基于小波包和Zernike矩特征提取的车牌字符识别  

Vehicle license plate characters recognition using wavelet packet and zernike moments

作　　者：王润民[1] 钱盛友[1] 宋平[1] 许慧燕[1] 

机构地区：[1]湖南师范大学物理与信息科学学院,长沙410081

出　　处：《计算机工程与应用》2007年第14期210-212,共3页Computer Engineering and Applications

基　　金：湖南省自然科学基金(No.02JJY2059)~~

摘　　要：提出了一种基于小波包和Zernike矩特征提取的车牌字符识别方法。首先针对数字字符特殊的字体结构,采用了数学形态学方法进行滤波处理。预处理后,对待识别字符进行三层小波包分解,获取第三层各节点小波包系数;同时对小波包分解后的第二层各节点进行重构,并分别计算重构后所得图像的Zernike矩;最后对获得的小波包系数和重构后所得图像的Zernike矩所组成的特征空间进行降维处理,并利用BP神经网络对车牌中的数字进行识别。实验结果表明,该方法效果良好。A kind of character recognition method of the vehicle license plate based on the wavelet packet and Zemike moments is presented in this paper.Firstly,the digital character is processed by mathematics morphology for its special typeface structure. Then,the character is analyzed by wavelet packet for three layers to get the coefficients in nodes of the third layer.In addition, the nodes of the second layer are reconstructed and the Zemike moments of the reconstructed images are computed individually. At last,the coefficients of the third wavelet packet and the Zemike moments make up the feature space,which is processed by reducing the dimension.The digits of the vehicle license plate are recognized by BP neural network.The experimental results demonstrate the efficiency of the proposed approach.

关 键 词：字符识别 小波包 ZERNIKE矩 神经网络 

分 类 号：TP393.41[自动化与计算机技术—计算机应用技术]