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Title of Journal: Int. J. Mach. Learn. & Cyber.

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Abbravation: International Journal of Machine Learning and Cybernetics

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Springer Berlin Heidelberg

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Bayesian networks for incomplete data analysis in form processing

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

Publish Date: 2014/02/14
Volume: 6, Issue:3, Pages: 347-363
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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.Ms. Emilie Philoppot performed experiments, under the supervision of Dr. Abdel Belaïd and Dr. Yolande Belaïd. Dr. K.C. Santosh analysed data and results, and wrote a complete paper and responses to the anonymous reviewers in addition to the supplementary results.



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