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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Kieninger, T. Dengel, A. |
| Copyright Year | 2001 |
| Description | Author affiliation: Inf. Manage. Dept., DFKI GmbH, Kaiserslautern, Germany (Kieninger, T.) |
| 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. |
| Sponsorship | IAPR |
| Starting Page | 518 |
| Ending Page | 522 |
| File Size | 532721 |
| Page Count | 5 |
| File Format | |
| ISBN | 0769512631 |
| DOI | 10.1109/ICDAR.2001.953843 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2001-09-13 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Business Interleaved codes Prototypes Character recognition Text recognition Artificial intelligence Information management Optical character recognition software Data mining Text analysis |
| Content Type | Text |
| Resource Type | Article |
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