一种有效的手写体汉字组合特征的抽取与识别算法  

An Effective Combined Feature Extraction on Handwriting Chinese Character and Recognition Algorithm

作　　者：孙权森[1] 金忠[1] 王平安[2] 夏德深[1] 

机构地区：[1]南京理工大学计算机系 [2]香港中文大学计算机科学与工程系

出　　处：《中文信息学报》2005年第4期78-83,88,共7页Journal of Chinese Information Processing

基　　金：国家自然科学基金资助项目(60473039);香港特区政府研究资助局资助项目(CUHK4223/04E)

摘　　要：基于特征融合的思想,从有利于模式分类的角度,推广了典型相关分析的理论,建立了广义的典型相关分析用于图像识别的理论框架。在该框架下,首先利用广义的典型相关判据准则函数,求取两组特征矢量的广义投影矢量集,构成一对变换矩阵;然后根据所提出的新的特征融合策略,对两种手写体汉字特征进行融合,所抽取的模式的相关特征矩阵,在普通分类器下取得了良好的分类效果,优于已有的特征融合方法及基于单一特征的PCA方法和FLDA方法。A new method of combined feature extraction, based on the idea of feature fusion, is proposed in this paper. The theory of canonical correlation analysis(CCA) in consideration of pattern classification have generalized. A framework of generalized canonical correlation analysis(GCCA) used in pattern recognition is established. In this framework, first of all, based on generalized canonical correlation discriminant criterion, solve the generalized projective vectors of the two groups of feature vectors to compose transformation matrix. Then, using a new feature fusion strategies to fuse two existing features of Handwriting Chinese Character, and the correlative feature matrix of same pattern sample is extracted. The correlative feature matrix extracted show the essence feature of Handwriting Chinese Character. In generic classifier, we have obtained the good experimental results, our recognition rate is far higher than that of the PCA method and FLDA method adopting single feature.

关 键 词：人工智能 模式识别 手写体汉字识别 广义的典型相关分析 特征融合 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]