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.
出处
《计算机工程与科学》
CSCD
北大核心
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)
关键词
小波包
能量函数
多分类支持向量机
金融汉字
wavelet packet decomposition
energy function
multi-class SVM
amount Chinese character
