Entropy Computations of Document Images in Run-Length Compressed Domain

P. Nagabhushan, M. Javed, B. Chaudhuri
{"title":"Entropy Computations of Document Images in Run-Length Compressed Domain","authors":"P. Nagabhushan, M. Javed, B. Chaudhuri","doi":"10.1109/ICSIP.2014.51","DOIUrl":null,"url":null,"abstract":"Compression of documents, images, audios and videos have been traditionally practiced to increase the efficiency of data storage and transfer. However, in order to process or carry out any analytical computations, decompression has become an unavoidable pre-requisite. In this research work, we have attempted to compute the entropy, which is an important document analytic directly from the compressed documents. We use Conventional Entropy Quantifier (CEQ) and Spatial Entropy Quantifiers (SEQ) for entropy computations [1]. The entropies obtained are useful in applications like establishing equivalence, word spotting and document retrieval. Experiments have been performed with all the data sets of [1], at character, word and line levels taking compressed documents in run-length compressed domain. The algorithms developed are computational and space efficient, and results obtained match 100% with the results reported in [1].","PeriodicalId":111591,"journal":{"name":"2014 Fifth International Conference on Signal and Image Processing","volume":"7 Suppl 6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"14","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 Fifth International Conference on Signal and Image Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSIP.2014.51","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 14

Abstract

Compression of documents, images, audios and videos have been traditionally practiced to increase the efficiency of data storage and transfer. However, in order to process or carry out any analytical computations, decompression has become an unavoidable pre-requisite. In this research work, we have attempted to compute the entropy, which is an important document analytic directly from the compressed documents. We use Conventional Entropy Quantifier (CEQ) and Spatial Entropy Quantifiers (SEQ) for entropy computations [1]. The entropies obtained are useful in applications like establishing equivalence, word spotting and document retrieval. Experiments have been performed with all the data sets of [1], at character, word and line levels taking compressed documents in run-length compressed domain. The algorithms developed are computational and space efficient, and results obtained match 100% with the results reported in [1].
行距压缩域文档图像的熵计算
传统上,压缩文档、图像、音频和视频的做法是为了提高数据存储和传输的效率。然而,为了处理或执行任何分析计算,解压缩已成为不可避免的先决条件。在本研究中,我们尝试直接从压缩文档中计算熵,这是一种重要的文档分析方法。我们使用常规熵量词(CEQ)和空间熵量词(SEQ)进行熵计算。获得的熵在建立等价、单词定位和文档检索等应用中很有用。对[1]的所有数据集进行了实验,在字符、单词和行级别上对运行长度压缩域中的压缩文档进行了实验。所开发的算法具有计算效率和空间效率,所得到的结果与[1]中报道的结果吻合100%。
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