PaddleOCR2Pytorch Alternatives

Similar projects and alternatives to PaddleOCR2Pytorch

OpenCV

196 75,566 9.9 C++ PaddleOCR2Pytorch VS OpenCV

Open Source Computer Vision Library
PaddleOCR

60 38,373 8.6 Python PaddleOCR2Pytorch VS 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)
InfluxDB

www.influxdata.com sponsored

Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
ncnn

12 19,176 9.4 C++ PaddleOCR2Pytorch VS ncnn

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

3 3,546 7.1 C++ PaddleOCR2Pytorch VS onnx-simplifier

Simplify your onnx model
opencv-mobile

1 2,125 8.5 C++ PaddleOCR2Pytorch VS opencv-mobile

The minimal opencv for Android, iOS, ARM Linux, Windows, Linux, MacOS, WebAssembly
Paddle2ONNX

1 648 7.9 Python PaddleOCR2Pytorch VS Paddle2ONNX

ONNX Model Exporter for PaddlePaddle
deepin-ocr

1 20 4.0 C++ PaddleOCR2Pytorch VS deepin-ocr
WorkOS

workos.com sponsored

The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a better PaddleOCR2Pytorch alternative or higher similarity.

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[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.