BP神经网络在车牌字符识别中的应用研究  

Study about License Plate Recognition Based on Back Propagation Neural Network

作　　者：郭荣艳[1] 胡雪惠[2] 

机构地区：[1]周口师范学院物理与电子工程系,河南周口466000 [2]信阳师范学院物理电子工程学院,河南信阳464000

出　　处：《计算机仿真》2010年第9期299-301,350,共4页Computer Simulation

摘　　要：研究车牌字符识别问题,针对传统神经网络在车牌字符识别存在识别准确率低、效率低的问题,提出了一种基于改进神经网络的车牌字符识别方法。该方法首先采用Gabor滤波器提取车牌字符的特征,PCA降维处理消除车牌字符特征之间的冗余信息,然后采用改进的神经网络对提取特征进行训练得到最优识别模型,最后利用最优模型对车牌字符进行识别。仿真实验表明,数字及字母的识别准确率达95.0%以上,汉字的识别准确率达93.1%,与传统识别方法相比,识别准确率和识别速度都有了较大的改进,该方法在车牌识别的应用有着广泛的前景。n the automatic recognition system of vehicle license,feature selection for vehicle character recognition is the key factor in pattern recognition. In view of the deficiencies of traditional combination optimization method and the shortcoming of too early convergence of simple genetic algorithm,a new method of license plate recognition is proposed. First,the features of plate characters were detected by Gabor filter and the principal component analysis feature extraction. Then the features were used to train the back propagation neural networkclassifier. Finally,the plate characters were classified by the BPNN. Using the algorithm,a high recognition rate can be reached. Experimental results showed that the recognition accuracy of number and alphabet is above 95.0% and that of Chinese character is 93.1%.The algorithm is feasible,robust and applicable.

关 键 词：神经网络 车牌字符识别 特征提取 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]