Printed Thai character recognition using fuzzy-rough sets

W. Kasemsiri, C. Kimpan
{"title":"Printed Thai character recognition using fuzzy-rough sets","authors":"W. Kasemsiri, C. Kimpan","doi":"10.1109/TENCON.2001.949607","DOIUrl":null,"url":null,"abstract":"This paper proposes the method of fuzzy rough sets for the recognition of Thai Characters. We divide the classification process into 2 levels, coarse and fine classification. Both levels of classification have the same processes, applying the rough set lower approximation and then using fuzzy rough sets. The difference between the two levels is in the features of the input data used for classifying. There are 40 coarse groups and some of them need not pass through the second level of classification. We trained this system with 2816 training samples, which were composed of 4 fonts and 4 sizes of characters. The system is tested with an unknown sample, which is composed of 7 fonts and 7 sizes of characters; 4 fonts and 4 sizes of the training sample are inclusive. The accuracy of this proposed system is as high as 89%.","PeriodicalId":358168,"journal":{"name":"Proceedings of IEEE Region 10 International Conference on Electrical and Electronic Technology. TENCON 2001 (Cat. No.01CH37239)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2001-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of IEEE Region 10 International Conference on Electrical and Electronic Technology. TENCON 2001 (Cat. No.01CH37239)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TENCON.2001.949607","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 11

Abstract

This paper proposes the method of fuzzy rough sets for the recognition of Thai Characters. We divide the classification process into 2 levels, coarse and fine classification. Both levels of classification have the same processes, applying the rough set lower approximation and then using fuzzy rough sets. The difference between the two levels is in the features of the input data used for classifying. There are 40 coarse groups and some of them need not pass through the second level of classification. We trained this system with 2816 training samples, which were composed of 4 fonts and 4 sizes of characters. The system is tested with an unknown sample, which is composed of 7 fonts and 7 sizes of characters; 4 fonts and 4 sizes of the training sample are inclusive. The accuracy of this proposed system is as high as 89%.
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
使用模糊粗糙集的印刷泰语字符识别
本文提出了一种基于模糊粗糙集的泰文字符识别方法。我们将分类过程分为粗分类和细分类2个层次。两个层次的分类过程相同,先使用粗糙集下近似,再使用模糊粗糙集。这两个级别之间的区别在于用于分类的输入数据的特征。有40个粗组,其中一些不需要通过第二级分类。我们用2816个训练样本来训练这个系统,这些样本由4种字体和4种大小的字符组成。系统使用未知样本进行测试,该样本由7种字体和7种大小的字符组成;包含4种字体和4种大小的训练样本。该系统的准确率高达89%。
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
Investigation of a fault tolerant and high performance motor drive for critical applications An optical fiber feeder system and performance for cellular microcell 800 MHz CDMA systems Reactive web policing based on self-organizing maps Features preserving filters using fuzzy Kohonen clustering network in detection of impulse noise Reliability modeling incorporating error processes for Internet-distributed software
×
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