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
Link To Document :
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