Handwritten Numeral Recognition by means of Evolutionary Algorithms

We present a handwritten numeral recognition system centered on a novel method for extracting the set of prototypes to be used during the classification. The method is based on an evolutionary learning mechanism that exploits a genetic algorithm with niching for producing the best set of prototypes. By combining the search power of genetic algorithms and the ability of niching mechanisms to maintain different prototypes during the evolution, the proposed method allows to obtain as many prototypes as needed to model the variability exhibited by the samples belonging to each class. Such a learning mechanism overcomes the limitations of other evolutionary learning methods proposed in the literature for dealing with problems characterized by a large amount of variability in the data set as in the case of handwriting recognition. Experiments have proved that the performance of the system is comparable with, or even better than that exhibited by a neural classifier.