License Plate Recognition Source Code Compiler
Patreon ► In this tutorial I show how to use the Tesseract - Optical Character Recognition (OCR) in conjunction with the OpenCV library to detect text on a license plate recognition application. Tesseract is an optical character recognition engine for various operating systems. It is free software, released under the Apache License, Version 2.0, and development has been sponsored by Google since 2006. Tesseract is considered one of the most accurate open source OCR engines currently available. The Tesseract engine was originally developed as proprietary software at Hewlett Packard labs in Bristol, England and Greeley, Colorado between 1985 and 1994, with some more changes made in 1996 to port to Windows, and some migration from C to C++ in 1998. A lot of the code was written in C, and then some more was written in C++.
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Since then all the code has been converted to at least compile with a C++ compiler. Very little work was done in the following decade. It was then released as open source in 2005 by Hewlett Packard and the University of Nevada, Las Vegas (UNLV). Tesseract development has been sponsored by Google since 2006. OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in the commercial products.
Learn how to detect vehicles license plates without heavy Machine Learning techniques, just. For those not familiar with this library, SOD is an open source, modern. Just compile the C file with the SOD sources to generate the executable. The entire source code can be run, realized the license plate image preprocessing, license plate location, morphological filters, vehicle license plate character segmentation, license plate recognition, and other functions.
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Being a BSD-licensed product, OpenCV makes it easy for businesses to utilize and modify the code. The library has more than 2500 optimized algorithms, which includes a comprehensive set of both classic and state-of-the-art computer vision and machine learning algorithms. These algorithms can be used to detect and recognize faces, identify objects, classify human actions in videos, track camera movements, track moving objects, extract 3D models of objects, produce 3D point clouds from stereo cameras, stitch images together to produce a high resolution image of an entire scene, find similar images from an image database, remove red eyes from images taken using flash, follow eye movements, recognize scenery and establish markers to overlay it with augmented reality, etc. OpenCV has more than 47 thousand people in their user community and an estimated number of downloads exceeding 7 million. The library is used extensively in companies, research groups and by governmental bodies. Email: fpiscani@stemapks.com twitter: git.