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