Text image restoration using cellular neural networks

Author

Stubberud, Peter A. ; Stubberud, AllenR

Author_Institution

Dept. of Electr. & Comput. Eng., Nevada Univ., Las Vegas, NV, USA

Volume

1

fYear

1997

fDate

9-12 Jun 1997

Firstpage

749

Abstract

Optical character recognition (OCR) is a machine process that recognizes writing symbols from an image and converts these symbols into a machine readable form. In this paper it is proposed that a cellular neural network (CNN) be trained to process distorted text and improve the accuracy of an OCR processor. The results of this test will be compared to the results for an ANN trained by back-propagation (BP) and an ANN trained by an extended Kalman filter (EKF) by examining how this preprocessing affects the accuracy of an OCR processor. The two proposed methods using ANN were successful but the training times were long. Improvement of the distortion was distinct in both cases. Continued research on both of these methods and new research on the use of a CNN for this problem is continuing

Keywords

Kalman filters; backpropagation; cellular neural nets; image restoration; learning (artificial intelligence); optical character recognition; ANN training; OCR processor; backpropagation; cellular neural networks; extended Kalman filter; image distortion; optical character recognition; preprocessing; text image restoration; training times; Artificial neural networks; Cellular neural networks; Character recognition; Image recognition; Image restoration; Optical character recognition software; Optical computing; Optical distortion; Optical filters; Writing;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 1997. ISCAS '97., Proceedings of 1997 IEEE International Symposium on

Print_ISBN

0-7803-3583-X

Type

conf

DOI

10.1109/ISCAS.1997.608996

Filename

608996