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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Long, V. |
| Copyright Year | 2009 |
| Abstract | 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 describesa framework for combining multiple solutions (including partial solutions) to solve a general table recognition problem. |
| Starting Page | 916 |
| Ending Page | 920 |
| File Size | 189795 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781424445004 |
| ISSN | 15205363 |
| DOI | 10.1109/ICDAR.2009.81 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-07-26 |
| Publisher Place | Spain |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Resource description framework Speech analysis Problem-solving Phase detection Text analysis Data mining Multiagent systems Intelligent systems International collaboration Algorithm design and analysis table analysis Blackboard architecture multi-agent system intelligent experts |
| Content Type | Text |
| Resource Type | Article |
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