Bayesian Networks Learning Algorithms for Online Form Classification

Emilie Philippot (1) , Yolande Belaïd (1) , Abdel Belaïd (1)

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In this paper a new method is presented for the recognition of online forms filled manually by a digital-type clip. This writing system transmits only the written fields without the pre-printed form. The form recognition consists in retrieving the original form directly from the filled fields without any context, which is a very challenging problem. We propose a method based on Bayesian networks. The networks use the conditional probabilities between fields in order to infer the real form. Two learning algorithms of form structures are employed to test their suitability for the case studied. The tests were conducted on the basis of 3200 forms provided by the Act image compagny, specialist in interactive writing processes. The first experiments show a recognition rate reaching more than 97%.

Mots clés: Baysian network; form classification; on-line hand-writting

Domaines: Interface homme-machine [cs.HC]; Traitement du texte et du document; Intelligence artificielle [cs.AI]

Emilie Philippot, Yolande Belaïd, Abdel Belaïd. Bayesian Networks Learning Algorithms for Online Form Classification. 20th International Conference on Pattern Recognition- ICPR 2010, Aug 2010, Istanbul, Turkey. pp.1981-1984, ⟨10.1109/ICPR.2010.488⟩. ⟨inria-00525546⟩