• 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