针对经验模态分解（EMD）能有效地对信号结构作出精确分辨的特点,提出了一种基于小波变换和EMD的手写体数字字符特征表示方法.首先对原始数字字符进行G小波变换极大模预处理,得到能反应字符特征信息的光滑轮廓;然后对规范轮廓曲率序列作EMD分解,以获取浓缩曲率特征的主要信息;最后对此曲率特征数据进行聚类和识别.实验结果表明,与经典的字符特征提取算法相比,文中方法具有更好的聚类效果,提高了分类器的分类设计能力.

As the empirical mode decompositon（EMD） can accurately recognize the structure of the original signal,this paper proposes a new feature extraction algorithm of handwritten Arabic numerals based on wavelet transform and EMD.In this algorithm,first,smooth contours of numeral image are obtained by preprocessing the maximum module of the G wavelet transform.Then,an EMD analysis is performed to decompose the normalized curvature sequences into their components,which produces more compact curvature features.Finally,the obtained curvature features are clustered and recognized.Experimental results show that the proposed algorithm is superior to the classic feature extraction algorithm in terms of clustering effect and classifier design ability.