Off-line recognition of handwritten numeral strings composed from two-digits partially overlapped using Convolutional Neural Networks

D. Ciresan, D. Pescaru
{"title":"Off-line recognition of handwritten numeral strings composed from two-digits partially overlapped using Convolutional Neural Networks","authors":"D. Ciresan, D. Pescaru","doi":"10.1109/ICCP.2008.4648354","DOIUrl":null,"url":null,"abstract":"The objective of the present work is to provide an efficient and reliable technique for off-line recognition of handwritten numerals composed from two digits partially overlapped. It can be used in various applications, like postal code recognition or information extraction from fields of different forms. Proposed solution uses convolutional neural networks (CNNs) and rely on very light preprocessing avoiding segmentation. Test results on a comprehensive well-known character database -NIST SD 19- show a high degree of recognition accuracy.","PeriodicalId":169031,"journal":{"name":"2008 4th International Conference on Intelligent Computer Communication and Processing","volume":"156 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 4th International Conference on Intelligent Computer Communication and Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCP.2008.4648354","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

Abstract

The objective of the present work is to provide an efficient and reliable technique for off-line recognition of handwritten numerals composed from two digits partially overlapped. It can be used in various applications, like postal code recognition or information extraction from fields of different forms. Proposed solution uses convolutional neural networks (CNNs) and rely on very light preprocessing avoiding segmentation. Test results on a comprehensive well-known character database -NIST SD 19- show a high degree of recognition accuracy.
使用卷积神经网络离线识别由部分重叠的两位数组成的手写数字字符串
本文的目标是提供一种高效可靠的离线识别技术,用于部分重叠的两个数字组成的手写数字的识别。它可以用于各种应用,如邮政编码识别或从不同形式的字段中提取信息。该解决方案使用卷积神经网络(cnn),并依赖于非常轻的预处理,避免了分割。测试结果在一个综合性的知名字符数据库- nist SD 19-显示了高度的识别准确性。
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 求助全文
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