手写体数字识别与认证的小波特征提取  

Wavelet Feature Extraction for the Recognition and Verification of Handwritten Numerals

作　　者：黄同城[1] 丁友东[1] 

机构地区：[1]上海大学计算机工程与科学学院

出　　处：《计算机工程与科学》2006年第10期47-49,共3页Computer Engineering & Science

基　　金：湖南省教育厅资助项目(02C429);邵阳学院自然科学基金资助项目(2004B10)

摘　　要：本文提出了基于Kirsch边缘增强的二维小波特征与二维复小波特征的提取技术。这两类特征与几何特征融合识别手写体数字。此外,对所提取的小波特征提取方法的优点进行了讨论。最后进行的手写体数字识别与认证实验表明,这两类混合特征的集合能获得很好的识别与认证性能。The paper puts forth the technique for extracting the 2-D wavelet features and the 2-D complex wavelet features based on the Kirsch edge enhancement. The two types of hybrid features are congregated by combining them with the geometrical features for the recognition of handwritten numerals. In addition, the merits of the proposed wavelet feature extraction methods are discussed. Experiments based on handwritten numeral recognition and verification show that the two hybrid feature sets can achieve high recognition and verification performance.

关 键 词：混合特征提取 小波变换 复小波变换 OCR 人工神经网络 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]