基于离散Hopfield神经网络的污染车牌字符识别  

Contaminated License Plate Character Recognition Based on Discrete Hopfield Neural Network

作　　者：刘玥 孙国强[1] LIU Yue;SUN Guo-qiang(School of Optical-Electrical&Computer Engineering,University of Shanghai for Science&Technology,Shanghai 200093,China)

机构地区：[1]上海理工大学光电信息与计算机工程学院,上海200093

出　　处：《软件导刊》2020年第7期32-35,共4页Software Guide

摘　　要：传统字符识别方法缺乏对污染车牌字符正确识别的能力,难以有效分辨易混淆字符等。针对这些弊端,采用MATLAB对真实车牌字符图像进行处理,提出一种基于离散Hopfield神经网络的改进算法(CLP-HNN),对车牌字母及数字进行识别。实验结果表明,该算法对污染车牌字符识别率达93.3%,不仅可有效降低污染车牌错误识别的风险,而且可提高易混淆字符正确辨别率,对减少车牌误识别引起的交通安全及秩序问题有较大参考价值。To improve the disadvantages of traditional character recognition methods which lack of ability of correctly recognizing contaminated license plate characters and effectively distinguishing the confusing characters,this paper utilizes MATLAB to process the real license plate character images and proposed the contaminated license plate-Hopfield neural network(CLP-HNN)which is a modified algorithm based on discrete Hopfield neural network to recognize the letters and numbers of contaminated license plate.Experiment results have shown that the recognition rate of contaminated license plate characters by CLP-HNN algorithm can reach 93.3%.It indicates the method proposed in this paper can not only effectively decrease the risk of misrecognition of contaminated license plates but also improve the correct discrimination rate of confusing characters,which is of great significance for reducing traffic safety problems caused by license plate recognition.

关 键 词：污染车牌 字符识别 HOPFIELD神经网络 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]