Shift- and deformation-robust optical character recognition based on parallel extraction of simple features

For a flexible pattern recognition system that is robust to the input variations, a feature extraction approach is investigated. Two types of features are extracted: one is line orientations, and the other is the eigenvectors of the covariance matrix of the patterns that cannot be distinguished with the line orientation features alone. For the feature extraction, the Vander Lugt-type filters are used, which are recorded in a small spot of holographic recording medium by use of multiplexing techniques. A multilayer perceptron implemented in a computer is trained with a set of optically extracted features, so that it can recognize the input patterns that are not used in the training. Through preliminary experiments, where English character patterns composed of only straight line segments were tested, the feasibility of our approach is demonstrated.