Text image restoration using cellular neural networks

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