### 2. The end-to-end text recognition performance across 9 PDF page types.
 
-<table>
-<thead>
-<tr>
-<th><strong>Model<br>Type</strong></th>
-<th><strong>Models</strong></th>
-<th><strong>Book</strong></th>
-<th><strong>Slides</strong></th>
-<th><strong>Financial<br>Report</strong></th>
-<th><strong>Textbook</strong></th>
-<th><strong>Exam<br>Paper</strong></th>
-<th><strong>Magazine</strong></th>
-<th><strong>Academic<br>Papers</strong></th>
-<th><strong>Notes</strong></th>
-<th><strong>Newspaper</strong></th>
-<th><strong>Overall</strong></th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td rowspan="3"><strong>Pipeline<br>Tools</strong></td>
-<td>MinerU</td>
-<td>0.055</td>
-<td>0.124</td>
-<td><u>0.033</u></td>
-<td>0.102</td>
-<td>0.159</td>
-<td><strong>0.072</strong></td>
-<td><u>0.025</u></td>
-<td>0.984</td>
-<td>0.171</td>
-<td>0.206</td>
-</tr>
-<tr>
-<td>Marker</td>
-<td>0.074</td>
-<td>0.340</td>
-<td>0.089</td>
-<td>0.319</td>
-<td>0.452</td>
-<td>0.153</td>
-<td>0.059</td>
-<td>0.651</td>
-<td>0.192</td>
-<td>0.274</td>
-</tr>
-<tr>
-<td>Mathpix</td>
-<td>0.131</td>
-<td>0.220</td>
-<td>0.202</td>
-<td>0.216</td>
-<td>0.278</td>
-<td>0.147</td>
-<td>0.091</td>
-<td>0.634</td>
-<td>0.690</td>
-<td>0.300</td>
-</tr>
-<tr>
-<td rowspan="4"><strong>Expert<br>VLMs</strong></td>
-<td>GOT-OCR</td>
-<td>0.111</td>
-<td>0.222</td>
-<td>0.067</td>
-<td>0.132</td>
-<td>0.204</td>
-<td>0.198</td>
-<td>0.179</td>
-<td>0.388</td>
-<td>0.771</td>
-<td>0.267</td>
-</tr>
-<tr>
-<td>Nougat</td>
-<td>0.734</td>
-<td>0.958</td>
-<td>1.000</td>
-<td>0.820</td>
-<td>0.930</td>
-<td>0.830</td>
-<td>0.214</td>
-<td>0.991</td>
-<td>0.871</td>
-<td>0.806</td>
-</tr>
-<tr>
-<td>Dolphin</td>
-<td>0.091</td>
-<td>0.131</td>
-<td>0.057</td>
-<td>0.146</td>
-<td>0.231</td>
-<td>0.121</td>
-<td>0.074</td>
-<td>0.363</td>
-<td>0.307</td>
-<td>0.177</td>
-</tr>
-<tr>
-<td>OCRFlux</td>
-<td>0.068</td>
-<td>0.125</td>
-<td>0.092</td>
-<td>0.102</td>
-<td>0.119</td>
-<td>0.083</td>
-<td>0.047</td>
-<td>0.223</td>
-<td>0.536</td>
-<td>0.149</td>
-</tr>
-<tr>
-<td rowspan="3"><strong>General<br>VLMs</strong></td>
-<td>GPT4o</td>
-<td>0.157</td>
-<td>0.163</td>
-<td>0.348</td>
-<td>0.187</td>
-<td>0.281</td>
-<td>0.173</td>
-<td>0.146</td>
-<td>0.607</td>
-<td>0.751</td>
-<td>0.316</td>
-</tr>
-<tr>
-<td>Qwen2.5-VL-7B</td>
-<td>0.148</td>
-<td><strong>0.053</strong></td>
-<td>0.111</td>
-<td>0.137</td>
-<td>0.189</td>
-<td>0.117</td>
-<td>0.134</td>
-<td>0.204</td>
-<td>0.706</td>
-<td>0.205</td>
-</tr>
-<tr>
-<td>InternVL3-8B</td>
-<td>0.163</td>
-<td><u>0.056</u></td>
-<td>0.107</td>
-<td>0.109</td>
-<td>0.129</td>
-<td>0.100</td>
-<td>0.159</td>
-<td><strong>0.150</strong></td>
-<td>0.681</td>
-<td>0.188</td>
-</tr>
-<tr>
-<td rowspan="4"><strong>Mix</strong></td>
-<td><strong>MonkeyOCR-3B <a href="https://huggingface.co/echo840/MonkeyOCR/blob/main/Structure/doclayout_yolo_docstructbench_imgsz1280_2501.pt">[Weight]</a></strong></td>
-<td><strong>0.046</strong></td>
-<td>0.120</td>
-<td><strong>0.024</strong></td>
-<td>0.100</td>
-<td>0.129</td>
-<td>0.086</td>
-<td><strong>0.024</strong></td>
-<td>0.643</td>
-<td><u>0.131</u></td>
-<td>0.155</td>
-</tr>
-<tr>
-<td><strong>MonkeyOCR-3B* <a href="https://huggingface.co/echo840/MonkeyOCR/blob/main/Structure/layout_zh.pt">[Weight]</a></strong></td>
-<td><u>0.054</u></td>
-<td>0.203</td>
-<td>0.038</td>
-<td>0.112</td>
-<td>0.138</td>
-<td>0.111</td>
-<td>0.032</td>
-<td>0.194</td>
-<td>0.136</td>
-<td>0.120</td>
-</tr>
-<tr>
-<td><strong>MonkeyOCR-pro-3B <a href="https://huggingface.co/echo840/MonkeyOCR-pro-3B">[Weight]</a></strong></td>
-<td>0.084</td>
-<td>0.129</td>
-<td>0.060</td>
-<td><strong>0.090</strong></td>
-<td><strong>0.107</strong></td>
-<td><u>0.073</u></td>
-<td>0.050</td>
-<td><u>0.171</u></td>
-<td><strong>0.107</strong></td>
-<td><strong>0.100</strong></td>
-</tr>
-<tr>
-<td><strong>MonkeyOCR-pro-1.2B <a href="https://huggingface.co/echo840/MonkeyOCR-pro-1.2B">[Weight]</a></strong></td>
-<td>0.087</td>
-<td>0.142</td>
-<td>0.059</td>
-<td><u>0.093</u></td>
-<td><u>0.115</u></td>
-<td>0.085</td>
-<td>0.045</td>
-<td>0.226</td>
-<td>0.122</td>
-<td><u>0.112</u></td>
-</tr>
-</tbody>
-</table>
 
-### 3. The evaluation results of olmOCR-bench.
-
-<table>
-<thead>
-<tr>
-<th>Model</th>
-<th>ArXiv</th>
-<th>Old Scans<br>Math</th>
-<th>Tables</th>
-<th>Old Scans</th>
-<th>Headers and<br>Footers</th>
-<th>Multi<br>column</th>
-<th>Long Tiny<br>Text</th>
-<th>Base</th>
-<th>Overall</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td>GOT OCR</td>
-<td>52.7</td>
-<td>52.0</td>
-<td>0.2</td>
-<td>22.1</td>
-<td>93.6</td>
-<td>42.0</td>
-<td>29.9</td>
-<td>94.0</td>
-<td>48.3 ± 1.1</td>
-</tr>
-<tr>
-<td>Marker</td>
-<td>76.0</td>
-<td>57.9</td>
-<td>57.6</td>
-<td>27.8</td>
-<td>84.9</td>
-<td>72.9</td>
-<td>84.6</td>
-<td><strong>99.1</strong></td>
-<td>70.1 ± 1.1</td>
-</tr>
-<tr>
-<td>MinerU</td>
-<td>75.4</td>
-<td>47.4</td>
-<td>60.9</td>
-<td>17.3</td>
-<td><strong>96.6</strong></td>
-<td>59.0</td>
-<td>39.1</td>
-<td>96.6</td>
-<td>61.5 ± 1.1</td>
-</tr>
-<tr>
-<td>Mistral OCR</td>
-<td>77.2</td>
-<td>67.5</td>
-<td>60.6</td>
-<td>29.3</td>
-<td>93.6</td>
-<td>71.3</td>
-<td>77.1</td>
-<td>99.4</td>
-<td>72.0 ± 1.1</td>
-</tr>
-<tr>
-<td>Nanonets OCR</td>
-<td>67.0</td>
-<td>68.6</td>
-<td><strong>77.7</strong></td>
-<td>39.5</td>
-<td>40.7</td>
-<td>69.9</td>
-<td>53.4</td>
-<td>99.3</td>
-<td>64.5 ± 1.1</td>
-</tr>
-<tr>
-<td>GPT-4o<br>(No Anchor)</td>
-<td>51.5</td>
-<td><strong>75.5</strong></td>
-<td>69.1</td>
-<td>40.9</td>
-<td>94.2</td>
-<td>68.9</td>
-<td>54.1</td>
-<td>96.7</td>
-<td>68.9 ± 1.1</td>
-</tr>
-<tr>
-<td>GPT-4o<br>(Anchored)</td>
-<td>53.5</td>
-<td>74.5</td>
-<td>70.0</td>
-<td>40.7</td>
-<td>93.8</td>
-<td>69.3</td>
-<td>60.6</td>
-<td>96.8</td>
-<td>69.9 ± 1.1</td>
-</tr>
-<tr>
-<td>Gemini Flash 2<br>(No Anchor)</td>
-<td>32.1</td>
-<td>56.3</td>
-<td>61.4</td>
-<td>27.8</td>
-<td>48.0</td>
-<td>58.7</td>
-<td><strong>84.4</strong></td>
-<td>94.0</td>
-<td>57.8 ± 1.1</td>
-</tr>
-<tr>
-<td>Gemini Flash 2<br>(Anchored)</td>
-<td>54.5</td>
-<td>56.1</td>
-<td>72.1</td>
-<td>34.2</td>
-<td>64.7</td>
-<td>61.5</td>
-<td>71.5</td>
-<td>95.6</td>
-<td>63.8 ± 1.2</td>
-</tr>
-<tr>
-<td>Qwen 2 VL<br>(No Anchor)</td>
-<td>19.7</td>
-<td>31.7</td>
-<td>24.2</td>
-<td>17.1</td>
-<td>88.9</td>
-<td>8.3</td>
-<td>6.8</td>
-<td>55.5</td>
-<td>31.5 ± 0.9</td>
-</tr>
-<tr>
-<td>Qwen 2.5 VL<br>(No Anchor)</td>
-<td>63.1</td>
-<td>65.7</td>
-<td>67.3</td>
-<td>38.6</td>
-<td>73.6</td>
-<td>68.3</td>
-<td>49.1</td>
-<td>98.3</td>
-<td>65.5 ± 1.2</td>
-</tr>
-<tr>
-<td>olmOCR v0.1.75<br>(No Anchor)</td>
-<td>71.5</td>
-<td>71.4</td>
-<td>71.4</td>
-<td><strong>42.8</strong></td>
-<td>94.1</td>
-<td>77.7</td>
-<td>71.0</td>
-<td>97.8</td>
-<td>74.7 ± 1.1</td>
-</tr>
-<tr>
-<td>olmOCR v0.1.75<br>(Anchored)</td>
-<td>74.9</td>
-<td>71.2</td>
-<td>71.0</td>
-<td>42.2</td>
-<td>94.5</td>
-<td><strong>78.3</strong></td>
-<td>73.3</td>
-<td>98.3</td>
-<td>75.5 ± 1.0</td>
-</tr>
-<tr>
-<td>MonkeyOCR-pro-3B <a href="https://huggingface.co/echo840/MonkeyOCR-pro-3B">[Weight]</a></td>
-<td><strong>83.8</strong></td>
-<td>68.8</td>
-<td>74.6</td>
-<td>36.1</td>
-<td>91.2</td>
-<td>76.6</td>
-<td>80.1</td>
-<td>95.3</td>
-<td><strong>75.8 ± 1.0</strong></td>
-</tr>
-<tr>
-<td>MonkeyOCR-pro-1.2B <a href="https://huggingface.co/echo840/MonkeyOCR-pro-1.2B">[Weight]</a></td>
-<td>80.5</td>
-<td>62.9</td>
-<td>71.1</td>
-<td>32.9</td>
-<td>92.2</td>
-<td>68.3</td>
-<td>74.0</td>
-<td>92.6</td>
-<td>71.8 ± 1.1</td>
-</tr>
-</tbody>
+### 2. The end-to-end text recognition performance across 9 PDF page types.
+<table style="width: 100%; border-collapse: collapse; text-align: center;">
+  <thead>
+    <tr style="border-bottom: 2px solid #000;">
+      <th><b>Model Type</b></th>
+      <th><b>Models</b></th>
+      <th><b>Book</b></th>
+      <th><b>Slides</b></th>
+      <th><b>Financial Report</b></th>
+      <th><b>Textbook</b></th>
+      <th><b>Exam Paper</b></th>
+      <th><b>Magazine</b></th>
+      <th><b>Academic Papers</b></th>
+      <th><b>Notes</b></th>
+      <th><b>Newspaper</b></th>
+      <th><b>Overall</b></th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td rowspan="3"><b>Pipeline Tools</b></td>
+      <td>MinerU</td>
+      <td><u>0.055</u></td>
+      <td>0.124</td>
+      <td><u>0.033</u></td>
+      <td><u>0.102</u></td>
+      <td><u>0.159</u></td>
+      <td><b>0.072</b></td>
+      <td><u>0.025</u></td>
+      <td>0.984</td>
+      <td>0.171</td>
+      <td>0.206</td>
+    </tr>
+    <tr>
+      <td>Marker</td>
+      <td>0.074</td>
+      <td>0.340</td>
+      <td>0.089</td>
+      <td>0.319</td>
+      <td>0.452</td>
+      <td>0.153</td>
+      <td>0.059</td>
+      <td>0.651</td>
+      <td>0.192</td>
+      <td>0.274</td>
+    </tr>
+    <tr>
+      <td>Mathpix</td>
+      <td>0.131</td>
+      <td>0.220</td>
+      <td>0.202</td>
+      <td>0.216</td>
+      <td>0.278</td>
+      <td>0.147</td>
+      <td>0.091</td>
+      <td>0.634</td>
+      <td>0.690</td>
+      <td>0.300</td>
+    </tr>
+    <tr>
+      <td rowspan="2"><b>Expert VLMs</b></td>
+      <td>GOT-OCR</td>
+      <td>0.111</td>
+      <td>0.222</td>
+      <td>0.067</td>
+      <td>0.132</td>
+      <td>0.204</td>
+      <td>0.198</td>
+      <td>0.179</td>
+      <td>0.388</td>
+      <td>0.771</td>
+      <td>0.267</td>
+    </tr>
+    <tr>
+      <td>Nougat</td>
+      <td>0.734</td>
+      <td>0.958</td>
+      <td>1.000</td>
+      <td>0.820</td>
+      <td>0.930</td>
+      <td>0.830</td>
+      <td>0.214</td>
+      <td>0.991</td>
+      <td>0.871</td>
+      <td>0.806</td>
+    </tr>
+    <tr>
+      <td rowspan="3"><b>General VLMs</b></td>
+      <td>GPT4o</td>
+      <td>0.157</td>
+      <td>0.163</td>
+      <td>0.348</td>
+      <td>0.187</td>
+      <td>0.281</td>
+      <td>0.173</td>
+      <td>0.146</td>
+      <td>0.607</td>
+      <td>0.751</td>
+      <td>0.316</td>
+    </tr>
+    <tr>
+      <td>Qwen2.5-VL-7B</td>
+      <td>0.148</td>
+      <td><b>0.053</b></td>
+      <td>0.111</td>
+      <td>0.137</td>
+      <td>0.189</td>
+      <td>0.117</td>
+      <td>0.134</td>
+      <td>0.204</td>
+      <td>0.706</td>
+      <td>0.205</td>
+    </tr>
+    <tr>
+      <td>InternVL3-8B</td>
+      <td>0.163</td>
+      <td><u>0.056</u></td>
+      <td>0.107</td>
+      <td>0.109</td>
+      <td><b>0.129</b></td>
+      <td>0.100</td>
+      <td>0.159</td>
+      <td><b>0.150</b></td>
+      <td>0.681</td>
+      <td>0.188</td>
+    </tr>
+    <tr>
+      <td rowspan="2"><b>Mix</b></td>
+      <td>MonkeyOCR-3B <a href="https://huggingface.co/echo840/MonkeyOCR/blob/main/Structure/doclayout_yolo_docstructbench_imgsz1280_2501.pt">[Weight]</a></td>
+      <td><b>0.046</b></td>
+      <td>0.120</td>
+      <td><b>0.024</b></td>
+      <td><b>0.100</b></td>
+      <td><b>0.129</b></td>
+      <td><u>0.086</u></td>
+      <td><b>0.024</b></td>
+      <td>0.643</td>
+      <td><b>0.131</b></td>
+      <td><u>0.155</u></td>
+    </tr>
+    <tr>
+      <td>MonkeyOCR-3B* <a href="https://huggingface.co/echo840/MonkeyOCR/blob/main/Structure/layout_zh.pt">[Weight]</a></td>
+      <td>0.054</td>
+      <td>0.203</td>
+      <td>0.038</td>
+      <td>0.112</td>
+      <td>0.138</td>
+      <td>0.111</td>
+      <td>0.032</td>
+      <td><u>0.194</u></td>
+      <td><u>0.136</u></td>
+      <td><b>0.120</b></td>
+    </tr>
+  </tbody>
 </table>
 
+### 3. Comparing MonkeyOCR with closed-source and extra large open-source VLMs.
+<img src="https://v1.ax1x.com/2025/06/05/7jQlj4.png" alt="7jQlj4.png" border="0" />
+
+
 ## Visualization Demo
 
-Get a Quick Hands-On Experience with Our Demo:  http://vlrlabmonkey.xyz:7685 (The latest model is available for selection)
+Get a Quick Hands-On Experience with Our Demo:  http://vlrlabmonkey.xyz:7685
 
 > Our demo is simple and easy to use:
 >
@@ -1230,10 +693,8 @@ If you wish to refer to the baseline results published here, please use the foll
 
 
 ## Acknowledgments
-We would like to thank [MinerU](https://github.com/opendatalab/MinerU), [DocLayout-YOLO](https://github.com/opendatalab/DocLayout-YOLO), [PyMuPDF](https://github.com/pymupdf/PyMuPDF), [layoutreader](https://github.com/ppaanngggg/layoutreader), [Qwen2.5-VL](https://github.com/QwenLM/Qwen2.5-VL), [LMDeploy](https://github.com/InternLM/lmdeploy), [PP-StructureV3](https://github.com/PaddlePaddle/PaddleOCR), [PP-DocLayout_plus-L](https://huggingface.co/PaddlePaddle/PP-DocLayout_plus-L), and [InternVL3](https://github.com/OpenGVLab/InternVL) for providing base code and models, as well as their contributions to this field. We also thank [M6Doc](https://github.com/HCIILAB/M6Doc), [DocLayNet](https://github.com/DS4SD/DocLayNet), [CDLA](https://github.com/buptlihang/CDLA), [D4LA](https://github.com/AlibabaResearch/AdvancedLiterateMachinery), [DocGenome](https://github.com/Alpha-Innovator/DocGenome), [PubTabNet](https://github.com/ibm-aur-nlp/PubTabNet), and [UniMER-1M](https://github.com/opendatalab/UniMERNet) for providing valuable datasets. We also thank everyone who contributed to this open-source effort.
+We would like to thank [MinerU](https://github.com/opendatalab/MinerU), [DocLayout-YOLO](https://github.com/opendatalab/DocLayout-YOLO), [PyMuPDF](https://github.com/pymupdf/PyMuPDF), [layoutreader](https://github.com/ppaanngggg/layoutreader), [Qwen2.5-VL](https://github.com/QwenLM/Qwen2.5-VL), [LMDeploy](https://github.com/InternLM/lmdeploy), and [InternVL3](https://github.com/OpenGVLab/InternVL) for providing base code and models, as well as their contributions to this field. We also thank [M6Doc](https://github.com/HCIILAB/M6Doc), [DocLayNet](https://github.com/DS4SD/DocLayNet), [CDLA](https://github.com/buptlihang/CDLA), [D4LA](https://github.com/AlibabaResearch/AdvancedLiterateMachinery), [DocGenome](https://github.com/Alpha-Innovator/DocGenome), [PubTabNet](https://github.com/ibm-aur-nlp/PubTabNet), and [UniMER-1M](https://github.com/opendatalab/UniMERNet) for providing valuable datasets.
 
-## 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.
+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.

Recognition/config.json

@@ -1,5 +1,5 @@
 {
-  "_name_or_path": "MonkeyOCR",
+  "_name_or_path": "/home/kas/train_data_all/models/qwen2_5vl_3b/Qwen2.5-VL",
   "architectures": [
     "Qwen2_5_VLForConditionalGeneration"
   ],