基于人工神经网络的手写字符识别

Hanmin Huang, Hu Xiyue, Zhang Ping, Chai Yi, Weiren Shi
{"title":"基于人工神经网络的手写字符识别","authors":"Hanmin Huang, Hu Xiyue, Zhang Ping, Chai Yi, Weiren Shi","doi":"10.1109/SICE.1999.788719","DOIUrl":null,"url":null,"abstract":"Based on an artificial neural network, digital image processing, and features extraction theory, the authors analyzed a BP network's defect then presented improving solutions. In this paper, a new kind of handwritten character system has been constructed. Referring to the shortcoming of the traditional BP algorithm, a modified learning factor with adaptation is introduced, and a bizarre sample feature database is constructed for speeding up modified BP learning and classification. Experimental results show that the modified BP neural network algorithm (three layers forward, no feedback) can be used in handwritten character recognition, and satisfactory results have been obtained.","PeriodicalId":103164,"journal":{"name":"SICE '99. Proceedings of the 38th SICE Annual Conference. International Session Papers (IEEE Cat. No.99TH8456)","volume":"123 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1999-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"ANN-based handwritten character recognition\",\"authors\":\"Hanmin Huang, Hu Xiyue, Zhang Ping, Chai Yi, Weiren Shi\",\"doi\":\"10.1109/SICE.1999.788719\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Based on an artificial neural network, digital image processing, and features extraction theory, the authors analyzed a BP network's defect then presented improving solutions. In this paper, a new kind of handwritten character system has been constructed. Referring to the shortcoming of the traditional BP algorithm, a modified learning factor with adaptation is introduced, and a bizarre sample feature database is constructed for speeding up modified BP learning and classification. Experimental results show that the modified BP neural network algorithm (three layers forward, no feedback) can be used in handwritten character recognition, and satisfactory results have been obtained.\",\"PeriodicalId\":103164,\"journal\":{\"name\":\"SICE '99. Proceedings of the 38th SICE Annual Conference. International Session Papers (IEEE Cat. No.99TH8456)\",\"volume\":\"123 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1999-07-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"SICE '99. Proceedings of the 38th SICE Annual Conference. International Session Papers (IEEE Cat. No.99TH8456)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SICE.1999.788719\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"SICE '99. Proceedings of the 38th SICE Annual Conference. International Session Papers (IEEE Cat. No.99TH8456)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SICE.1999.788719","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5

摘要

基于人工神经网络、数字图像处理和特征提取理论,分析了BP网络存在的缺陷,提出了改进方案。本文构建了一种新的手写体字符系统。针对传统BP算法的不足,引入了具有自适应的改进学习因子,构建了奇异样本特征库,加快了改进BP算法的学习和分类速度。实验结果表明,改进的BP神经网络算法(三层前向,无反馈)可用于手写体字符识别,并取得了满意的效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ANN-based handwritten character recognition
Based on an artificial neural network, digital image processing, and features extraction theory, the authors analyzed a BP network's defect then presented improving solutions. In this paper, a new kind of handwritten character system has been constructed. Referring to the shortcoming of the traditional BP algorithm, a modified learning factor with adaptation is introduced, and a bizarre sample feature database is constructed for speeding up modified BP learning and classification. Experimental results show that the modified BP neural network algorithm (three layers forward, no feedback) can be used in handwritten character recognition, and satisfactory results have been obtained.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信