摘要
本文提出了一种基于主元分析法(PCA)的多模板字符识别算法。为了进一步提高数字和英文字符识别的鲁棒性,本文首先利用PCA算法把提取到的字符特征降维,再采用K-均值算法把每类字符聚类为多类,使同一类字符有多个模板,最后采用欧式距离实现多模板的匹配。本文将该算法用于巴西车牌字符识别,实验表明,该算法能有效地提高多字体字符的识别正确率,具有较高的实用价值。
This paper presented a new multi-template character recognition method based on principal component analysis （PCA）. To improve robust classification research on digital and English character recognition, this paper used PCA to dimension reduction of character features firstly, then used K-means to cluster each character to make each character have multiple templates, the last achieves multi-template match by Euclidean distance. This method has been used for Brazil car plate character recognition. Experiments show that this method can improve recognition accuracy of characters, and is of practical value obviously.
出处
《电子测量技术》
2007年第1期138-141,共4页
Electronic Measurement Technology
