基于SVM的车牌数字识别方法

运用支持向量机对车牌字符进行识别,解决了由于图像受客观条件的影响、样本数量不是很大等原因导致的识别率不高的问题.主要针对车牌字符中的数字进行实验,选取了15组数字样本,8组进行训练,7组进行测试,采用交叉验证的思想对SVM进行参数C与g的寻优,并选择合适的核函数,对样本进行训练和预测,对于某些数字的识别率可达到100%,并在相同的训练集和测试集下与BP网络的识别效果进行对比.实验结果表明,SVM在训练样本较少且无字符特征提取的情况下具有很好的识别率,并且有很好的分类推广能力.

A Method for License Plate Number Recognition Based on Support Vector Machine

The application of SVM is presented in vehicle licence recognition,and to avoid the problem of the low recognition rate dependency on feature extraction and a not enough sample.This article mainly aims at the numbers of the characters,select 15 number samples, 8 groups for the training,7 foe the test,use cross validation to find the best C and g,select appropriate kernel function,after training and testing,some digital recognition rate can reach 100%,and compared with BP which using the same data.Experiment results show that:SVM has high recognition rate which can be extended greatly.