摘要
给出了一种结合核主成分分析(KPCA)和支持向量机(SVM)进行车牌字符识别的新方法。该算法通过KPCA进行字符的特征提取,并利用SVM分类器完成字符的识别。实验证明,KPCA在高维空间具有较强的特征选择能力,SVM的识别率也明显高于BP神经网络。
This paper presents a new method of combining KPCA with SVM for recognizing license plate characters. First, the kernel principal component analysis is used to extract the features of a character, and then the SVM is selected to recognize the character. Experiments show that the KPCA has a strong ability to extract features and that the SVM has better performance than BPNN.
出处
《电子科技》
2006年第10期59-61,67,共4页
Electronic Science and Technology
关键词
支持向量机(SVM)
核主成分分析(KPCA)车牌字符识别
support vector machine（SVM）
kernel principal component analysis （KPCA）
license plate character recognition
