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# 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
## Installation
```bash
# Choose **one** installation method below:
# Method 1: Install with ONNX Runtime CPU version
pip install phocr[cpu]
# Method 2: Install with ONNX Runtime GPU version
pip install phocr[cuda]
# Required: Make sure the CUDA toolkit and cuDNN library are properly installed
# You can install cuda Runtime and cuDNN via conda:
conda install -c nvidia cuda-runtime=12.1 cudnn=9
# Or manually install the corresponding CUDA toolkit and cuDNN libraries
# Method 3: Manually manage ONNX Runtime
# You can install `onnxruntime` or `onnxruntime-gpu` yourself, then install PHOCR
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>
<tr>
<td>PP-OCRv5</td>
<td>0.0149</td>
<td>0.0226</td>
<td>0.0722</td>
<td>0.0625</td>
<td>0.0490</td>
<td>0.1140</td>
<td>0.0113</td>
<td>0.0519</td>
<td>0.0348</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)
Chinese & English mixed text recognition is mainly from [OmniDocBench](https://github.com/opendatalab/OmniDocBench) and [TC-STR](https://github.com/esun-ai/traditional-chinese-text-recogn-dataset).
Other datasets are collected by our team manually.
## 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}
}
```
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"description": "<!-- icon -->\n\n[](https://github.com/puhuilab/phocr)\n[](https://github.com/puhuilab/phocr)\n[](https://github.com/puhuilab/phocr/issues)\n[](https://github.com/puhuilab/phocr/issues)\n[](https://pypi.org/project/phocr/)\n[](https://pypi.org/project/phocr/)\n[](https://pepy.tech/project/phocr)\n[](https://pepy.tech/project/phocr)\n\n# PHOCR: High-Performance OCR Toolkit\n\n[English](README.md) | [\u7b80\u4f53\u4e2d\u6587](README_CN.md)\n\nPHOCR 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.**\n\n## Motivation\n\nCurrent 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 \u2014 an order of magnitude improvement.\n\n## Features\n\n- **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.\n- **Multi-language Support**: Chinese, English, Japanese, Korean, Russian, and more\n- **Rich Vocabulary**: Comprehensive vocabulary for each language. Chinese: 15,316, Korean: 17,388, Japanese: 11,186, Russian: 292.\n- **High Performance**: Optimized inference engine with ONNX Runtime support\n- **Easy Integration**: Simple Python API for quick deployment\n- **Cross-platform**: Support for CPU and CUDA\n\n## Visualization\n\n\n\n## Installation\n\n```bash\n# Choose **one** installation method below:\n\n# Method 1: Install with ONNX Runtime CPU version\npip install phocr[cpu]\n\n# Method 2: Install with ONNX Runtime GPU version\npip install phocr[cuda]\n# Required: Make sure the CUDA toolkit and cuDNN library are properly installed\n# You can install cuda Runtime and cuDNN via conda:\nconda install -c nvidia cuda-runtime=12.1 cudnn=9 \n# Or manually install the corresponding CUDA toolkit and cuDNN libraries\n\n# Method 3: Manually manage ONNX Runtime\n# You can install `onnxruntime` or `onnxruntime-gpu` yourself, then install PHOCR\npip install phocr\n```\n\n## Quick Start\n\n```python\nfrom phocr import PHOCR\n\n# Initialize OCR engine\nengine = PHOCR()\n\n# Perform OCR on image\nresult = engine(\"path/to/image.jpg\")\nprint(result)\n\n# Visualize results\nresult.vis(\"output.jpg\")\nprint(result.to_markdown())\n```\n\n## Benchmarks\n\nWe 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.**\n\n### Overall Performance Comparison\n\n<table style=\"width: 90%; margin: auto; border-collapse: collapse; font-size: small;\">\n <thead>\n <tr>\n <th rowspan=\"2\">Model</th>\n <th colspan=\"4\">ZH & EN<br><span style=\"font-weight: normal; font-size: x-small;\">CER \u2193</span></th>\n <th colspan=\"2\">JP<br><span style=\"font-weight: normal; font-size: x-small;\">CER \u2193</span></th>\n <th colspan=\"2\">KO<br><span style=\"font-weight: normal; font-size: x-small;\">CER \u2193</span></th>\n <th colspan=\"1\">RU<br><span style=\"font-weight: normal; font-size: x-small;\">CER \u2193</span></th>\n </tr>\n <tr>\n <th><i>English</i></th>\n <th><i>Simplified Chinese</i></th>\n <th><i>EN CH Mixed</i></th>\n <th><i>Traditional Chinese</i></th>\n <th><i>Document</i></th>\n <th><i>Scene</i></th>\n <th><i>Document</i></th>\n <th><i>Scene</i></th>\n <th><i>Document</i></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <td>PHOCR</td>\n <td><strong>0.0008</strong></td>\n <td><strong>0.0057</strong></td>\n <td><strong>0.0171</strong></td>\n <td><strong>0.0145</strong></td>\n <td><strong>0.0039</strong></td>\n <td><strong>0.0197</strong></td>\n <td><strong>0.0050</strong></td>\n <td><strong>0.0255</strong></td>\n <td><strong>0.0046</strong></td>\n </tr>\n <tr>\n <td>Baidu</td>\n <td>0.0014</td>\n <td>0.0069</td>\n <td>0.0354</td>\n <td>0.0431</td>\n <td>0.0222</td>\n <td>0.0607</td>\n <td>0.0238</td>\n <td>0.212</td>\n <td>0.0786</td>\n </tr>\n <tr>\n <td>Ali</td>\n <td>-</td>\n <td>-</td>\n <td>-</td>\n <td>-</td>\n <td>0.0272</td>\n <td>0.0564</td>\n <td>0.0159</td>\n <td>0.102</td>\n <td>0.0616</td>\n </tr>\n <tr>\n <td>PP-OCRv5</td>\n <td>0.0149</td>\n <td>0.0226</td>\n <td>0.0722</td>\n <td>0.0625</td>\n <td>0.0490</td>\n <td>0.1140</td>\n <td>0.0113</td>\n <td>0.0519</td>\n <td>0.0348</td>\n </tr>\n </tbody>\n</table>\n\n\nNotice\n\n- baidu: [Baidu Accurate API](https://ai.baidu.com/tech/ocr/general)\n- Ali: [Aliyun API](https://help.aliyun.com/zh/ocr/product-overview/recognition-of-characters-in-languages-except-for-chinese-and-english-1)\n- CER: the total edit distance divided by the total number of characters in the ground truth.\n\n\n## Advanced Usage\n\nWith 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.\n\n### Language-specific Configuration\n\nSee [demo.py](./demo.py) for more examples.\n\n## Evaluation & Benchmarking\n\nPHOCR provides comprehensive benchmarking tools to evaluate model performance across different languages and scenarios.\n\n### Quick Benchmark\n\nRun the complete benchmark pipeline:\n```bash\nsh benchmark/run_recognition.sh\n```\n\nCalculate Character Error Rate (CER) for model predictions:\n```bash\nsh benchmark/run_score.sh\n```\n\n### Benchmark Datasets\n\nPHOCR uses standardized benchmark datasets for fair comparison:\n\n- **zh_en_rec_bench** [Chinese & English mixed text recognition](https://huggingface.co/datasets/puhuilab/zh_en_rec_bench)\n- **jp_rec_bench** [Japanese text recognition](https://huggingface.co/datasets/puhuilab/jp_rec_bench)\n- **ko_rec_bench** [Korean text recognition](https://huggingface.co/datasets/puhuilab/ko_rec_bench)\n- **ru_rec_bench** [Russian text recognition](https://huggingface.co/datasets/puhuilab/ru_rec_bench)\n\nChinese & English mixed text recognition is mainly from [OmniDocBench](https://github.com/opendatalab/OmniDocBench) and [TC-STR](https://github.com/esun-ai/traditional-chinese-text-recogn-dataset).\nOther datasets are collected by our team manually.\n\n## Further Improvements\n\n- Character error rate (CER), including punctuation, can be further reduced through additional normalization of the training corpus.\n- Text detection accuracy can be further enhanced by employing a more advanced detection framework.\n\n## Contributing\n\nWe welcome contributions! 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