A Neural Network Based Character Recognition System Using Double Backpropagation

Abstract

Proposes a neural network based invariant character recognition system using double backpropagation network. The model consists of two parts. The first is a preprocessor which is intended to produce a translation, rotation and scale invariant representation of the input pattern. The second is a neural net classifier. The outputs produced by the preprocessor at the first stage are classified by this neural net classifier trained by a learning algorithm called double backpropagation. The recognition system was tested with ten numeric digits (0~9). The test included rotated, scaled and translated version of exemplar patterns. This simple recognizer with double backpropagation classifier could successfully recognize nearly 97% of the test patterns.