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Learning Algorithms of Form Structure for Bayesian Networks

Emilie Philippot 1 yolande Belaïd 1 Abdel Belaïd 1 
LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : In this paper, a new method is presented for the recognition of online forms filled manually by a digital-type clip. This writing process is not very restrictive but it is only sending electronic ink without the pre-printed form, which will require to undertake field recognition without context. To identify the form model of filled fields, we propose a method based on Bayesian networks. The networks use the conditional probabilities between fields in order to infer the real structure. We associate multiple Bayesian networks for different structures levels (i.e. sub-structures) and test different algorithms for form structure learning. The experiments were conducted on the basis of 3200 forms provided by the Actimage compagny, specialist in interactive writing processes. The first results show a recognition rate reaching more than 97%.
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Contributor : Yolande Belaid Connect in order to contact the contributor
Submitted on : Friday, October 15, 2010 - 4:02:27 PM
Last modification on : Friday, February 26, 2021 - 3:28:07 PM
Long-term archiving on: : Sunday, January 16, 2011 - 2:55:54 AM


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  • HAL Id : inria-00526725, version 1



Emilie Philippot, yolande Belaïd, Abdel Belaïd. Learning Algorithms of Form Structure for Bayesian Networks. International Conference on Image Processing - ICIP 2010, Sep 2010, Hong Kong, China. pp.2149-2152. ⟨inria-00526725⟩



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