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LICENSE

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+MIT License
+
+Copyright (c) 2025 PuHui Lab
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+Additional Terms
+
+If you use the PHOCR models in commercial products or services, 
+you **must include clear attribution** to PHOCR in your product documentation, 
+user interface, or other appropriate locations, e.g. Powered By PHOCR from PuHui Lab.

README.md

@@ -1,3 +1,201 @@
----
-license: mit
----
+---
+tags:
+- ocr
+- image-to-text
+license: mit
+library_name: transformers
+---
+
+# Model Card: PHOCR
+
+an open high-performance Optical Character Recognition (OCR) toolkit [PHOCR](https://github.com/puhuilab/phocr).
+
+# PHOCR: High-Performance OCR Toolkit
+
+[English](README.md) | [简体中文](README_CN.md)
+
+PHOCR is an open high-performance Optical Character Recognition (OCR) toolkit designed for efficient text recognition across multiple languages including Chinese, Japanese, Korean, Russian, Vietnamese, and Thai. **PHOCR features a completely custom-developed recognition model (PH-OCRv1) that significantly outperforms existing solutions.**
+
+## Motivation
+
+Current token-prediction-based model architectures are highly sensitive to the accuracy of contextual tokens. Repetitive patterns, even as few as a thousand instances, can lead to persistent memorization by the model. While most open-source text recognition models currently achieve character error rates (CER) in the percent range, our goal is to push this further into the per-mille range. At that level, for a system processing 100 million characters, the total number of recognition errors would be reduced to under 1 million — an order of magnitude improvement.
+
+## Features
+
+- **Custom Recognition Model**: **PH-OCRv1** achieves sub-0.x% character error rate in document-style settings by leveraging open-source models. Even achieves 0.0x% character error rate in English.
+- **Multi-language Support**: Chinese, English, Japanese, Korean, Russian, and more
+- **Rich Vocabulary**: Comprehensive vocabulary for each language. Chinese: 15,316, Korean: 17,388, Japanese: 11,186, Russian: 292.
+- **High Performance**: Optimized inference engine with ONNX Runtime support
+- **Easy Integration**: Simple Python API for quick deployment
+- **Cross-platform**: Support for CPU and CUDA
+
+## Visualization
+
+![Visualization](./vis.gif)
+
+## Installation
+
+```bash
+pip install phocr
+```
+
+## Quick Start
+
+```python
+from phocr import PHOCR
+
+# Initialize OCR engine
+engine = PHOCR()
+
+# Perform OCR on image
+result = engine("path/to/image.jpg")
+print(result)
+
+# Visualize results
+result.vis("output.jpg")
+print(result.to_markdown())
+```
+
+## Benchmarks
+
+We conducted comprehensive benchmarks comparing PHOCR with leading OCR solutions across multiple languages and scenarios. **Our custom-developed PH-OCRv1 model demonstrates significant improvements over existing solutions.**
+
+### Overall Performance Comparison
+
+<table style="width: 90%; margin: auto; border-collapse: collapse; font-size: small;">
+  <thead>
+    <tr>
+      <th rowspan="2">Model</th>
+      <th colspan="4">ZH & EN<br><span style="font-weight: normal; font-size: x-small;">CER ↓</span></th>
+      <th colspan="2">JP<br><span style="font-weight: normal; font-size: x-small;">CER ↓</span></th>
+      <th colspan="2">KO<br><span style="font-weight: normal; font-size: x-small;">CER ↓</span></th>
+      <th colspan="1">RU<br><span style="font-weight: normal; font-size: x-small;">CER ↓</span></th>
+    </tr>
+    <tr>
+      <th><i>English</i></th>
+      <th><i>Simplified Chinese</i></th>
+      <th><i>EN CH Mixed</i></th>
+      <th><i>Traditional Chinese</i></th>
+      <th><i>Document</i></th>
+      <th><i>Scene</i></th>
+      <th><i>Document</i></th>
+      <th><i>Scene</i></th>
+      <th><i>Document</i></th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>PHOCR</td>
+      <td><strong>0.0008</strong></td>
+      <td><strong>0.0057</strong></td>
+      <td><strong>0.0171</strong></td>
+      <td><strong>0.0145</strong></td>
+      <td><strong>0.0039</strong></td>
+      <td><strong>0.0197</strong></td>
+      <td><strong>0.0050</strong></td>
+      <td><strong>0.0255</strong></td>
+      <td><strong>0.0046</strong></td>
+    </tr>
+    <tr>
+      <td>Baidu</td>
+      <td>0.0014</td>
+      <td>0.0069</td>
+      <td>0.0354</td>
+      <td>0.0431</td>
+      <td>0.0222</td>
+      <td>0.0607</td>
+      <td>0.0238</td>
+      <td>0.212</td>
+      <td>0.0786</td>
+    </tr>
+    <tr>
+      <td>Ali</td>
+      <td>-</td>
+      <td>-</td>
+      <td>-</td>
+      <td>-</td>
+      <td>0.0272</td>
+      <td>0.0564</td>
+      <td>0.0159</td>
+      <td>0.102</td>
+      <td>0.0616</td>
+    </tr>
+  </tbody>
+</table>
+
+
+Notice
+
+- baidu: [Baidu Accurate API](https://ai.baidu.com/tech/ocr/general)
+- Ali: [Aliyun API](https://help.aliyun.com/zh/ocr/product-overview/recognition-of-characters-in-languages-except-for-chinese-and-english-1)
+- CER: the total edit distance divided by the total number of characters in the ground truth.
+
+
+## Advanced Usage
+
+With global KV cache enabled, we implement a simple version using PyTorch (CUDA). When running with torch (CUDA), you can enable caching by setting `use_cache=True` in `ORTSeq2Seq(...)`, which also allows for larger batch sizes.
+
+### Language-specific Configuration
+
+See [demo.py](./demo.py) for more examples.
+
+## Evaluation & Benchmarking
+
+PHOCR provides comprehensive benchmarking tools to evaluate model performance across different languages and scenarios.
+
+### Quick Benchmark
+
+Run the complete benchmark pipeline:
+```bash
+sh benchmark/run_recognition.sh
+```
+
+Calculate Character Error Rate (CER) for model predictions:
+```bash
+sh benchmark/run_score.sh
+```
+
+### Benchmark Datasets
+
+PHOCR uses standardized benchmark datasets for fair comparison:
+
+- **zh_en_rec_bench** [Chinese & English mixed text recognition](https://huggingface.co/datasets/puhuilab/zh_en_rec_bench)
+- **jp_rec_bench** [Japanese text recognition](https://huggingface.co/datasets/puhuilab/jp_rec_bench)
+- **ko_rec_bench** [Korean text recognition](https://huggingface.co/datasets/puhuilab/ko_rec_bench)
+- **ru_rec_bench** [Russian text recognition](https://huggingface.co/datasets/puhuilab/ru_rec_bench)
+
+## Further Improvements
+
+- Character error rate (CER), including punctuation, can be further reduced through additional normalization of the training corpus.
+- Text detection accuracy can be further enhanced by employing a more advanced detection framework.
+
+## Contributing
+
+We welcome contributions! Please feel free to submit issues, feature requests, or pull requests.
+
+## Support
+
+For questions and support, please open an issue on GitHub or contact the maintainers.
+
+## Acknowledgements
+
+Many thanks to [RapidOCR](https://github.com/RapidAI/RapidOCR) for detection and main framework.
+
+## License
+
+- This project is released under the Apache 2.0 license
+- The copyright of the OCR detection and classification model is held by Baidu
+- The PHOCR recognition models are under the modified MIT License - see the [LICENSE](./LICENSE) file for details
+
+## Citation
+
+If you use PHOCR in your research, please cite:
+
+```bibtex
+@misc{phocr2025,
+  title={PHOCR: High-Performance OCR Toolkit},
+  author={PuHui Lab},
+  year={2025},
+  url={https://github.com/puhuilab/phocr}
+}
+```

README_CN.md

@@ -0,0 +1,215 @@
+---
+tags:
+- ocr
+- image-to-text
+license: mit
+library_name: transformers
+---
+
+# Model Card: PHOCR
+
+高性能文字识别工具包 [PHOCR](https://github.com/puhuilab/phocr).
+
+
+# PHOCR：
+
+[English](README.md) | [简体中文](README_CN.md)
+
+PHOCR 是一个高性能的开源光学字符识别（OCR）工具包，专为多语种文本识别任务设计，支持包括中文、日文、韩文、俄文、越南文和泰文在内的多种语言。**PHOCR 搭载了我们完全自研的识别模型 PH-OCRv1，在准确率上显著优于现有解决方案。**
+
+## 背景动机
+
+当前基于下一个 token 预测的模型结构对上下文 token 的准确性非常敏感。即使仅有千次重复的模式，也可能导致模型产生永久性记忆。虽然大多数开源文字识别模型目前的字符错误率（CER）仍处于百分位水平，我们的目标是将其进一步提升至千分位（0.x%）。在这一精度下，对于处理 1 亿字符的系统，总错误字符数将下降至100万内，达到数量级的准确率提升。
+
+## 主要特性
+
+- **自研识别模型**：**PH-OCRv1**，可在文档场景中实现千分位CER，英文场景下甚至可达0.0x%。
+- **多语种支持**：支持中文、英文、日文、韩文、俄文等多种语言。
+- **丰富词表覆盖**：中文 15316，韩文 17388，日文 11186，俄文 292。
+- **高性能推理**：深度优化的模型结构，集成 ONNX Runtime 支持。
+- **轻松集成**：提供简洁的 Python API。
+- **跨平台**：支持 CPU 与 CUDA 环境。
+
+## 可视化效果
+
+![可视化效果](./vis.gif)
+
+## 安装方式
+
+```bash
+pip install phocr
+```
+
+## 快速开始
+
+```python
+from phocr import PHOCR
+
+# 初始化 OCR 引擎
+engine = PHOCR()
+
+# 对图像进行 OCR 识别
+result = engine("path/to/image.jpg")
+print(result)
+
+# 可视化结果
+result.vis("output.jpg")
+print(result.to_markdown())
+```
+
+## 性能基准测试
+
+我们进行了全面的基准测试，将 PHOCR 与领先的 OCR 解决方案在多种语言和场景下进行比较。**我们自研的 PH-OCRv1 模型在现有解决方案基础上实现了显著改进。**
+
+### 整体性能对比
+
+<table style="width: 90%; margin: auto; border-collapse: collapse; font-size: small;">
+  <thead>
+    <tr>
+      <th rowspan="2">模型</th>
+      <th colspan="4">中文 & 英文<br><span style="font-weight: normal; font-size: x-small;">CER ↓</span></th>
+      <th colspan="2">日文<br><span style="font-weight: normal; font-size: x-small;">CER ↓</span></th>
+      <th colspan="2">韩文<br><span style="font-weight: normal; font-size: x-small;">CER ↓</span></th>
+      <th colspan="1">俄文<br><span style="font-weight: normal; font-size: x-small;">CER ↓</span></th>
+    </tr>
+    <tr>
+      <th><i>英文</i></th>
+      <th><i>简体中文</i></th>
+      <th><i>中英混合</i></th>
+      <th><i>繁体中文</i></th>
+      <th><i>文档</i></th>
+      <th><i>场景</i></th>
+      <th><i>文档</i></th>
+      <th><i>场景</i></th>
+      <th><i>文档</i></th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td>PHOCR</td>
+      <td><strong>0.0008</strong></td>
+      <td><strong>0.0057</strong></td>
+      <td><strong>0.0171</strong></td>
+      <td><strong>0.0145</strong></td>
+      <td><strong>0.0039</strong></td>
+      <td><strong>0.0197</strong></td>
+      <td><strong>0.0050</strong></td>
+      <td><strong>0.0255</strong></td>
+      <td><strong>0.0046</strong></td>
+    </tr>
+    <tr>
+      <td>百度</td>
+      <td>0.0014</td>
+      <td>0.0069</td>
+      <td>0.0354</td>
+      <td>0.0431</td>
+      <td>0.0222</td>
+      <td>0.0607</td>
+      <td>0.0238</td>
+      <td>0.212</td>
+      <td>0.0786</td>
+    </tr>
+    <tr>
+      <td>阿里</td>
+      <td>-</td>
+      <td>-</td>
+      <td>-</td>
+      <td>-</td>
+      <td>0.0272</td>
+      <td>0.0564</td>
+      <td>0.0159</td>
+      <td>0.102</td>
+      <td>0.0616</td>
+    </tr>
+    <tr>
+      <td>PPOCR V5</td>
+      <td>0.011</td>
+      <td>0.060</td>
+      <td>0.032</td>
+      <td>0.061</td>
+      <td>-</td>
+      <td>-</td>
+      <td>-</td>
+      <td>-</td>
+      <td>-</td>
+    </tr>
+  </tbody>
+</table>
+
+说明:
+
+- baidu: [Baidu Accurate API](https://ai.baidu.com/tech/ocr/general)
+- Ali: [Aliyun API](https://help.aliyun.com/zh/ocr/product-overview/recognition-of-characters-in-languages-except-for-chinese-and-english-1)
+- 字符错误率（CER）：总的编辑距离除以真实标签（ground truth）中字符的总数量。
+
+## 高级用法
+
+启用全局 KV 缓存后，我们使用 PyTorch (CUDA) 实现了一个简单版本。在使用 torch (CUDA) 运行时，您可以通过在 `ORTSeq2Seq(...)` 中设置 `use_cache=True` 来启用缓存，这也允许更大的批处理大小。
+
+### 语言特定配置
+
+更多示例请参见 [demo.py](./demo.py)。
+
+## 评估与基准测试
+
+PHOCR 提供全面的基准测试工具，用于评估模型在不同语言和场景下的性能。
+
+### 快速基准测试
+
+运行完整的基准测试流程：
+```bash
+sh benchmark/run_recognition.sh
+```
+
+计算模型预测的字符错误率 (CER)：
+```bash
+sh benchmark/run_score.sh
+```
+
+### 基准测试数据集
+
+PHOCR 使用标准化的基准测试数据集进行公平比较：
+
+- **zh_en_rec_bench** [中英文混合文本识别](https://huggingface.co/datasets/puhuilab/zh_en_rec_bench)
+- **jp_rec_bench** [日文文本识别](https://huggingface.co/datasets/puhuilab/jp_rec_bench)
+- **ko_rec_bench** [韩文文本识别](https://huggingface.co/datasets/puhuilab/ko_rec_bench)
+- **ru_rec_bench** [俄文文本识别](https://huggingface.co/datasets/puhuilab/ru_rec_bench)
+
+中英文混合文本识别主要来自于[OmniDocBench](https://github.com/opendatalab/OmniDocBench)的随机采样。
+其它数据由我们的团队手工采集完成。
+
+## 后续优化方向
+
+- 通过进一步归一化训练语料，字符错误率（CER，包括标点符号）可以得到进一步降低。
+- 通过采用更先进的检测框架，文字检测的准确率可以进一步提升。
+
+## 贡献指南
+
+我们欢迎任何贡献！请随时提交 issue、功能请求或 pull request。
+
+## 支持
+
+如有问题或需要支持，请在 GitHub 上提交 issue 或联系维护者。
+
+## 鸣谢
+
+特别感谢 [RapidOCR](https://github.com/RapidAI/RapidOCR) 提供的检测模型及主框架支持。
+
+## 许可证
+
+- 本项目采用 Apache 2.0 开源许可证
+- OCR 检测和分类模型的版权归百度所有
+- PHOCR 识别模型采用修改版 MIT 许可证，详情请见 [LICENSE](./LICENSE) 文件
+
+## 引用方式
+
+如果您在研究中使用了 PHOCR，请引用：
+
+```bibtex
+@misc{phocr2025,
+  title={PHOCR: High-Performance OCR Toolkit},
+  author={PuHui Lab},
+  year={2025},
+  url={https://github.com/puhuilab/phocr}
+}
+```

configuration.json

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