Improve model card: Add transformers library tag, paper/code links, authors, and fix image URLs

README.md

@@ -1,14 +1,18 @@
 ---
-license: apache-2.0
 datasets:
 - zenosai/MonkeyDocv2
+license: apache-2.0
 pipeline_tag: image-text-to-text
+library_name: transformers
 ---
-<div align="center" xmlns="http://www.w3.org/1999/html">
-<h2>
-<b>MonkeyOCRv2: A Visual-Text Foundation Model for Document AI</b>
-</h2>
 
+# MonkeyOCRv2: A Visual-Text Foundation Model for Document AI
+
+*   **Paper:** [MonkeyOCRv2: A Visual-Text Foundation Model for Document AI](https://huggingface.co/papers/2607.11562)
+*   **Code:** [GitHub Repository](https://github.com/Yuliang-Liu/MonkeyOCRv2)
+*   **Authors:** Yuliang Liu, Zhang Li, Ziyang Zhang, Shuo Zhang, Qiang Liu, Jiajun Song, Zidun Guo, Xinhan Wang, Handong Zheng, Yang Liu, Dongliang Luo, Zhiyin Ma, Jiarui Zhang, Xiang Bai
+
+<div align="center" xmlns="http://www.w3.org/1999/html">
 [![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)
@@ -16,7 +20,7 @@ pipeline_tag: image-text-to-text
 [![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"/>
+<img src="https://raw.githubusercontent.com/Yuliang-Liu/MonkeyOCRv2/main/asserts/overview.png" width="600"/>
 </div>
 
 ## News
@@ -308,7 +312,7 @@ python infer.py -h
 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"/>
+  <img src="https://raw.githubusercontent.com/Yuliang-Liu/MonkeyOCRv2/main/asserts/Visualization.gif?raw=true" width="600"/>
 </p>
 
 ## Evaluation Results
@@ -447,6 +451,8 @@ Our model supports robust document parsing in real-world scenarios across 17 lan
       <th>ExpRate</th>
       <th>CDM</th>
       <th>ExpRate</th>
+      <th>CDM</th>
+      <th>ExpRate</th>
     </tr>
   </thead>
   <tbody>
@@ -533,7 +539,7 @@ Our model supports robust document parsing in real-world scenarios across 17 lan
 #### 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"/>
+  <img src="https://raw.githubusercontent.com/Yuliang-Liu/MonkeyOCRv2/main/asserts/text_detection.png?raw=true" width="600"/>
 </p>
 
 
@@ -1189,4 +1195,4 @@ This multi-expert agreement strategy reduces model-specific annotation errors an
 - **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.
+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.