

Rapidocr
Overview :
RapidOCR is a multilingual OCR toolkit based on ONNXRuntime, OpenVINO, and PaddlePaddle. It converts PaddleOCR models into ONNX format, supporting multi-platform deployment in Python, C++, Java, and C#. It is characterized by speed, lightweight design, and intelligence, addressing memory leakage issues present in PaddleOCR.
Target Users :
RapidOCR is designed for businesses and developers who require fast and accurate text recognition, particularly in multilingual environments. Whether for document digitization, automated data entry, or image content analysis, RapidOCR provides efficient solutions.
Use Cases
Companies use RapidOCR for automated document processing, enhancing work efficiency.
Developers utilize RapidOCR for rapid text recognition in images, creating intelligent applications.
Educational institutions use RapidOCR for digitizing teaching materials, facilitating online teaching and resource sharing.
Features
Default language recognition for Chinese and English; other languages require manual conversion.
OCR based on deep learning technology, focusing on the advantages of artificial intelligence and smaller models.
Uses ONNXRuntime inference engine, achieving inference speeds 4 to 5 times faster than PaddlePaddle.
Supports rapid offline deployment, compatible with various programming languages.
Provides detailed documentation and installation guides.
Has an active community with numerous contributors and maintainers.
Follows the Apache-2.0 license, available for open-source and free usage.
How to Use
1. Visit the RapidOCR GitHub page to learn about the project details.
2. Choose the appropriate installation package and documentation based on your needs, then download and install it.
3. Read the documentation to understand how to configure the environment and use the API.
4. Write your own OCR application based on the example code provided.
5. Run the OCR model to perform text recognition on images or documents.
6. Process the recognition results for subsequent data handling and application development.
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