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