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This article mainly introduces the example analysis of optical character recognition in python OpenCV, which is very detailed and has certain reference value. Friends who are interested must read it!

1. Optical character recognition, referred to as OCR, is used to describe the algorithm and technology of converting text images into machine-encoded text.

2. Image preprocessing and post-processing of OCR results are usually used to improve the accuracy of OCR.

OSD is a basic component of any OCR system.

It mainly includes the following three steps:

Accept input images (scanned, photographed or computer generated)

Automatically detect text, just like human reading

Convert text to machine-readable format for searching, indexing, and processing in larger computer vision systems

OCR looks simple on the surface. Although the field of computer vision has existed for more than 50 years, researchers have not yet created a highly accurate general OCR system, and there is still a long way to go.

In addition, given the many nuances of human communication through writing-all the problems of natural language processing (NLPNaturalLanguageProcessing)-computer vision systems can never achieve 100 per cent accuracy in reading text from images. There are too many variables such as noise, writing style, image quality and so on. Implementing OCR is not easy.

The above is all the contents of the article "sample Analysis of Optical character recognition in python OpenCV". Thank you for reading! Hope to share the content to help you, more related knowledge, welcome to follow the industry information channel!

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