Character recognition by double backpropagation neural network

A neural network based invariant character recognition system is proposed. The proposed 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 preprocessed output is then classified by a neural net classifier trained by a relatively new learning algorithm called double backpropagation. The recognition system was tested with ten numeric digits (0∼9). The test included rotated scaled and translated versions of exemplar patterns. This simple recognizer with double backpropagation classifier could successfully recognize nearly 97% of the test patterns.