小波包变换在手写体金融汉字识别中的应用

针对小波包变换的特点,提出了一种基于小波包变换的手写体金融汉字识别算法。该算法首先对汉字图像进行二维小波包分解,利用基于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,将特征向量送入支持向量机进行分类。实验结果表明,该算法取得了较好的识别效果。

Application of wavelet packet transformation in handwritten amount Chinese characters recognition

According to the traits of wavelet packet transformation,a handwritten amount Chinese characters recognition based on wavelet packet transformation is proposed.Firstly,wavelet packet transformation is used to decompose the character images whose proper partial decomposition tree could be chosen based on the variance characterization of 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 is applied to all the feature vectors in order to determine a few significant features to reduce the samples of SVM.Finally,SVM is used for classification. The efficiency of this method is proved by the experiments which effectually improves the recognition rate of the amount Chinese characters.