DocumentCode :
2504252
Title :
Bayesian Networks Learning Algorithms for Online Form Classification
Author :
Philippot, Emilie ; Belaïd, Yolande ; Belaid, Abdel
Author_Institution :
Univ. Nancy 2 LORIA, Vandoeuvre-lès-Nancy, France
fYear :
2010
fDate :
23-26 Aug. 2010
Firstpage :
1981
Lastpage :
1984
Abstract :
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%.
Keywords :
belief networks; image classification; learning (artificial intelligence); Bayesian networks learning; conditional probabilities; digital-type clip; form recognition; learning algorithm; online form classification; writing system; Pattern recognition; Baysian network; form classification; on-line hand-writting;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition (ICPR), 2010 20th International Conference on
Conference_Location :
Istanbul
ISSN :
1051-4651
Print_ISBN :
978-1-4244-7542-1
Type :
conf
DOI :
10.1109/ICPR.2010.488
Filename :
5597264
Link To Document :
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