• DocumentCode
    2788007
  • Title

    Automatic classification of form features based on neural networks and fourier transform

  • Author

    He, Guo-hui ; Xie, Zheng-mei ; Chen, Rong

  • Author_Institution
    Sch. of Inf., Wuyi Univ., Jiangmen
  • Volume
    2
  • fYear
    2008
  • fDate
    12-15 July 2008
  • Firstpage
    1162
  • Lastpage
    1166
  • Abstract
    This paper focuses on the identification and classification of forms in image document management system. It introduces a methodology that uses the pretreated horizontal and vertical projection of the forms for Fourier transform and the resulted power spectrum density as the eigenvector. Then we study and practice to extract the characteristics of the forms using BP neural network. This method has overcome the deficiencies caused by poor generalization or being unable to identify symmetric form structure correctly. Experiments have proved that this method can perform classification on forms with different structures, and has excellent adaptability.
  • Keywords
    Fourier transforms; backpropagation; document image processing; eigenvalues and eigenfunctions; feature extraction; image classification; neural nets; BP neural network; Fourier transform; automatic classification; automatic form feature classification; eigenvector; form horizontal projection; form identification; form vertical projection; generalization; image document management system; power spectrum density; Cybernetics; Discrete Fourier transforms; Feature extraction; Fourier transforms; Helium; Machine learning; Machine learning algorithms; Neural networks; Pattern matching; Signal processing algorithms; Classification; Feature extraction; Form identification; Fourier Transform; Neural Networks;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics, 2008 International Conference on
  • Conference_Location
    Kunming
  • Print_ISBN
    978-1-4244-2095-7
  • Electronic_ISBN
    978-1-4244-2096-4
  • Type

    conf

  • DOI
    10.1109/ICMLC.2008.4620579
  • Filename
    4620579