基于小波分析的倾斜车牌图像字符识别仿真  

Character Recognition Simulation of Tilting License Plate Image Based on Wavelet Analysis

作　　者：贺瑜飞 HE Yu-fei(School of mathematics and statistics,Yulin University San*xi Yulin 719000,China)

机构地区：[1]榆林学院数学与统计学院

出　　处：《计算机仿真》2019年第5期190-193,共4页Computer Simulation

摘　　要：针对传统倾斜车牌图像字符识别率较低、识别精度不高的问题,提出一种基于小波分析的倾斜车牌图像字符识别方法。首先对原始倾斜车牌图像转换成灰度图像预处理,获得图像像素点,利用小波矩对预处理后的车牌图像像素点提取字符特征,运用直线拟合方程矫正字符倾斜角度目标图像,然后通过主分量分析对提取的字符特征进行选择,并降低字符特征维数,获得特征向量,并输入到BP神经网络,通过对该网络权值初始化,分析输入层与输出层之间的线性映射,并通过线性映射关系完成字符识别。实验结果表明,所提方法能有效提高倾斜车牌图像字符识别率。A method to recognize characters in slanted license plate image based on wavelet analysis was proposed.First of all,this method converted the original slanted license plate image into gray image for pretreatment,and then the pixel points of image were obtained.After that,our method used wavelet moment to extract the character features from the pixels in license plate image after the pretreatment.Moreover,this method used straight-line fitting equation to correct the target image of inclination angle of character.Meanwhile,we selected the extracted character features by principal component analysis and reduced the character feature dimension to get feature vector which was inputted to BP neural network.By initializing this network weight,we analyzed the linear mapping between the input layer and the output layer.Thus,we completed the character recognition through the linear mapping relationship.Simulation results show that the proposed method can effectively improve the recognition rate for characters in slanted license plate image.

关 键 词：字符识别 小波分析 倾斜车牌 神经网络 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]