deepin-ocr VS PaddleOCR2Pytorch

Compare deepin-ocr vs PaddleOCR2Pytorch and see what are their differences.

PaddleOCR2Pytorch

PaddleOCR inference in PyTorch. Converted from [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR) (by frotms)
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deepin-ocr PaddleOCR2Pytorch
1 1
20 760
- -
4.0 4.4
9 months ago 2 months ago
C++ Python
GNU General Public License v3.0 only Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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deepin-ocr

Posts with mentions or reviews of deepin-ocr. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-06-12.

PaddleOCR2Pytorch

Posts with mentions or reviews of PaddleOCR2Pytorch. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-06-12.
  • [Technical Article] OCR Upgrade
    8 projects | /r/deepin | 12 Jun 2022
    Path №2: use the tools provided by the project https://github.com/frotms/PaddleOCR2Pytorch to convert the models of the Paddle Inference to the PTH files of Pytorch, then use the tools in Pytorch to convert the PTH files to the TorchScript files in Trace mode, and finally use PNNX in the NCNN toolbox to output them to PNNX and NCNN formats, and take the model files in NCNN format for deployment.

What are some alternatives?

When comparing deepin-ocr and PaddleOCR2Pytorch you can also consider the following projects:

onnx-simplifier - Simplify your onnx model

Paddle2ONNX - ONNX Model Exporter for PaddlePaddle

opencv-mobile - The minimal opencv for Android, iOS, ARM Linux, Windows, Linux, MacOS, WebAssembly

ncnn - ncnn is a high-performance neural network inference framework optimized for the mobile platform

OpenCV - Open Source Computer Vision Library

PaddleOCR - Awesome multilingual OCR toolkits based on PaddlePaddle (practical ultra lightweight OCR system, support 80+ languages recognition, provide data annotation and synthesis tools, support training and deployment among server, mobile, embedded and IoT devices)