数字验证码识别的设计与实现

[Abstract]Digital Authentication Code (DAC)plays a very important role insecurity,so it can be used in many websites.With the rapid development of theInternet,we also need to think about security issues.The leak of privacy will affectusers more or less.Digital Authentication Code,as a common barrier to Internetsecurity,can make the Internet ecological environment healthier,more convenient andbetter protect user privacy.At present,the relatively common digital verification codeon websites is composed of numbers and letters.In view of the poor performance of image recognition of digital verificationcodes with glue and interference noise,this paper chooses KNN algorithm as thecharacter recognition method of digital verification codes by comparing variousmethods of identifying digital verification codes.This topic designs and analyzes therecognition of digital verification codes with glue distortion.The process mainlyconsists of three steps:preprocessing,Match recognition,analyze recognitionrate.Picture preprocessing process uses grayscale,binarization,noise reduction andsegmentation.In the phase of image segmentation,four,three,two and one charactersmay be detected.Different methods are used to process them,then single charactermatching is performed with Python tools.Finally,matching number verification codesare recognized by KNN algorithm,and the recognition rate is up to 94.4%.Thisdemonstrates that the algorithm can recognize the distorted digital authentication codepicture very well.[Keywords ]Verification code identification:KNN recognition:Verification CodeMatching:Python第一章绪论1.1研究目的及意义当前，随着互联网技术的不断创新研发，我国科技技术飞速发展，各类新兴产品应运而生，在不断丰富人们生活的同时，提高了人们生活质量与生活水平。