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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 3/2015

01-06-2015 | Original Article

Bayesian networks for incomplete data analysis in form processing

Authors: Emilie Philippot, K. C. Santosh, Abdel Belaïd, Yolande Belaïd

Published in: International Journal of Machine Learning and Cybernetics | Issue 3/2015

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Abstract

In this paper, we study Bayesian network (BN) for form identification based on partially filled fields. It uses electronic ink-tracing files without having any information about form structure. Given a form format, the ink-tracing files are used to build the BN by providing the possible relationships between corresponding fields using conditional probabilities, that goes from individual fields up to the complete model construction. To simplify the BN, we sub-divide a single form into three different areas: header, body and footer, and integrate them together, where we study three fundamental BN learning algorithms: Naive, Peter & Clark and maximum weighted spanning tree. Under this framework, we validate it with a real-world industrial problem i.e., electronic note-taking in form processing. The approach provides satisfactory results, attesting the interest of BN for exploiting the incomplete form analysis problems, in particular.
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Metadata
Title
Bayesian networks for incomplete data analysis in form processing
Authors
Emilie Philippot
K. C. Santosh
Abdel Belaïd
Yolande Belaïd
Publication date
01-06-2015
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 3/2015
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-014-0234-4

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