基于非线性PCA神经网络的手写体字符识别  

Handwritten Character Recognition Based on the Nonlinear PCA Neural Network

作　　者：孙光民[1] 张程[1] 王鹏[1] 邓超[1] 

机构地区：[1]北京工业大学 电子信息与控制工程学院,北京100022

出　　处：《北京工业大学学报》2007年第9期915-919,共5页Journal of Beijing University of Technology

摘　　要：非线性主分量分析PCA算法与子空间模式识别方法相结合,提出了一种应用于手写体字符识别的基于非线性PCA神经网络的信号重构模型,并与BP网络模型进行了比较实验,结果表明,本文提出的方法,对于0～9手写体数字识别,正确识别率达到了94.74%,而对于a～z手写体字符识别,正确识别率达到了91.03%.Principal component analysis （PCA） has been applied widely in pattern recognition. Based on the nonlinear PCA algorithm and subspace pattern recognition method, a nonlinear PCA neural network model of signal reconstruction has been proposed in this paper. The method has been used in handwritten digits and characters recognition, and a comparison with BP neural network based classifiers has been made. Some satisfactory results have been obtained. The experiment results show that the average correct identification rate of our method is up to 94.74% for the handwritten digits, and 91.03% for the handwritten characters.

关 键 词：文字识别 主分量分析 神经网络 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]