手印汉字识别的非线性归一化方法——线密度均衡

H. Yamada, K. Yamamoto, T. Saito
{"title":"手印汉字识别的非线性归一化方法——线密度均衡","authors":"H. Yamada, K. Yamamoto, T. Saito","doi":"10.1109/ICPR.1988.28198","DOIUrl":null,"url":null,"abstract":"A nonlinear normalization method called the line density equalization is proposed in which a resampling is done so as to equate the product of a local line density and a (variable) sampling pitch. Consequently, the line density in the space is homogenized, the efficiency of utilization of the space is increased, and a stable normalization is obtained for partially irregular shape variations. The method was applied to handprinted kanji character recognition, and its effectiveness was demonstrated.<<ETX>>","PeriodicalId":314236,"journal":{"name":"[1988 Proceedings] 9th International Conference on Pattern Recognition","volume":"92 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1988-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"A nonlinear normalization method for handprinted kanji character recognition-line density equalization\",\"authors\":\"H. Yamada, K. Yamamoto, T. Saito\",\"doi\":\"10.1109/ICPR.1988.28198\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A nonlinear normalization method called the line density equalization is proposed in which a resampling is done so as to equate the product of a local line density and a (variable) sampling pitch. Consequently, the line density in the space is homogenized, the efficiency of utilization of the space is increased, and a stable normalization is obtained for partially irregular shape variations. The method was applied to handprinted kanji character recognition, and its effectiveness was demonstrated.<<ETX>>\",\"PeriodicalId\":314236,\"journal\":{\"name\":\"[1988 Proceedings] 9th International Conference on Pattern Recognition\",\"volume\":\"92 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1988-11-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"[1988 Proceedings] 9th International Conference on Pattern Recognition\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICPR.1988.28198\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"[1988 Proceedings] 9th International Conference on Pattern Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPR.1988.28198","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 11

摘要

提出了一种称为线密度均衡的非线性归一化方法,该方法通过重新采样使局部线密度与(可变)采样间距的乘积相等。从而使空间中的线密度均匀化,提高了空间的利用效率,并对部分不规则的形状变化得到了稳定的归一化。将该方法应用于手印汉字识别,验证了其有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A nonlinear normalization method for handprinted kanji character recognition-line density equalization
A nonlinear normalization method called the line density equalization is proposed in which a resampling is done so as to equate the product of a local line density and a (variable) sampling pitch. Consequently, the line density in the space is homogenized, the efficiency of utilization of the space is increased, and a stable normalization is obtained for partially irregular shape variations. The method was applied to handprinted kanji character recognition, and its effectiveness was demonstrated.<>
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