基于小波包分解的手写体金融汉字识别  

Handwritten Amount Chinese Characters Recognition Based on Wavelet Packet Decomposition

作　　者：喻莹[1,2] 杨扬[1] 董才林[3] 何秀玲[3] 陈增照[3] 

机构地区：[1]北京科技大学信息工程学院,北京100083 [2]华中师范大学计算机科学系,湖北武汉430079 [3]华中师范大学数统学院,湖北武汉430079

出　　处：《计算机工程与科学》2009年第6期40-43,共4页Computer Engineering & Science

基　　金：湖北省科技攻关计划资助项目(2003BDST004)

摘　　要：本文提出一种基于小波包分解的手写体金融汉字识别算法。该算法首先对汉字图像进行小波包分解,利用基于节点子图像能量方差的准则选择适当的部分分解树;然后,将得到的子图像划分成多个局部窗口,计算局部窗口的能量值组成特征向量;再通过主成分分析(PCA)选择分类能力最强的一组特征,降低特征空间的维数;最后,用SVM多类分类方法进行分类判决。实验结果表明,该算法取得了较好的识别效果。A handwritten amount Chinese characters recognition algorithm based on wavelet packet transform is proposed. Firstly, wavelet packet transformation is used to decompose the character images whose proper partial decomposition tree can be chosen based on the variance characterization of the energy function. Secondly, each sub-image is divided into several local windows whose energy values are calculated to combine the feature vectors. Thirdly, the PCA transform is ap plied to all the feature vectors in order to determine a few significant features to reduce the samples of SVM. Finally, multiclass SVM is used for classification. The efficiency of this method is proved by the experiments which effectively improves the recognition rate of the amount Chinese characters.

关 键 词：小波包 能量函数 多分类支持向量机 金融汉字 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]