图像处理和图像模式识别编程教程

A. Chakraborty
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引用次数: 8

摘要

图像识别是机器学习应用的一个主要领域,随着许多编程平台的发展,它正在快速发展。虽然每个平台都有自己的独特性,但图像识别的方法包括一系列图像处理任务、分类器算法的开发、训练和测试,然后是部署。本教程将深入研究图像处理的编程方面,包括阈值,轮廓和模板匹配。为了提供编程的实际操作,本教程将密切关注图像模式识别的三个实际应用,即使用Tesseract OCR的ALPR,并将触及使用CNN进行字符检测。本教程将解释算法,通过Python使用两个主要平台实现伪代码:OpenCV和Tensorflow。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Image Processing and Image Pattern Recognition a Programming Tutorial
Image recognition is a major area of application of machine learning - evolving at a rapid pace with a number of programming platforms available to developers. While each platform has its own uniqueness, the methodology of image recognition consists of a sequence of image processing tasks, development of a classifier algorithm, training and testing followed by deployment. This tutorial will delve into the programming aspects of image processing including thresholding, contouring and template matching. In order to provide practical hands on programming this tutorial will closely look at three real life applications of image pattern recognition namely ALPR using Tesseract OCR and will touch upon using CNN for character detection. The tutorial will explain the algorithm, implementation of pseudocode through Python using two major platforms: OpenCV and Tensorflow.
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