{"title":"Applying the T-Recs table recognition system to the business letter domain","authors":"T. Kieninger, A. Dengel","doi":"10.1109/ICDAR.2001.953843","DOIUrl":null,"url":null,"abstract":"This paper summarizes the core idea of the T-Recs table recognition system, an integrated system covering block-segmentation, table location and a model-free structural analysis of tables. T-Recs works on the output of commercial OCR systems that provide the word bounding box geometry together with the text itself (e.g. Xerox ScanWorX). While T-Recs performs well on a number of document categories, business letters still remained a challenging domain because the T-Recs location heuristics are mislead by their header or footer resulting in a low recognition precision. Business letters such as invoices are a very interesting domain for industrial applications due to the large amount of documents to be analyzed and the importance of the data carried within their tables. Hence, we developed a more restrictive approach which is implemented in the T-Recs++ prototype. This paper describes the ideas of the T-Recs++ location and also proposes a quality evaluation measure that reflects the bottom-up strategy of either T-Recs or T-Recs++. Finally, some results comparing both systems on a collection of business letters are given.","PeriodicalId":277816,"journal":{"name":"Proceedings of Sixth International Conference on Document Analysis and Recognition","volume":"435 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2001-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"80","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of Sixth International Conference on Document Analysis and Recognition","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDAR.2001.953843","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 80
Abstract
This paper summarizes the core idea of the T-Recs table recognition system, an integrated system covering block-segmentation, table location and a model-free structural analysis of tables. T-Recs works on the output of commercial OCR systems that provide the word bounding box geometry together with the text itself (e.g. Xerox ScanWorX). While T-Recs performs well on a number of document categories, business letters still remained a challenging domain because the T-Recs location heuristics are mislead by their header or footer resulting in a low recognition precision. Business letters such as invoices are a very interesting domain for industrial applications due to the large amount of documents to be analyzed and the importance of the data carried within their tables. Hence, we developed a more restrictive approach which is implemented in the T-Recs++ prototype. This paper describes the ideas of the T-Recs++ location and also proposes a quality evaluation measure that reflects the bottom-up strategy of either T-Recs or T-Recs++. Finally, some results comparing both systems on a collection of business letters are given.