onnxocr 2.0.0

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OnnxOCR

A High-Performance Multilingual OCR Engine Based on ONNX

🚀 Version Updates

• 2025.05.21
  1. Added PP-OCRv5 model, supporting 5 language types in a single model: Simplified Chinese, Traditional Chinese, Chinese Pinyin, English, and Japanese.
  2. Overall recognition accuracy improved by 13% compared to PP-OCRv4.
  3. Accuracy is consistent with PaddleOCR 3.0.

🌟 Core Advantages

1. Deep Learning Framework-Free: A universal OCR engine ready for direct deployment.
2. Cross-Architecture Support: Uses PaddleOCR-converted ONNX models, rebuilt for deployment on both ARM and x86 architecture computers with unchanged accuracy under limited computing power.
3. High-Performance Inference: Faster inference speed on computers with the same performance.
4. Multilingual Support: Single model supports 5 language types: Simplified Chinese, Traditional Chinese, Chinese Pinyin, English, and Japanese.
5. Model Accuracy: Consistent with PaddleOCR models.
6. Domestic Hardware Adaptation: Restructured code architecture for easy adaptation to more domestic GPUs by modifying only the inference engine.

🛠️ Environment Setup

python>=3.6  

pip install -i https://pypi.tuna.tsinghua.edu.cn/simple -r requirements.txt  

Note:

• The Mobile version model is used by default; the PP-OCRv5_Server-ONNX model offers better performance.
• The Mobile model is already in onnxocr/models/ppocrv5 and requires no download;
• The PP-OCRv5_Server-ONNX model is large and uploaded to Baidu Netdisk (extraction code: wu8t). After downloading, place the det and rec models in ./models/ppocrv5/ to replace the existing ones.

🚀 One-Click Run

python test_ocr.py  

📡 API Service (CPU Example)

Start Service

python app-service.py  

Test Example

Request

curl -X POST http://localhost:5005/ocr \  
-H "Content-Type: application/json" \  
-d '{"image": "base64_encoded_image_data"}'  

Response

{  
  "processing_time": 0.456,  
  "results": [  
    {  
      "text": "Name",  
      "confidence": 0.9999361634254456,  
      "bounding_box": [[4.0, 8.0], [31.0, 8.0], [31.0, 24.0], [4.0, 24.0]]  
    },  
    {  
      "text": "Header",  
      "confidence": 0.9998759031295776,  
      "bounding_box": [[233.0, 7.0], [258.0, 7.0], [258.0, 23.0], [233.0, 23.0]]  
    }  
  ]  
}  

🐳 Docker Image Environment (CPU)

Build Image

docker build -t ocr-service .  

Run Image

docker run -itd --name onnxocr-service-v3 -p 5006:5005 onnxocr-service:v3  

POST Request

url: ip:5006/ocr  

Response Example

{  
  "processing_time": 0.456,  
  "results": [  
    {  
      "text": "Name",  
      "confidence": 0.9999361634254456,  
      "bounding_box": [[4.0, 8.0], [31.0, 8.0], [31.0, 24.0], [4.0, 24.0]]  
    },  
    {  
      "text": "Header",  
      "confidence": 0.9998759031295776,  
      "bounding_box": [[233.0, 7.0], [258.0, 7.0], [258.0, 23.0], [233.0, 23.0]]  
    }  
  ]  
}  

🌟 Effect Demonstration

Example 1	Example 2

Example 3	Example 4

Example 5	Example 6

👨💻 Contact & Communication

Career Opportunities

I am currently seeking job opportunities. Welcome to connect!

OnnxOCR Community

WeChat Group

QQ Group

🎉 Acknowledgments

Thanks to PaddleOCR for technical support!

🌍 Open Source & Donations

I am passionate about open source and AI technology, believing they can bring convenience and help to those in need, making the world a better place. If you recognize this project, you can support it via Alipay or WeChat Pay (please note "Support OnnxOCR" in the remarks).

📈 Star History

🤝 Contribution Guidelines

Welcome to submit Issues and Pull Requests to improve the project together!