近似识别文本搜索的贝叶斯相似度模型估计

A. Takasu
{"title":"近似识别文本搜索的贝叶斯相似度模型估计","authors":"A. Takasu","doi":"10.1109/ICDAR.2009.193","DOIUrl":null,"url":null,"abstract":"Approximate text search is a basic technique to handle recognized text that contains recognition errors.This paper proposes an approximate string search for recognized texturing a statistical similarity model focusing on parameter estimation.The main contribution of this paper is to propose a parameter estimation algorith using variational Bayesian expectation maximization technique. We applied the obtained model to approximate substring detection problem and experimentally showed that the Bayesian estimation is effective.","PeriodicalId":433762,"journal":{"name":"2009 10th International Conference on Document Analysis and Recognition","volume":"29 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Bayesian Similarity Model Estimation for Approximate Recognized Text Search\",\"authors\":\"A. Takasu\",\"doi\":\"10.1109/ICDAR.2009.193\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Approximate text search is a basic technique to handle recognized text that contains recognition errors.This paper proposes an approximate string search for recognized texturing a statistical similarity model focusing on parameter estimation.The main contribution of this paper is to propose a parameter estimation algorith using variational Bayesian expectation maximization technique. We applied the obtained model to approximate substring detection problem and experimentally showed that the Bayesian estimation is effective.\",\"PeriodicalId\":433762,\"journal\":{\"name\":\"2009 10th International Conference on Document Analysis and Recognition\",\"volume\":\"29 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-07-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 10th International Conference on Document Analysis and Recognition\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDAR.2009.193\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 10th International Conference on Document Analysis and Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDAR.2009.193","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6

摘要

近似文本搜索是处理包含识别错误的已识别文本的一种基本技术。提出了一种基于参数估计的统计相似度模型的纹理识别近似字符串搜索方法。本文的主要贡献是提出了一种利用变分贝叶斯期望最大化技术的参数估计算法。我们将所得到的模型应用于近似子串检测问题,实验表明贝叶斯估计是有效的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bayesian Similarity Model Estimation for Approximate Recognized Text Search
Approximate text search is a basic technique to handle recognized text that contains recognition errors.This paper proposes an approximate string search for recognized texturing a statistical similarity model focusing on parameter estimation.The main contribution of this paper is to propose a parameter estimation algorith using variational Bayesian expectation maximization technique. We applied the obtained model to approximate substring detection problem and experimentally showed that the Bayesian estimation is effective.
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