zenosai
/

MonkeyOCRv2-AS

 ---
+license: apache-2.0
+datasets:
+- zenosai/MonkeyDocv2
+pipeline_tag: image-text-to-text
 ---
+<div align="center" xmlns="http://www.w3.org/1999/html">
+<h2>
+<b>MonkeyOCRv2: A Visual-Text Foundation Model for Document AI</b>
+</h2>
+[![arXiv](https://img.shields.io/badge/Arxiv-MonkeyOCRv2-b31b1b.svg?logo=arXiv)](https://arxiv.org/abs/2607.11562)
+[![MonkeyOCRv2](https://img.shields.io/badge/MonkeyOCRv2-black.svg?logo=Huggingface)](https://huggingface.co/collections/zenosai/monkeyocrv2)
+[![MonkeyDocv2](https://img.shields.io/badge/MonkeyDoc_v2-blue.svg?logo=ModelScope)](https://modelscope.cn/datasets/zenosai/MonkeyDocv2)
+[![GitHub issues](https://img.shields.io/github/issues/Yuliang-Liu/MonkeyOCRv2?color=critical&label=Issues)](https://github.com/Yuliang-Liu/MonkeyOCRv2/issues?q=is%3Aopen+is%3Aissue)
+[![GitHub closed issues](https://img.shields.io/github/issues-closed/Yuliang-Liu/MonkeyOCRv2?color=success&label=Issues)](https://github.com/Yuliang-Liu/MonkeyOCRv2/issues?q=is%3Aissue+is%3Aclosed)
+[![Demo](https://img.shields.io/badge/Demo-white.svg)](http://vlrlabmonkey.xyz:8891/)
+<img src="https://raw.githubusercontent.com/Yuliang-Liu/MonkeyOCRv2/refs/heads/main/asserts/overview.png" width="600"/>
+</div>
+## News
+* `2026.07.11` 🚀 We release [MonkeyOCRv2](https://github.com/Yuliang-Liu/MonkeyOCRv2), including MonkeyOCRv2 vision encoder, MonkeyOCRv2-Parsing for multilingual document parsing, MonkeyOCRv2-Und for efficient document understanding.
+## Introduction
+MonkeyOCRv2 is a text-centric visual foundation model that unifies fine-grained text modeling, cross-task representation learning, and cross-lingual generalization in a single encoder. MonkeyOCRv2 generalizes effectively across a broad range of OCR and document intelligence tasks, including multilingual document parsing, document understanding, text recognition, formula recognition, document tampering detection, scene text detection, and overlapping text segmentation.
+## Model Zoo
+#### 1. Vision Encoder
+<table>
+  <thead>
+    <tr>
+      <th>Model</th>
+      <th>Backbone</th>
+      <th>Params</th>
+      <th>Pretraining<br>Resolution</th>
+      <th>Applicable Tasks</th>
+      <th>Checkpoint Link</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>Monkey<br>OCRv2-S</td><td>ViT-S</td><td>28M</td><td>1280*28*28</td><td>Recognition / Parsing / Understanding</td><td><a href="https://huggingface.co/zenosai/MonkeyOCRv2-S">🤗HuggingFace</a><br><a href="https://modelscope.cn/models/zenosai/MonkeyOCRv2-S">🤖ModelScope</a></td>
+    </tr>
+    <tr>
+      <td>Monkey<br>OCRv2-B</td><td>ViT-B</td><td>113M</td><td>1280*28*28</td><td>Recognition / Parsing / Understanding</td><td><a href="https://huggingface.co/zenosai/MonkeyOCRv2-B">🤗HuggingFace</a><br><a href="https://modelscope.cn/models/zenosai/MonkeyOCRv2-B">🤖ModelScope</a></td>
+    </tr>
+    <tr>
+      <td>Monkey<br>OCRv2-AS</td><td>ViTAEv2-S</td><td>21M</td><td>1760*32*32</td><td>Detection / Segmentation</td><td><a href="https://huggingface.co/zenosai/MonkeyOCRv2-AS">🤗HuggingFace</a><br><a href="https://modelscope.cn/models/zenosai/MonkeyOCRv2-AS">🤖ModelScope</a></td>
+    </tr>
+  </tbody>
+</table>
+#### 2. Document Parsing Model
+<table>
+  <thead>
+    <tr>
+      <th>Model</th>
+      <th>Link</th>
+      <th>Total Params</th>
+      <th>ViT</th>
+      <th>LLM</th>
+      <th>All</th>
+      <th>Digit.</th>
+      <th>Photo.</th>
+      <th>Latin Avg.</th>
+      <th>DE</th>
+      <th>EN</th>
+      <th>ES</th>
+      <th>FR</th>
+      <th>ID</th>
+      <th>IT</th>
+      <th>NL</th>
+      <th>PT</th>
+      <th>VI</th>
+      <th>Non-Latin Avg.</th>
+      <th>AR</th>
+      <th>HI</th>
+      <th>JP</th>
+      <th>KO</th>
+      <th>RU</th>
+      <th>TH</th>
+      <th>ZH</th>
+      <th>ZH-T</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>MonkeyOCRv2-S-Parsing</td><td><a href="https://huggingface.co/zenosai/MonkeyOCRv2-S-Parsing">HuggingFace</a> <a href="https://modelscope.cn/models/zenosai/MonkeyOCRv2-S-Parsing">ModelScope</a></td>
+      <td>0.6B</td>
+      <td>0.03B</td>
+      <td>0.6B</td>
+      <td>82.5</td>
+      <td>87.9</td>
+      <td>80.7</td>
+      <td>83.2</td>
+      <td>87.3</td>
+      <td>83.6</td>
+      <td>76.8</td>
+      <td>73.6</td>
+      <td>85.4</td>
+      <td>87.2</td>
+      <td>85.5</td>
+      <td>87.4</td>
+      <td>81.9</td>
+      <td>81.7</td>
+      <td>91.2</td>
+      <td>87.1</td>
+      <td>69.9</td>
+      <td>88.7</td>
+      <td>78.0</td>
+      <td>79.8</td>
+      <td>84.4</td>
+      <td>74.7</td>
+    </tr>
+    <tr>
+      <td>MonkeyOCRv2-B-Parsing</td><td><a href="https://huggingface.co/zenosai/MonkeyOCRv2-B-Parsing">HuggingFace</a> <a href="https://modelscope.cn/models/zenosai/MonkeyOCRv2-B-Parsing">ModelScope</a></td>
+      <td>0.7B</td>
+      <td>0.1B</td>
+      <td>0.6B</td>
+      <td>83.3</td>
+      <td>88.1</td>
+      <td>81.7</td>
+      <td>84.2</td>
+      <td>87.7</td>
+      <td>84.5</td>
+      <td>75.2</td>
+      <td>78.4</td>
+      <td>86.5</td>
+      <td>88.6</td>
+      <td>86.1</td>
+      <td>87.9</td>
+      <td>83.2</td>
+      <td>82.1</td>
+      <td>90.7</td>
+      <td>87.2</td>
+      <td>71.9</td>
+      <td>87.6</td>
+      <td>80.1</td>
+      <td><strong>80.8</strong></td>
+      <td>83.6</td>
+      <td>75.3</td>
+    </tr>
+  </tbody>
+</table>
+#### 3. Document Understanding Model
+<table>
+  <thead>
+    <tr>
+      <th>Model</th>
+      <th>Link</th>
+      <th>Total Params</th>
+      <th>Overall</th>
+      <th>DocVQA</th>
+      <th>InfoVQA</th>
+      <th>DF</th>
+      <th>KLC</th>
+      <th>WTQ</th>
+      <th>ChartQA</th>
+      <th>DT-VQA</th>
+      <th>OCRBench</th>
+    </tr>
+  </thead>
+    <tr>
+      <td>MonkeyOCRv2-S-Und</td><td><a href="https://huggingface.co/zenosai/MonkeyOCRv2-S-Und">HuggingFace</a> <a href="https://modelscope.cn/models/zenosai/MonkeyOCRv2-S-Und">ModelScope</a></td>
+      <td>1.7B</td>
+      <td>55.9</td>
+      <td>79.3</td>
+      <td>44.5</td>
+      <td>65.1</td>
+      <td>37.6</td>
+      <td>43.0</td>
+      <td>62.0</td>
+      <td>63.1</td>
+      <td>52.2</td>
+    </tr>
+    <tr>
+      <td>MonkeyOCRv2-B-Und</td><td><a href="https://huggingface.co/zenosai/MonkeyOCRv2-B-Und">HuggingFace</a> <a href="https://modelscope.cn/models/zenosai/MonkeyOCRv2-B-Und">ModelScope</a></td>
+      <td>1.8B</td>
+      <td>57.2</td>
+      <td>79.3</td>
+      <td>46.3</td>
+      <td>65.8</td>
+      <td>38.2</td>
+      <td>43.2</td>
+      <td>62.0</td>
+      <td>64.3</td>
+      <td>58.1</td>
+    </tr>
+  </tbody>
+</table>
+## Quick Start
+### Vision Encoder
+#### 1. Install
+Install transformers and flash attention:
+```bash
+conda create -n MonkeyOCRv2 python=3.10
+conda activate MonkeyOCRv2
+pip install torch==2.6.0 torchvision==0.21.0 torchaudio==2.6.0 --index-url https://download.pytorch.org/whl/cu126
+pip install transformers==4.57.6
+pip install flash-attn==2.7.4.post1 --no-build-isolation
+pip install accelerate
+pip install qwen_vl_utils
+```
+#### 2. Download Model Weights
+Download our model from Huggingface.
+```bash
+python download_model.py -n MonkeyOCRv2-B # or MonkeyOCRv2-S / MonkeyOCRv2-AS
+```
+You can also download our model from ModelScope.
+```bash
+pip install modelscope
+python download_model.py -t modelscope -n MonkeyOCRv2-B # or MonkeyOCRv2-S / MonkeyOCRv2-AS
+```
+#### 3. Extract Image Feature
+```bash
+cd vision
+# For MonkeyOCRv2-B and MonkeyOCRv2-S
+python extract_feature.py
+# For MonkeyOCRv2-AS
+python extract_feature_vitae.py
+```
+### Document Parsing
+#### 1. Install
+Install vLLM following its [official guide](https://docs.vllm.ai/en/v0.11.2/getting_started/installation/gpu/):
+```bash
+conda create -n MonkeyOCRv2Parsing python=3.10
+conda activate MonkeyOCRv2Parsing
+pip install uv
+uv pip install vllm==0.11.2 --torch-backend=auto -i https://pypi.tuna.tsinghua.edu.cn/simple requests
+pip install -r parsing/requirements.txt
+```
+#### 2. Download Model Weights
+Download our model from Huggingface.
+```bash
+python download_model.py -n MonkeyOCRv2-B-Parsing # or MonkeyOCRv2-S-Parsing
+```
+You can also download our model from ModelScope.
+```bash
+pip install modelscope
+python download_model.py -t modelscope -n MonkeyOCRv2-B-Parsing # or MonkeyOCRv2-S-Parsing
+```
+#### 3. Inference
+Parse a single document or a directory containing PDFs or images:
+```bash
+cd parsing
+python parse.py \
+    -i ../images_test/ar.JPEG \
+    -o output/test \
+    -m ../model_weight/MonkeyOCRv2-B-Parsing \
+    -g 500 \
+    --draw-layout \
+    --skip-processed
+# Show help messages
+python parse.py -h
+```
+#### 4. Web Demo
+Start gradio web demo:
+```bash
+cd parsing
+python demo/gradio_demo.py \
+    --model-path ../model_weight/MonkeyOCRv2-B-Parsing \
+    --output-dir output/demo_outputs
+```
+### Document Understanding
+#### 1. Install
+See install part of MonkeyOCRv2.
+#### 2. Download Model Weights
+Download our model from Huggingface.
+```bash
+python download_model.py -n MonkeyOCRv2-B-Und # or MonkeyOCRv2-S-Und
+```
+You can also download our model from ModelScope.
+```bash
+pip install modelscope
+python download_model.py -t modelscope -n MonkeyOCRv2-B-Und # or MonkeyOCRv2-S-Und
+```
+#### 3. Inference
+```bash
+cd understanding
+python infer.py \
+    -m ../model_weight/MonkeyOCRv2-B-Und \
+    -i ../images_test/vqa.png \
+    -q 'What is the serving size?'
+# Show help messages
+python infer.py -h
+```
+## Visualization
+Our model supports robust document parsing in real-world scenarios across 17 languages, including Simplified Chinese (ZH), Traditional Chinese (ZH-T), English (EN), Arabic (AR), German (DE), Spanish (ES), French (FR), Hindi (HI), Indonesian (ID), Italian (IT), Japanese (JP), Korean (KO), Dutch (NL), Portuguese (PT), Russian (RU), Thai (TH), and Vietnamese (VI).
+<p align="center">
+  <img src="https://github.com/Yuliang-Liu/MonkeyOCRv2/blob/main/asserts/Visualization.gif?raw=true" width="600"/>
+</p>
+## Evaluation Results
+#### 1. Text recognition results on Common Benchmarks, Union14M-Benchmark, OST, and Chinese Benchmarks. We follow the training and evaluation protocols of [OpenOCR](https://github.com/Topdu/OpenOCR/blob/main/docs/svtrv2.md).
+<table>
+  <thead>
+    <tr>
+      <th rowspan="2">Model</th>
+      <th rowspan="2"><strong>Overall</strong></th>
+      <th style="text-align: center;" colspan="8">Union14M-Benchmark</th>
+      <th style="text-align: center;" colspan="5">Chinese Benchmarks</th>
+      <th style="text-align: center;" rowspan="2">Occlusion SceneText</th>
+    </tr>
+    <tr>
+      <th><strong>Avg</th>
+      <th>Artistic</th>
+      <th>Context less</th>
+      <th>Curve</th>
+      <th>General</th>
+      <th>Multi Oriented</th>
+      <th>Multi Words </th>
+      <th>Saliency</th>
+      <th><strong>Avg</th>
+      <th>Scene</th>
+      <th>Web</th>
+      <th>Document</th>
+      <th>Hand writing</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>ABINet</td>
+      <td>73.7</td>
+      <td>75.7</td><td>71.7</td><td>74.7</td><td>80.4</td><td>79.8</td><td>69.0</td><td>76.8</td><td>77.6</td>
+      <td>70.3</td><td>66.6</td><td>63.2</td><td>98.2</td><td>53.1</td>
+      <td>75.0</td>
+    </tr>
+    <tr>
+      <td>MAERec</td>
+      <td>81.6</td>
+      <td>85.2</td><td>79.0</td><td>84.2</td><td>89.1</td><td>84.6</td><td>87.1</td><td>85.9</td><td>86.3</td>
+      <td>83.1</td><td>84.4</td><td>83.0</td><td><b>99.5</b></td><td>65.6</td>
+      <td>76.4</td>
+    </tr>
+    <tr>
+      <td>CPPD</td>
+      <td>80.4</td>
+      <td>81.9</td><td>76.5</td><td>82.9</td><td>86.2</td><td>83.5</td><td>78.7</td><td>81.9</td><td>83.5</td>
+      <td>81.7</td><td>82.7</td><td>82.4</td><td>99.4</td><td>62.3</td>
+      <td>79.6</td>
+    </tr>
+    <tr>
+      <td>IGTR-AR</td>
+      <td>81.0</td>
+      <td>84.9</td><td>77.0</td><td>82.4</td><td>90.4</td><td>84.4</td><td>91.2</td><td>84.0</td><td>84.7</td>
+      <td>81.7</td><td>82.0</td><td>81.7</td><td><b>99.5</b></td><td>63.8</td>
+      <td>76.3</td>
+    </tr>
+    <tr>
+      <td>SMTR</td>
+      <td>80.4</td>
+      <td>85.0</td><td>76.8</td><td>83.9</td><td>89.1</td><td>83.7</td><td>87.7</td><td><b>89.3</b></td><td>84.6</td>
+      <td>82.7</td><td>83.4</td><td>83.0</td><td>99.3</td><td>65.1</td>
+      <td>73.5</td>
+    </tr>
+    <tr>
+      <td>SVTRv2</td>
+      <td>83.1</td>
+      <td>86.1</td><td><b>79.3</b></td><td>86.1</td><td>90.6</td><td>85.1</td><td>89.0</td><td>86.7</td><td>86.2</td>
+      <td>83.3</td><td>83.5</td><td><b>83.3</b></td><td><b>99.5</b></td><td>67.0</td>
+      <td>80.0</td>
+    </tr>
+    <tr>
+      <td colspan="16">&nbsp;</td>
+    </tr>
+    <tr>
+      <td>CRNN (ResNet)</td>
+      <td>58.7</td>
+      <td>49.2</td><td>51.2</td><td>62.3</td><td>48.1</td><td>68.2</td><td>13.0</td><td>60.4</td><td>41.4</td>
+      <td>68.8</td><td>63.8</td><td>68.2</td><td>97.0</td><td>46.1</td>
+      <td>58.0</td>
+    </tr>
+    <tr>
+      <td>CRNN (MonkeyOCRv2-S)</td>
+      <td>67.3</td>
+      <td>65.2</td><td>63.7</td><td>73.0</td><td>71.1</td><td>74.5</td><td>28.6</td><td>72.1</td><td>73.4</td>
+      <td>74.2</td><td>73.0</td><td>74.9</td><td>96.9</td><td>51.8</td>
+      <td>62.4</td>
+    </tr>
+    <tr>
+      <td>PARSeq (ViT)</td>
+      <td>82.2</td>
+      <td>84.3</td><td>76.5</td><td>83.4</td><td>87.6</td><td>84.9</td><td>88.8</td><td>84.3</td><td>84.4</td>
+      <td>82.4</td><td>84.2</td><td>82.8</td><td><b>99.5</b></td><td>63.0</td>
+      <td>79.9</td>
+    </tr>
+    <tr>
+      <td>PARSeq (MonkeyOCRv2-S)</td>
+      <td><b>84.3</b></td>
+      <td><b>87.6</b></td><td>78.6</td><td><b>86.4</b></td><td><b>92.1</b></td><td><b>85.4</b></td><td><b>93.9</b></td><td>88.7</td><td><b>87.7</b></td>
+      <td><b>83.7</b></td><td><b>84.6</b></td><td>83.2</td><td><b>99.5</b></td><td><b>67.3</b></td>
+      <td><b>81.5</b></td>
+    </tr>
+  </tbody>
+</table>
+#### 2. Formula recognition results on [OmniDocBench 1.6](https://github.com/opendatalab/OmniDocBench), [MathWriting](https://arxiv.org/pdf/2404.10690), and [UniMER-Test](https://github.com/opendatalab/unimernet).
+<table>
+  <thead>
+    <tr>
+      <th rowspan="2">Model</th>
+      <th rowspan="2">Params</th>
+      <th style="text-align: center;" colspan="2">Overall</th>
+      <th style="text-align: center;" colspan="2">OmniDocBench 1.6</th>
+      <th style="text-align: center;" colspan="2">MathWriting</th>
+      <th style="text-align: center;" colspan="2">SPE</th>
+      <th style="text-align: center;" colspan="2">CPE</th>
+      <th style="text-align: center;" colspan="2">HWE</th>
+      <th style="text-align: center;" colspan="2">SCE</th>
+    </tr>
+    <tr>
+      <th>CDM</th>
+      <th>ExpRate</th>
+      <th>CDM</th>
+      <th>ExpRate</th>
+      <th>CDM</th>
+      <th>ExpRate</th>
+      <th>CDM</th>
+      <th>ExpRate</th>
+      <th>CDM</th>
+      <th>ExpRate</th>
+      <th>CDM</th>
+      <th>ExpRate</th>
+      <th>CDM</th>
+      <th>ExpRate</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>Pix2tex</td>
+      <td>25.5M</td>
+      <td>53.8</td>
+      <td>23.3</td>
+      <td>69.4</td><td>27.0</td>
+      <td>0.4</td><td>0.0</td>
+      <td>96.2</td><td>72.4</td>
+      <td>64.9</td><td>7.1</td>
+      <td>24.5</td><td>0.6</td>
+      <td>67.6</td><td>32.8</td>
+    </tr>
+    <tr>
+      <td>Texify</td>
+      <td>312M</td>
+      <td>67.3</td>
+      <td>40.4</td>
+      <td>76.5</td><td>46.4</td>
+      <td>26.6</td><td>2.0</td>
+      <td>98.5</td><td>91.0</td>
+      <td>70.4</td><td>28.2</td>
+      <td>52.7</td><td>23.6</td>
+      <td>79.3</td><td>51.3</td>
+    </tr>
+    <tr>
+      <td>UniMERNet-B</td>
+      <td>325M</td>
+      <td>89.5</td>
+      <td>64.5</td>
+      <td>90.4</td><td>59.5</td>
+      <td>63.8</td><td>12.3</td>
+      <td>99.1</td><td>93.3</td>
+      <td>96.0</td><td><b>80.5</b></td>
+      <td>94.0</td><td>64.3</td>
+      <td>93.7</td><td>77.0</td>
+    </tr>
+    <tr>
+      <td>UniMERNet-S</td>
+      <td>202M</td>
+      <td>89.8</td>
+      <td>64.0</td>
+      <td>90.1</td><td>59.1</td>
+      <td>65.9</td><td>12.7</td>
+      <td>99.1</td><td>93.4</td>
+      <td>95.9</td><td>77.7</td>
+      <td>93.7</td><td>63.9</td>
+      <td><b>94.1</b></td><td>76.9</td>
+    </tr>
+    <tr>
+      <td colspan="16">&nbsp;</td>
+    </tr>
+    <tr>
+      <td>UniMERNet-T (Swin)</td>
+      <td>107M</td>
+      <td>89.4</td>
+      <td>61.8</td>
+      <td>89.9</td><td>57.2</td>
+      <td>65.6</td><td>12.9</td>
+      <td>99.1</td><td>92.3</td>
+      <td>94.9</td><td>69.9</td>
+      <td>93.3</td><td>61.9</td>
+      <td>93.8</td><td>76.6</td>
+    </tr>
+    <tr>
+      <td><b>UniMERNet-T (MonkeyOCRv2-S)</b></td>
+      <td>110M</td>
+      <td><b>90.9</b></td>
+      <td><b>66.4</b></td>
+      <td><b>90.8</b></td><td><b>61.1</b></td>
+      <td><b>70.8</b></td><td><b>16.2</b></td>
+      <td><b>99.2</b></td><td><b>93.8</b></td>
+      <td><b>96.1</b></td><td>79.2</td>
+      <td><b>94.3</b></td><td><b>69.5</b></td>
+      <td>94.0</td><td><b>78.6</b></td>
+    </tr>
+  </tbody>
+</table>
+</table>
+#### 3. Text detection results on Total-Text, CTW1500, ICDAR2015 and ArT. We follow the training and evaluation protocols of [MMOCR](https://github.com/open-mmlab/mmocr) and [DPText-DETR](https://github.com/ymy-k/DPText-DETR).
+<p align="center">
+  <img src="https://raw.githubusercontent.com/Yuliang-Liu/MonkeyOCRv2/refs/heads/main/asserts/overview.png?raw=true" width="600"/>
+</p>
+#### 4. Document tampering detection results on [DocTamper](https://github.com/qcf-568/DocTamper) benchmark.
+<table>
+  <thead>
+    <tr>
+      <th rowspan="2">Method</th>
+      <th rowspan="2">Params</th>
+      <th style="text-align: center;" colspan="2">Overall</th>
+      <th style="text-align: center;" colspan="4">DocTamper-Test</th>
+      <th style="text-align: center;" colspan="4">DocTamper-FCD</th>
+      <th style="text-align: center;" colspan="4">DocTamper-SCD</th>
+    </tr>
+    <tr>
+      <th style="text-align: center;">IoU</th>
+      <th style="text-align: center;">F</th>
+      <th style="text-align: center;">IoU</th>
+      <th style="text-align: center;">P</th>
+      <th style="text-align: center;">R</th>
+      <th style="text-align: center;">F</th>
+      <th style="text-align: center;">IoU</th>
+      <th style="text-align: center;">P</th>
+      <th style="text-align: center;">R</th>
+      <th style="text-align: center;">F</th>
+      <th style="text-align: center;">IoU</th>
+      <th style="text-align: center;">P</th>
+      <th style="text-align: center;">R</th>
+      <th style="text-align: center;">F</th>
+    </tr>
+  </thead>
+  <tbody>
+<tr>
+  <td>PSCC-Net</td>
+  <td>5M</td>
+  <td>13.7</td><td>31.3</td><td>17.0</td><td>25.0</td><td>83.0</td><td>39.0</td>
+  <td>13.0</td><td>19.0</td><td>82.0</td><td>30.0</td>
+  <td>11.0</td><td>15.0</td><td><b>83.0</b></td><td>25.0</td>
+</tr>
+<tr>
+  <td>UperNet</td>
+  <td>67M</td>
+  <td>49.3</td><td>54.0</td><td>70.0</td><td>66.0</td><td>60.0</td><td>62.0</td>
+  <td>30.0</td><td>57.0</td><td>35.0</td><td>43.0</td>
+  <td>48.0</td><td>57.0</td><td>58.0</td><td>57.0</td>
+</tr>
+<tr>
+  <td>CAT-Net</td>
+  <td>114M</td>
+  <td>67.3</td><td>71.0</td><td>78.0</td><td>75.0</td><td>69.0</td><td>72.0</td>
+  <td>66.0</td><td>85.0</td><td>70.0</td><td>76.0</td>
+  <td>58.0</td><td>65.0</td><td>65.0</td><td>65.0</td>
+</tr>
+<tr>
+  <td>Swin-UPer</td>
+  <td>81M</td>
+  <td>66.7</td><td>71.7</td><td>79.0</td><td>75.0</td><td>72.0</td><td>73.0</td>
+  <td>64.0</td><td>80.0</td><td>70.0</td><td>75.0</td>
+  <td>57.0</td><td>66.0</td><td>68.0</td><td>67.0</td>
+</tr>
+<tr>
+  <td>SegFormer</td>
+  <td>85M</td>
+  <td>70.3</td><td>74.0</td><td>81.0</td><td>77.0</td><td>74.0</td><td>75.0</td>
+  <td>69.0</td><td>82.0</td><td>74.0</td><td>78.0</td>
+  <td>61.0</td><td>68.0</td><td>70.0</td><td>69.0</td>
+</tr>
+<tr>
+  <td>Mask2Former</td>
+  <td>69M</td>
+  <td>69.7</td><td>78.0</td><td>84.0</td><td>82.0</td><td>83.0</td><td>82.0</td>
+  <td>66.0</td><td>81.0</td><td>75.0</td><td>78.0</td>
+  <td>59.0</td><td>70.0</td><td>79.0</td><td>74.0</td>
+</tr>
+<tr>
+  <td>ConvNext</td>
+  <td>122M</td>
+  <td>69.7</td><td>75.3</td><td>84.0</td><td>81.0</td><td>78.0</td><td>79.0</td>
+  <td>62.0</td><td>76.0</td><td>71.0</td><td>74.0</td>
+  <td>63.0</td><td>71.0</td><td>74.0</td><td>73.0</td>
+</tr>
+<tr>
+  <td>ConvNextV2</td>
+  <td>121M</td>
+  <td>72.7</td><td>77.7</td><td>86.0</td><td>82.0</td><td>79.0</td><td>81.0</td>
+  <td>65.0</td><td>79.0</td><td>75.0</td><td>77.0</td>
+  <td>67.0</td><td>74.0</td><td>76.0</td><td>75.0</td>
+</tr>
+<tr>
+  <td>InternImage</td>
+  <td>128M</td>
+  <td>73.3</td><td>77.7</td><td>84.0</td><td>81.0</td><td>77.0</td><td>79.0</td>
+  <td>72.0</td><td>83.0</td><td>79.0</td><td>81.0</td>
+  <td>64.0</td><td>73.0</td><td>74.0</td><td>73.0</td>
+</tr>
+<tr>
+  <td>ASC-Former</td>
+  <td>80M</td>
+  <td>68.2</td><td>80.8</td><td>81.5</td><td>91.8</td><td>87.8</td><td>89.8</td>
+  <td>61.3</td><td>74.9</td><td>77.1</td><td>76.0</td>
+  <td>61.9</td><td>78.0</td><td>75.0</td><td>76.5</td>
+</tr>
+<tr>
+  <td>DTD</td>
+  <td>66M</td>
+  <td><u>77.0</u></td><td>79.7</td><td>84.0</td><td>81.0</td><td>77.0</td><td>79.0</td>
+  <td>79.0</td><td>88.0</td><td>82.0</td><td>85.0</td>
+  <td><b>68.0</b></td><td>75.0</td><td>76.0</td><td>75.0</td>
+</tr>
+<tr>
+  <td colspan="14">&nbsp;</td>
+</tr>
+<tr>
+  <td>FFDN* (ViTAEv2)</td>
+  <td>69M</td>
+  <td>70.7</td><td><u>82.7</u></td>
+  <td>69.4</td><td>76.2</td><td>88.7</td><td>82.0</td>
+  <td>79.0</td><td><b>92.5</b></td><td>84.4</td><td>88.3</td>
+  <td>63.6</td><td>79.1</td><td>76.5</td><td>77.8</td>
+</tr>
+<tr>
+  <td><b>FFDN (MonkeyOCRv2-AS)</b></td>
+  <td>71M</td>
+  <td><b>78.2</b></td><td><b>87.5</b></td>
+  <td><b>87.4</b></td><td><b>94.8</b></td><td><b>91.8</b></td><td><b>93.3</b></td>
+  <td><b>79.9</b></td><td>90.4</td><td><b>87.4</b></td><td><b>88.9</b></td>
+  <td>67.2</td><td><b>81.0</b></td><td>79.8</td><td><b>80.4</b></td>
+</tr>
+</tbody>
+</table>
+<p><small>* denotes models trained with the ViTAEv2 pretrained by DeepSolo</small></p>
+#### 5. Overlapping text segmentation results on [MOT](https://github.com/willpat1213/MOTS) dataset.
+<table>
+  <thead>
+    <tr>
+      <th>Model</th>
+      <th><b>mIoU<sub>Text</sub></b></th>
+      <th>IoU<sub>Occ</sub></th>
+      <th>IoU<sub>Occd</sub></th>
+      <th>IoU<sub>Ov</sub></th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>Unet</td>
+      <td>62.2</td>
+      <td>80.2</td>
+      <td>65.7</td>
+      <td>40.7</td>
+    </tr>
+    <tr>
+      <td>Deeplab v3</td>
+      <td>67.9</td>
+      <td>83.2</td>
+      <td>71.2</td>
+      <td>49.3</td>
+    </tr>
+    <tr>
+      <td>OCRNet</td>
+      <td>65.8</td>
+      <td>81.0</td>
+      <td>68.5</td>
+      <td>47.8</td>
+    </tr>
+    <tr>
+      <td>Segformer</td>
+      <td>69.0</td>
+      <td>83.6</td>
+      <td>74.1</td>
+      <td>49.3</td>
+    </tr>
+    <tr>
+      <td>MaskFormer</td>
+      <td>68.4</td>
+      <td>83.5</td>
+      <td>70.3</td>
+      <td>51.4</td>
+    </tr>
+    <tr>
+      <td>TexRNet</td>
+      <td>68.9</td>
+      <td>84.2</td>
+      <td>73.2</td>
+      <td>49.3</td>
+    </tr>
+    <tr>
+      <td>EAFormer</td>
+      <td>69.1</td>
+      <td>83.8</td>
+      <td>74.2</td>
+      <td>50.5</td>
+    </tr>
+    <tr>
+      <td>WASNet</td>
+      <td>70.8</td>
+      <td>84.8</td>
+      <td>74.4</td>
+      <td>53.1</td>
+    </tr>
+    <tr>
+      <td colspan="5">&nbsp;</td>
+    </tr>
+    <tr>
+      <td>Mask2Former (ResNet)</td>
+      <td>70.3</td>
+      <td>84.7</td>
+      <td>73.3</td>
+      <td>52.8</td>
+    </tr>
+    <tr>
+      <td><b>Mask2Former (MonkeyOCRv2-AS)</b></td>
+      <td><u>76.6</u></td>
+      <td><b>88.6</b></td>
+      <td><b>83.4</b></td>
+      <td>57.7</td>
+    </tr>
+    <tr>
+      <td>MOTS (ResNet)</td>
+      <td>72.6</td>
+      <td>85.2</td>
+      <td>77.5</td>
+      <td>54.9</td>
+    </tr>
+    <tr>
+      <td><b>MOTS (MonkeyOCRv2-AS)</b></td>
+      <td><b>76.9</b></td>
+      <td><b>88.6</b></td>
+      <td><u>82.6</u></td>
+      <td><b>59.4</b></td>
+    </tr>
+  </tbody>
+</table>
+#### 6. Document parsing results on [MDPBench](https://github.com/Yuliang-Liu/MultimodalOCR/tree/main/MDPBench), a comprehensive multilingual benchmark for real-world document parsing.
+<table>
+  <thead>
+    <tr>
+      <th>Model</th>
+      <th>Total Params</th>
+      <th>ViT</th>
+      <th>LLM</th>
+      <th>All</th>
+      <th>Digit.</th>
+      <th>Photo.</th>
+      <th>Latin Avg.</th>
+      <th>DE</th>
+      <th>EN</th>
+      <th>ES</th>
+      <th>FR</th>
+      <th>ID</th>
+      <th>IT</th>
+      <th>NL</th>
+      <th>PT</th>
+      <th>VI</th>
+      <th>Non-Latin Avg.</th>
+      <th>AR</th>
+      <th>HI</th>
+      <th>JP</th>
+      <th>KO</th>
+      <th>RU</th>
+      <th>TH</th>
+      <th>ZH</th>
+      <th>ZH-T</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+        <td colspan="25" class="section">
+            <strong>Closed-source VLMs</strong>
+        </td>
+    </tr>
+    <tr>
+      <td>ChatGPT-5.2-2025-12-11</td>
+      <td>-</td>
+      <td>-</td>
+      <td>-</td>
+      <td>68.6</td>
+      <td>85.6</td>
+      <td>63.0</td>
+      <td>75.2</td>
+      <td>70.8</td>
+      <td>79.4</td>
+      <td>71.4</td>
+      <td>60.0</td>
+      <td>77.7</td>
+      <td>78.5</td>
+      <td>71.6</td>
+      <td>85.0</td>
+      <td>82.1</td>
+      <td>61.1</td>
+      <td>64.9</td>
+      <td>63.4</td>
+      <td>55.8</td>
+      <td>65.4</td>
+      <td>60.7</td>
+      <td>63.8</td>
+      <td>56.3</td>
+      <td>58.7</td>
+    </tr>
+    <tr>
+      <td>Claude-Sonnet-4.6</td>
+      <td>-</td>
+      <td>-</td>
+      <td>-</td>
+      <td>73.1</td>
+      <td>85.0</td>
+      <td>69.3</td>
+      <td>79.2</td>
+      <td>79.8</td>
+      <td>80.6</td>
+      <td>72.8</td>
+      <td>66.5</td>
+      <td>82.3</td>
+      <td>83.3</td>
+      <td>76.7</td>
+      <td>88.0</td>
+      <td>83.1</td>
+      <td>66.2</td>
+      <td>67.8</td>
+      <td>71.7</td>
+      <td>63.4</td>
+      <td>64.3</td>
+      <td>70.8</td>
+      <td>65.2</td>
+      <td>61.3</td>
+      <td>65.1</td>
+    </tr>
+    <tr>
+      <td>Doubao-2.0-pro</td>
+      <td>-</td>
+      <td>-</td>
+      <td>-</td>
+      <td>74.2</td>
+      <td>78.9</td>
+      <td>72.8</td>
+      <td>75.7</td>
+      <td>82.8</td>
+      <td>74.4</td>
+      <td>69.0</td>
+      <td>70.0</td>
+      <td>73.3</td>
+      <td>82.0</td>
+      <td>69.9</td>
+      <td>83.4</td>
+      <td>76.5</td>
+      <td>72.5</td>
+      <td>81.3</td>
+      <td>75.7</td>
+      <td>65.8</td>
+      <td>74.7</td>
+      <td>63.3</td>
+      <td>71.9</td>
+      <td>71.9</td>
+      <td>75.2</td>
+    </tr>
+    <tr>
+      <td>Gemini-3-pro</td>
+      <td>-</td>
+      <td>-</td>
+      <td>-</td>
+      <td><strong>86.4</strong></td>
+      <td><strong>90.4</strong></td>
+      <td><strong>85.1</strong></td>
+      <td><strong>88.4</strong></td>
+      <td><strong>91.2</strong></td>
+      <td><strong>90.6</strong></td>
+      <td><strong>83.4</strong></td>
+      <td><strong>82.7</strong></td>
+      <td><strong>91.5</strong></td>
+      <td><strong>91.6</strong></td>
+      <td><strong>87.7</strong></td>
+      <td><strong>91.4</strong></td>
+      <td><strong>85.9<strong></td>
+      <td><strong>84.1</strong></td>
+      <td><strong>89.4<strong></td>
+      <td><strong>90.4</strong></td>
+      <td><strong>74.8<strong></td>
+      <td><strong>85.5<strong></td>
+      <td><strong>84.9</strong></td>
+      <td><strong>80.6<strong></td>
+      <td><strong>85.1</strong></td>
+      <td><strong>82.1</strong></td>
+    </tr>
+    <tr>
+        <td colspan="25" class="section">
+            <strong>Open-source VLMs</strong>
+        </td>
+    </tr>
+    <tr>
+<td>InternVL-3.5-8B</td><td>8.3B</td><td>0.3B</td><td>8B</td><td>42.7</td><td>59.7</td><td>37.0</td><td>53.4</td><td>39.8</td><td>64.2</td><td>47.5</td><td>42.7</td><td>53.8</td><td>60.6</td><td>52.2</td><td>63.2</td><td>57.0</td><td>30.6</td><td>8.2</td><td>9.0</td><td>45.6</td><td>30.3</td><td>26.1</td><td>10.8</td><td>55.3</td><td>59.3</td>
+</tr>
+<tr>
+<td>MinerU-2.5</td><td>1.2B</td><td>0.7B</td><td>0.5B</td><td>46.3</td><td>61.9</td><td>40.8</td><td>63.0</td><td>68.8</td><td>78.4</td><td>54.7</td><td>57.3</td><td>67.5</td><td>75.2</td><td>60.4</td><td>58.8</td><td>46.0</td><td>27.4</td><td>1.3</td><td>9.0</td><td>39.1</td><td>14.7</td><td>8.6</td><td>11.3</td><td>72.9</td><td>62.2</td>
+</tr>
+<tr>
+<td>DeepSeek-OCR</td><td>3.4B</td><td>0.4B</td><td>3B</td><td>51.8</td><td>80.7</td><td>42.2</td><td>54.5</td><td>55.0</td><td>58.3</td><td>44.1</td><td>43.2</td><td>60.9</td><td>69.3</td><td>52.4</td><td>53.0</td><td>54.1</td><td>48.9</td><td>56.9</td><td>52.2</td><td>49.1</td><td>28.2</td><td>36.2</td><td>49.4</td><td>59.7</td><td>59.2</td>
+</tr>
+<tr>
+<td>MonkeyOCR-pro-3B</td><td>3.7B</td><td>0.7B</td><td>3B</td><td>52.2</td><td>68.0</td><td>47.0</td><td>65.1</td><td>71.7</td><td>77.9</td><td>55.9</td><td>62.1</td><td>66.2</td><td>74.5</td><td>66.3</td><td>71.1</td><td>40.2</td><td>37.6</td><td>4.6</td><td>4.2</td><td>55.2</td><td>60.5</td><td>42.6</td><td>9.1</td><td>72.2</td><td>52.4</td>
+</tr>
+<tr>
+<td>Nanonets-OCR-s</td><td>4.7B</td><td>0.7B</td><td>4B</td><td>63.7</td><td>78.8</td><td>58.7</td><td>71.3</td><td>75.1</td><td>78.5</td><td>61.2</td><td>62.5</td><td>70.3</td><td>81.0</td><td>69.6</td><td>75.9</td><td>67.5</td><td>55.0</td><td>59.5</td><td>61.8</td><td>55.9</td><td>51.2</td><td>43.5</td><td>39.5</td><td>67.4</td><td>61.5</td>
+</tr>
+<tr>
+<td>Nanonets-OCR2-3B</td><td>3.7B</td><td>0.7B</td><td>3B</td><td>64.2</td><td>79.2</td><td>59.3</td><td>71.4</td><td>76.7</td><td>76.4</td><td>61.8</td><td>66.1</td><td>68.4</td><td>78.5</td><td>74.1</td><td>74.2</td><td>66.0</td><td>56.2</td><td>60.2</td><td>59.2</td><td>52.1</td><td>54.7</td><td>45.5</td><td>44.6</td><td>68.3</td><td>65.1</td>
+</tr>
+<tr>
+<td>Qwen3.5-Instruct-9B</td><td>9.7B</td><td>0.7B</td><td>9B</td><td>65.7</td><td>74.8</td><td>62.7</td><td>72.5</td><td>72.8</td><td>72.0</td><td>72.0</td><td>64.4</td><td>66.2</td><td>77.6</td><td>74.5</td><td>79.1</td><td>74.0</td><td>58.2</td><td>53.4</td><td>56.2</td><td>55.7</td><td>60.3</td><td>54.7</td><td>56.7</td><td>60.8</td><td>67.5</td>
+</tr>
+<tr>
+<td>GLM-OCR</td><td>0.9B</td><td>0.4B</td><td>0.5B</td><td>67.3</td><td>77.9</td><td>63.7</td><td>78.7</td><td>82.7</td><td>84.5</td><td><u>75.8</u></td><td>76.2</td><td>79.7</td><td>82.8</td><td>80.2</td><td>77.4</td><td>69.2</td><td>54.3</td><td>21.7</td><td>39.6</td><td>65.5</td><td>61.2</td><td>64.2</td><td>27.4</td><td>78.5</td><td>76.7</td>
+</tr>
+<tr>
+<td>Qwen3-VL-Instruct-8B</td><td>8.3B</td><td>0.3B</td><td>8B</td><td>68.3</td><td>78.4</td><td>65.0</td><td>73.6</td><td>73.7</td><td>71.4</td><td>69.3</td><td>66.2</td><td>68.5</td><td>79.1</td><td>78.3</td><td>82.2</td><td>73.4</td><td>62.5</td><td>63.1</td><td>58.4</td><td>59.9</td><td>61.9</td><td>57.9</td><td>62.0</td><td>62.6</td><td>73.8</td>
+</tr>
+<tr>
+<td>HunyuanOCR</td><td>1B</td><td>0.4B</td><td>0.6B</td><td>68.3</td><td>80.2</td><td>64.3</td><td>72.4</td><td>75.0</td><td>73.1</td><td>63.0</td><td>66.1</td><td>69.9</td><td>80.3</td><td>61.4</td><td>81.9</td><td>80.6</td><td>63.7</td><td>68.3</td><td>73.1</td><td>55.6</td><td>68.9</td><td>52.2</td><td>60.7</td><td>66.8</td><td>64.2</td>
+</tr>
+<tr>
+<td>PaddleOCR-VL</td><td>0.9B</td><td>0.6B</td><td>0.3B</td><td>69.6</td><td>87.6</td><td>63.6</td><td>72.1</td><td>78.2</td><td>79.3</td><td>62.9</td><td>66.0</td><td>77.4</td><td>78.4</td><td>67.9</td><td>72.0</td><td>66.6</td><td>66.7</td><td>65.8</td><td>68.4</td><td>59.9</td><td>77.8</td><td>56.9</td><td>57.8</td><td>78.2</td><td>68.5</td>
+</tr>
+<tr>
+<td>olmOCR2</td><td>7.7B</td><td>0.7B</td><td>7B</td><td>70.4</td><td>79.9</td><td>67.2</td><td>76.7</td><td>75.7</td><td>77.3</td><td>72.5</td><td>68.9</td><td>70.6</td><td>81.0</td><td>72.0</td><td><u>88.0</u></td><td>84.0</td><td>63.3</td><td>59.0</td><td>60.8</td><td>59.4</td><td>70.6</td><td>65.8</td><td>59.2</td><td>68.6</td><td>63.4</td>
+</tr>
+<tr>
+<td>MinerU-2.5-Pro</td><td>1.2B</td><td>0.7B</td><td>0.5B</td><td>71.0</td><td>86.2</td><td>66.1</td><td>74.6</td><td>78.3</td><td>79.5</td><td>63.4</td><td>67.4</td><td>78.0</td><td>79.7</td><td>72.1</td><td>78.6</td><td>74.2</td><td>67.0</td><td>56.6</td><td>72.2</td><td>59.1</td><td>77.6</td><td>62.6</td><td>61.8</td><td>76.5</td><td>69.7</td>
+</tr>
+<tr>
+<td>PaddleOCR-VL-1.6</td><td>0.9B</td><td>0.6B</td><td>0.3B</td><td>75.0</td><td>82.8</td><td>72.6</td><td>78.0</td><td>84.1</td><td>79.7</td><td>69.2</td><td>74.8</td><td>81.6</td><td>82.0</td><td>74.7</td><td>76.4</td><td>79.3</td><td>71.6</td><td>69.4</td><td>65.6</td><td>68.7</td><td>82.5</td><td>70.7</td><td>62.3</td><td>78.0</td><td>75.7</td>
+</tr>
+<tr>
+<td>HunyuanOCR-1.5</td><td>1B</td><td>0.4B</td><td>0.6B</td><td>76.8</td><td>86.2</td><td>73.6</td><td>79.7</td><td>79.6</td><td>80.4</td><td>74.2</td><td>70.0</td><td>81.5</td><td>84.5</td><td>78.4</td><td>86.4</td><td>82.4</td><td>73.5</td><td>71.8</td><td>71.6</td><td>65.5</td><td>75.7</td><td>67.4</td><td>77.7</td><td>80.8</td><td>77.2</td>
+</tr>
+<tr>
+<td>Kimi-K2.5</td><td>1T</td><td>0.4B</td><td>1T</td><td>77.5</td><td>85.0</td><td>75.0</td><td>81.6</td><td>85.9</td><td>86.2</td><td>72.7</td><td>71.0</td><td>80.6</td><td>86.6</td><td>77.4</td><td>87.6</td><td><strong>86.2</strong></td><td>72.9</td><td>75.8</td><td>74.5</td><td><u>72.5</u></td><td>70.9</td><td>61.8</td><td>67.0</td><td>81.7</td><td><u>78.6</u></td>
+</tr>
+<tr>
+<td>PaddleOCR-VL-1.5</td><td>0.9B</td><td>0.6B</td><td>0.3B</td><td>78.3</td><td>87.4</td><td>75.2</td><td>81.2</td><td>84.8</td><td>83.0</td><td>75.7</td><td><u>78.1</u></td><td>83.9</td><td>85.2</td><td>80.6</td><td>80.2</td><td>78.9</td><td>74.9</td><td>71.3</td><td>67.7</td><td>69.5</td><td>86.0</td><td>76.0</td><td>68.4</td><td><strong>84.8</strong></td><td>75.7</td>
+</tr>
+<tr>
+<td>chandra-ocr-2</td><td>5.3B</td><td>0.5B</td><td>4.8B</td><td>79.7</td><td>87.8</td><td>77.1</td><td>82.7</td><td>86.6</td><td><u>86.5</u></td><td>69.7</td><td>70.3</td><td>84.6</td><td>87.4</td><td>82.7</td><td><strong>90.7</strong></td><td><u>85.6</u></td><td>76.4</td><td>78.2</td><td>81.1</td><td>68.8</td><td>80.3</td><td>74.0</td><td>78.5</td><td>73.8</td><td>76.3</td>
+</tr>
+<tr>
+<td>dots.mocr</td><td>3B</td><td>1.2B</td><td>1.8B</td><td>80.5</td><td><strong>90.5</strong></td><td>77.2</td><td>81.7</td><td>82.6</td><td><strong>87.4</strong></td><td>71.3</td><td>70.1</td><td>84.5</td><td><strong>89.3</strong></td><td>83.2</td><td>86.8</td><td>79.9</td><td>79.2</td><td>83.3</td><td>83.6</td><td><strong>75.0</strong></td><td>78.7</td><td>71.2</td><td>77.9</td><td><u>84.6</u></td><td><strong>79.6</strong></td>
+</tr>
+<tr>
+<td><strong>MonkeyOCRv2-S-Parsing<a href="https://huggingface.co/zenosai/MonkeyOCRv2-S-Parsing">🤗</a></strong></td><td>0.6B</td><td>0.03B</td><td>0.6B</td><td><u>82.5</u></td><td>87.9</td><td><u>80.7</u></td><td><u>83.2</u></td><td><u>87.3</u></td><td>83.6</td><td><strong>76.8</strong></td><td>73.6</td><td><u>85.4</u></td><td>87.2</td><td><u>85.5</u></td><td>87.4</td><td>81.9</td><td><u>81.7</u></td><td><strong>91.2</strong></td><td><u>87.1</u></td><td>69.9</td><td><strong>88.7</strong></td><td><u>78.0</u></td><td><u>79.8</u></td><td>84.4</td><td>74.7</td>
+</tr>
+<tr>
+<td><strong>MonkeyOCRv2-B-Parsing<a href="https://huggingface.co/zenosai/MonkeyOCRv2-B-Parsing">🤗</a><strong></td><td>0.7B</td><td>0.1B</td><td>0.6B</td><td><strong>83.3</strong></td><td><u>88.1</u></td><td><strong>81.7</strong></td><td><strong>84.2</strong></td><td><strong>87.7</strong></td><td>84.5</td><td>75.2</td><td><strong>78.4</strong></td><td><strong>86.5</strong></td><td><u>88.6</u></td><td><strong>86.1</strong></td><td>87.9</td><td>83.2</td><td><strong>82.1</strong></td><td><u>90.7</u></td><td><strong>87.2</strong></td><td>71.9</td><td><u>87.6</u></td><td><strong>80.1</strong></td><td><strong>80.8</strong></td><td>83.6</td><td>75.3</td>
+</tr>
+  </tbody>
+</table>
+#### 7. Document understanding performance comparison across different vision foundation models. The evaluation benchmarks are selected following [TextMonkey](https://arxiv.org/pdf/2403.04473) and [DT-VQA](https://arxiv.org/pdf/2405.06706).
+<table>
+    <thead>
+        <tr>
+            <th>Model</th>
+            <th>Params</th>
+            <th>Overall</th>
+            <th>DocVQA</th>
+            <th>InfoVQA</th>
+            <th>DF</th>
+            <th>KLC</th>
+            <th>WTQ</th>
+            <th>ChartQA</th>
+            <th>DT-VQA</th>
+            <th>OCRBench</th>
+        </tr>
+    </thead>
+    <tbody>
+        <tr>
+            <td>CLIP-B</td>
+            <td>86M</td>
+            <td>16.0</td>
+            <td>20.1</td>
+            <td>24.2</td>
+            <td>2.3</td>
+            <td>13.8</td>
+            <td>12.8</td>
+            <td>22.2</td>
+            <td>22.3</td>
+            <td>10.6</td>
+        </tr>
+        <tr>
+            <td>SigLIP2-B</td>
+            <td>93M</td>
+            <td>24.9</td>
+            <td>27.0</td>
+            <td>23.5</td>
+            <td>3.1</td>
+            <td>16.7</td>
+            <td>17.4</td>
+            <td>35.0</td>
+            <td>41.5</td>
+            <td>35.1</td>
+        </tr>
+        <tr>
+            <td>RADIOv2.5-B</td>
+            <td>98M</td>
+            <td>37.5</td>
+            <td>60.3</td>
+            <td>31.2</td>
+            <td>29.9</td>
+            <td>30.4</td>
+            <td>29.7</td>
+            <td>51.1</td>
+            <td>44.2</td>
+            <td>23.1</td>
+        </tr>
+        <tr>
+            <td>OpenVision-B</td>
+            <td>87M</td>
+            <td>44.0</td>
+            <td>63.3</td>
+            <td>30.7</td>
+            <td>19.8</td>
+            <td>33.1</td>
+            <td>31.1</td>
+            <td>58.3</td>
+            <td>62.6</td>
+            <td><u>52.9</u></td>
+        </tr>
+        <tr>
+            <td>DINOv3-B</td>
+            <td>86M</td>
+            <td>16.1</td>
+            <td>26.5</td>
+            <td>20.8</td>
+            <td>5.6</td>
+            <td>13.2</td>
+            <td>14.0</td>
+            <td>28.9</td>
+            <td>15.8</td>
+            <td>3.9</td>
+        </tr>
+        <tr>
+            <td>SAM-B</td>
+            <td>90M</td>
+            <td>25.2</td>
+            <td>37.8</td>
+            <td>22.2</td>
+            <td>4.7</td>
+            <td>17.5</td>
+            <td>17.6</td>
+            <td>46.5</td>
+            <td>33.3</td>
+            <td>21.9</td>
+        </tr>
+        <tr>
+            <td>SAM2-B</td>
+            <td>69M</td>
+            <td>22.3</td>
+            <td>32.5</td>
+            <td>21.9</td>
+            <td>2.7</td>
+            <td>15.8</td>
+            <td>16.6</td>
+            <td>40.2</td>
+            <td>30.3</td>
+            <td>18.4</td>
+        </tr>
+        <tr>
+            <td>oCLIP</td>
+            <td>24M</td>
+            <td>12.4</td>
+            <td>14.8</td>
+            <td>19.5</td>
+            <td>1.4</td>
+            <td>7.4</td>
+            <td>11.4</td>
+            <td>17.9</td>
+            <td>19.2</td>
+            <td>7.4</td>
+        </tr>
+        <tr>
+            <td>DiT</td>
+            <td>86M</td>
+            <td>8.9</td>
+            <td>11.3</td>
+            <td>20.9</td>
+            <td>0.9</td>
+            <td>5.2</td>
+            <td>9.9</td>
+            <td>12.0</td>
+            <td>9.2</td>
+            <td>1.9</td>
+        </tr>
+        <tr>
+            <td><strong>MonkeyOCRv2-S*<a href="https://huggingface.co/zenosai/MonkeyOCRv2-S-Und">🤗Link</a></strong></td>
+            <td>28M</td>
+            <td><u>55.9</u></td>
+            <td><strong>79.3</strong></td>
+            <td><u>44.5</u></td>
+            <td><u>65.1</u></td>
+            <td><u>37.6</u></td>
+            <td><u>43.0</u></td>
+            <td><strong>62.0</strong></td>
+            <td><u>63.1</u></td>
+            <td>52.2</td>
+        </tr>
+        <tr>
+            <td><strong>MonkeyOCRv2-B*<a href="https://huggingface.co/zenosai/MonkeyOCRv2-B-Und">🤗Link</a></strong></td>
+            <td>113M</td>
+            <td><strong>57.2</strong></td>
+            <td><strong>79.3</strong></td>
+            <td><strong>46.3</strong></td>
+            <td><strong>65.8</strong></td>
+            <td><strong>38.2</strong></td>
+            <td><strong>43.2</strong></td>
+            <td><strong>62.0</strong></td>
+            <td><strong>64.3</strong></td>
+            <td><strong>58.1</strong></td>
+        </tr>
+    </tbody>
+</table>
+## Expert Model Labeling Toolchain
+We adopt a multi-expert labeling pipeline to obtain reliable annotations for documents. The pipeline includes the following steps:
+1. **Structure Detection**
+   We use **dots.mocr** for document structure detection and reading-order prediction. The detected regions, including text blocks, tables, formulas, and other layout elements, are cropped from the original page image for subsequent recognition.
+2. **Content Recognition**
+   Each cropped block is independently recognized by three expert models: **dots.mocr**, **PaddleOCR-VL**, and **Qwen3-VL**. These complementary models provide multiple annotations for the same block, reducing reliance on any single OCR system.
+3. **Block-Level Agreement Filtering**
+   We compare the recognition results from the three expert models and filter out blocks with low agreement. For retained blocks, we select the prediction that has the highest average agreement with the other two predictions as the final block-level annotation.
+4. **Page-Level Quality Control**
+   Pages containing any filtered block are discarded. In addition, we use **Qwen3** to verify whether the predicted reading order is reasonable, and **Qwen3-VL** to check whether document regions are missed during structure detection.
+This multi-expert agreement strategy reduces model-specific annotation errors and improves the reliability of the generated annotations.
+### References
+- **dots.mocr**: https://github.com/rednote-hilab/dots.mocr
+- **PaddleOCR-VL**: https://github.com/PaddlePaddle/PaddleOCR
+- **Qwen3-VL**: https://github.com/QwenLM/Qwen3-VL
+- **Qwen3**: https://github.com/QwenLM/Qwen3
+## Copyright
+We warmly welcome your feedback, suggestions, and contributions, which are essential to the continued development and improvement of our framework. Note: This model is intended for academic research and non-commercial use only. For any questions, please contact us at xbai@hust.edu.cn or ylliu@hust.edu.cn.