

Onnxocr
Overview :
OnnxOCR is a lightweight OCR model restructured from PaddleOCR, which frees itself from the PaddlePaddle deep learning training framework to achieve rapid inference speeds. The model supports inference in over 80 languages and, after being converted to an ONNX model, its inference speed is up to 5 times faster than when using the PaddlePaddle framework. OnnxOCR is independent of the deep learning training framework and can be directly deployed, making it suitable for scenarios with limited computational power but a need for accuracy, and it can be deployed on both ARM and x86 architectures.
Target Users :
OnnxOCR is designed for developers and companies in need of fast and accurate text recognition, especially in resource-constrained environments. It provides efficient text recognition services across various platforms, be it mobile devices, embedded systems, or servers.
Use Cases
Integrate OnnxOCR into a mobile app to implement photo translation functionality
Use OnnxOCR for document digitization to improve office automation efficiency
Utilize OnnxOCR in intelligent monitoring systems for license plate recognition
Features
Supports OCR recognition in over 80 languages
Converts to ONNX model to improve inference speed
Independent of deep learning training framework, easy to deploy
Suitable for computers with ARM and x86 architectures
Improves inference speed by 4-5 times on computers with equal performance
Suitable for scenarios with limited computational power but a need for accuracy
How to Use
1. Visit the OnnxOCR GitHub page to learn about the project details
2. Clone or download the OnnxOCR code repository
3. Install the required dependencies according to the project documentation
4. Run the test script to verify the OCR model's inference effect
5. Adjust the model parameters according to your needs to optimize performance
6. Integrate OnnxOCR into your own project to implement text recognition functionality
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