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.
|Title of host publication||ICDAR '09|
|Subtitle of host publication||Proceedings of the 10th International Conference on Document Analysis and Recognition, 26-29 July 2009, Barcelona, Catalonia, Spain|
|Place of Publication||Los Alamitos, Calif.|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Number of pages||5|
|Publication status||Published - 2009|
|Event||IEEE International Conference on Document Analysis and Recognition (10th : 2009) - Barcelona|
Duration: 26 Jul 2009 → 29 Jul 2009
|Conference||IEEE International Conference on Document Analysis and Recognition (10th : 2009)|
|Period||26/07/09 → 29/07/09|
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