摘要
分析了手写汉字特征的提取方法,提取具有一定互补性的轮廓方向特征和方向距离分布特征,并进行K-L变换降维处理,用多特征合成一个区分能力更强的新特征。讨论了RBF网络分类器特性,结合特征融合方法和模块RBF神经网络结构有机地构建一个小类别手写体汉字识别系统。实验表明,该系统可行和有效。
This paper analysis the feature extraction method of handwritten Chinese character. The contour direction feature(CDF)and directional distance distributions feature(DDDF) are extracted from Chinese character image as a pair of feature vectors having good complementarity. After dimensions reduction of original feature using Karhunen-Loeve transform, these two feature vectors are combined to produce a new feature vector has high discriminating powers. Basing on the characteristic of RBFNN classifier, a novel architecture which integrates feature fusion and modular RBF neural networks classifier approaches into a small set handwritten Chinese character recognition system is presented. Experiments show that the system has achieved impressive performance and the results are informing.
出处
《电子科技大学学报》
EI
CAS
CSCD
北大核心
2002年第3期229-233,共5页
Journal of University of Electronic Science and Technology of China
关键词
特征融合
手写体汉字识别
K-L变换
模块RBF网络
feature fusion
karhunen-loeve transform
modular radial basis function neural network
handwritten chinese character recognition
