基于改进KPCA算法的车牌字符识别方法

针对核主元分析(KPCA)用于提取车牌字符特征不足的情况,提出了一种采用多组均值矢量来代替原始图像矢量进行核矩阵计算的方法,该方法使得核矩阵维数大幅降低,同时有效地保留了字符图像信息.实验结果表明,该方法在不降低识别精度的基础上对输入数据实现了有效的降维,大大缩短了计算时间,有效地满足了车牌实时识别系统技术要求.通过实验对比可知,该方法比目前常用的PCA及FLD算法具有更高的性能指标.

License Plate Recognition Method Based on Improved KPCA Algorithm

Analyzing the shortages of KPCA in feature extraction of license plate characters/figures,a new algorithm is proposed using multiple mean-vectors instead of original image information vectors to compute kernel matrix.Thus,the kernel matrix's dimensions can be reduced substantially with the image information of characters/figures kept up efficiently.Experimental results showed that the algorithm does not reduce the accuracy of recognition,during reducing greatly the number of dimensions of input data and the computing time can be shortened greatly,thus meeting satisfactorily the technological requirement for real-time recognition of vehicle license plates.The comparative test results revealed that this algorithm's performance indices are higher than both the conventional PCA and FLD algorithms.