基于贝叶斯网络的脱机手写体汉字智能识别  

Intelligent offline handwritten Chinese character recognition based on Bayesian network

作　　者：温尚清[1] 郝志峰[1] 廖芹[1] 陈炎雄[1] 

机构地区：[1]华南理工大学数学科学学院,广东广州510641

出　　处：《计算机辅助工程》2006年第3期72-74,89,共4页Computer Aided Engineering

基　　金：教育部人文社会科学研究规划项目(2005-241);广东省科技攻关项目(2005B10101010)

摘　　要：针对汉字识别的超多类问题,将贝叶斯网络分类器引入小样本字符集脱机手写体汉字识别中.对手写大写数字汉字的小样本字符集构造识别系统,同时与传统的欧氏距离方法进行比较,实验表明该算法将识别率提高到92.4%,在小样本字符集脱机手写体识别中具有较强的实用性和良好的扩展性.With the super-multi-class issue in Chinese character recognition, Bayesian network classifier is introduced into small-set offline handwritten Chinese character recognition, for which a recognition system is constructed and it is compared with Euclidean distance classifier. The experiments indicate that it can increase the recognition rate to 92.4% , and therefore it has more practicability and scalability.

关 键 词：贝叶斯网络 分类器 脱机手写体汉字 智能识别 欧氏距离 

分 类 号：TP391.43[自动化与计算机技术—计算机应用技术]