@inproceedings{171f7b4943a440e0b6a86b1c2589d687,

title = "Table detection via probability optimization",

abstract = "In this paper, we define the table detection problem as a probability optimization problem. We begin, as we do in our previous algorithm, finding and validating each detected table candidates. We proceed to compute a set of probability measurements for each of the table entities. The computation of the probability measurements takes into consideration tables, table text separators and table neighboring text blocks. Then, an iterative updating method is used to optimize the page segmentation probability to obtain the final result. This new algorithm shows a great improvement over our previous algorithm. The training and testing data set for the algorithm include 1, 125 document pages having 518 table entities and a total of 10, 934 cell entities. Compared with our previouswork, it raised the accuracy rate to 95.67% from 90.32% and to 97.05% from 92.04%.",

author = "Yalin Wang and Phillips, {Ihsin T.} and Haralick, {Robert M.}",

year = "2002",

doi = "10.1007/3-540-45869-7_31",

language = "English (US)",

isbn = "3540440682",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "272--282",

editor = "Daniel Lopresti and Jianying Hu and Ramanujan Kashi",

booktitle = "Document Analysis Systems V - 5th International Workshop, DAS 2002, Proceedings",

note = "5th International Workshop on Document Analysis Systems, DAS 2002 ; Conference date: 19-08-2002 Through 21-08-2002",

}