Bayesian networks for incomplete data analysis in form processing - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles International journal of machine learning and cybernetics Year : 2014

Bayesian networks for incomplete data analysis in form processing

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 (PC) and maximum weighted spanning tree (MWST). 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.
Fichier principal
Vignette du fichier
MLC_final.pdf (3.17 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01099727 , version 1 (05-01-2015)

Identifiers

Cite

Emilie Philippot, Santosh K.C., Abdel Belaïd, Yolande Belaïd. Bayesian networks for incomplete data analysis in form processing. International journal of machine learning and cybernetics, 2014, pp.25. ⟨10.1007/s13042-014-0234-4⟩. ⟨hal-01099727⟩
100 View
293 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More