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An RDF-based blackboard architecture for improving table analysis

Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.14/118448

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Title: An RDF-based blackboard architecture for improving table analysis
Related: IEEE International Conference on Document Analysis and Recognition (10th : 2009) (26 - 29 July 2009 : Barcelona)
Related: ICDAR '09 : Proceedings of the 10th International Conference on Document Analysis and Recognition, 26-29 July 2009, Barcelona, Catalonia, Spain, p.916-920
DOI: 10.1109/ICDAR.2009.81
Publisher: Los Alamitos, Calif : IEEE Computer Society
Date: 2009
FoR/RFCD Code(s): 080100 Artificial Intelligence and Image Processing
Author/Creator: Long, Vanessa
Description: Table analysis is a complex problem, involving searching solutions from a large search space. Studies show that finding the most credible answers to complex problems often require combining multiple kinds of knowledge. Although the literature shows that both layout and language information have been used in table extraction systems, the amount of information each system uses is limited, and up till now, there is not an easy, systematic way to incorporate new information in these systems. This paper describes a framework for combining multiple solutions (including partial solutions) to solve a general table recognition problem.
Description: 5 page(s)
Subject Keyword: 080100 Artificial Intelligence and Image Processing
Subject Keyword: blackboard architecture
Subject Keyword: multi-agent system
Subject Keyword: intelligent experts
Subject Keyword: table analysis
Resource Type: conference paper
Organisation: Macquarie University. Dept. of Computing

Identifier: http://hdl.handle.net/1959.14/118448
Identifier: ISBN:9780769537252
Identifier: ISSN:1520-5363
Identifier: mq-rm-2009004595
Language: eng
Rights: Copyright 2009 IEEE. Reprinted from ICDAR '09 : Proceedings of the 10th International Conference on Document Analysis and Recognition, 26-29 July 2009, Barcelona, Catalonia, Spain. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Macquarie University’s products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
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