A novel feature extraction technique for the recognition of segmented handwritten characters

F. Kimura, N. Kayahara, Y. Miyake, M. Shridhar
{"title":"A novel feature extraction technique for the recognition of segmented handwritten characters","authors":"F. Kimura, N. Kayahara, Y. Miyake, M. Shridhar","doi":"10.1109/ICDAR.2003.1227647","DOIUrl":null,"url":null,"abstract":"High accuracy character recognition techniques can provide useful information for segmentation-based handwritten word recognition systems. This research describes neural network-based techniques for segmented character recognition that may be applied to the segmentation and recognition components of an off-line handwritten word recognition system. Two neural architectures along with two different feature extraction techniques were investigated. A novel technique for character feature extraction is discussed and compared with others in the literature. Recognition results above 80% are reported using characters automatically segmented from the CEDAR benchmark database as well as standard CEDAR alphanumerics.","PeriodicalId":249193,"journal":{"name":"Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings.","volume":"64 9","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"134","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDAR.2003.1227647","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 134

Abstract

High accuracy character recognition techniques can provide useful information for segmentation-based handwritten word recognition systems. This research describes neural network-based techniques for segmented character recognition that may be applied to the segmentation and recognition components of an off-line handwritten word recognition system. Two neural architectures along with two different feature extraction techniques were investigated. A novel technique for character feature extraction is discussed and compared with others in the literature. Recognition results above 80% are reported using characters automatically segmented from the CEDAR benchmark database as well as standard CEDAR alphanumerics.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
一种新的手写体分割字符识别特征提取技术
高精度字符识别技术可以为基于分词的手写词识别系统提供有用的信息。本研究描述了基于神经网络的字符分割识别技术,该技术可以应用于离线手写单词识别系统的分割和识别组件。研究了两种神经网络结构以及两种不同的特征提取技术。讨论了一种新的字符特征提取技术,并与已有文献进行了比较。80%以上的识别结果是使用从CEDAR基准数据库自动分割的字符以及标准的CEDAR字母数字来报告的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Impact of imperfect OCR on part-of-speech tagging Writer identification using innovative binarised features of handwritten numerals Word searching in CCITT group 4 compressed document images Exploiting reliability for dynamic selection of classi .ers by means of genetic algorithms Investigation of off-line Japanese signature verification using a pattern matching
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1