本文提出了一种基于统计识别方法与人工神经元网络相结合的手写体相似汉字识别方法。该方法充分利用了统计识别方法和神经元网络识别方法的优点,不仅显著地提高了相似字的识别率,而且有效地提高了系统的整体性能。对相似字的识别率由79.02%提高到84.32% ,提高了五个百分点,整体识别率提高了1.3个百分点。
Abstract
This paper presents a method to recognize handwritten similar Chinese characters based on combining statistics model recognition method with artificial neural networks (ANN) . This method takes advantage of the peculiarities of above two methods efficiently. It not only increases the recognition rate to similar Chinese characters , but also improves the performance of the system . The recognition rate to similar Chinese characters can be improved from 79.02% to 84.32% , about 5 percentage points improving ; the recognition rate to system can be improved about 1.3 percentage points.
关键词
神经元网络 /
汉字识别 /
相似字识别
{{custom_keyword}} /
Key words
neural networks /
Chinese characters recognition /
similar Chinese characters recognition
{{custom_keyword}} /
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] 马少平,夏莹,朱小燕. 基于模糊方向线素特征的手写体汉字识别. 清华大学学报,1997 ,37(3) :42 - 45
[2] 马少平. 脱机手写汉字识别研究[博士学位论文] . 北京:清华大学,1995
[3] 马少平,夏莹,朱小燕. 基于非线性规格化的手写体汉字识别. 软件学报,1996 ,863 专刊:200 - 205
[4] 马少平,夏莹,朱小燕,姜哲. 汉字识别系统的误识模型. 清华大学学报,1998 ,38(S1) :108 - 111
[5] 金连文,徐秉铮. 基于多级神经网络结构的手写体汉字识别. 通信学报,1997 ,18(5) :21 - 27
[6] 王国胤,施鸿宝. 汉字识别的并行神经网络方法. 模式识别与人工智能,1996 ,9(1) :96 - 101
[7] 洪沁,何振亚. 手写体数字的神经网络识别方法. 模式识别与人工智能,1994 ,7(1) :66 - 71
[8] 谢光毅,钟义信. 神经网络用于手写体数字识别. 模式识别与人工智能,1994 ,7(4) :334 - 337
{{custom_fnGroup.title_cn}}
脚注
{{custom_fn.content}}