基于词图像的潜在语义索引在文档图像数据库中的概念查询

Sameek Banerjee, Gaurav Harit, S. Chaudhury
{"title":"基于词图像的潜在语义索引在文档图像数据库中的概念查询","authors":"Sameek Banerjee, Gaurav Harit, S. Chaudhury","doi":"10.1109/ICDAR.2007.269","DOIUrl":null,"url":null,"abstract":"In this paper we present an application of latent semantic analysis (LSA) for indexing and retrieval of document images with text. The query is specified as a set of word images and the documents which best match with the query representation in the the latent semantic space are retrieved. We show through extensive experiments on a large database that use of LSA for document images provides improvements in retrieval precision as is the case with electronic text documents.","PeriodicalId":279268,"journal":{"name":"Ninth International Conference on Document Analysis and Recognition (ICDAR 2007)","volume":"110 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Word image based latent semantic indexing for conceptual querying in document image databases\",\"authors\":\"Sameek Banerjee, Gaurav Harit, S. Chaudhury\",\"doi\":\"10.1109/ICDAR.2007.269\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we present an application of latent semantic analysis (LSA) for indexing and retrieval of document images with text. The query is specified as a set of word images and the documents which best match with the query representation in the the latent semantic space are retrieved. We show through extensive experiments on a large database that use of LSA for document images provides improvements in retrieval precision as is the case with electronic text documents.\",\"PeriodicalId\":279268,\"journal\":{\"name\":\"Ninth International Conference on Document Analysis and Recognition (ICDAR 2007)\",\"volume\":\"110 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2007-09-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ninth International Conference on Document Analysis and Recognition (ICDAR 2007)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDAR.2007.269\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ninth International Conference on Document Analysis and Recognition (ICDAR 2007)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDAR.2007.269","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8

摘要

本文提出了一种潜在语义分析(LSA)在带文本的文档图像索引和检索中的应用。查询被指定为一组单词图像,并在潜在语义空间中检索与查询表示最匹配的文档。我们通过在一个大型数据库上进行的大量实验表明,对文档图像使用LSA可以提高检索精度,就像电子文本文档的情况一样。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Word image based latent semantic indexing for conceptual querying in document image databases
In this paper we present an application of latent semantic analysis (LSA) for indexing and retrieval of document images with text. The query is specified as a set of word images and the documents which best match with the query representation in the the latent semantic space are retrieved. We show through extensive experiments on a large database that use of LSA for document images provides improvements in retrieval precision as is the case with electronic text documents.
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