{"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}
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.