Handwritten characters recognition based on nature-inspired computing and neuro-evolution

The enormous services obtainable by bank and postal systems are not 100 % guaranteed due to variability of handwriting styles. Various methods based on neural networks have been suggested to address this issue. Unfortunately, they often fall into local optima that arises from the use of old learning methods. Global optimization methods provided new directions for neural networks evolution that may be useful in recognition. This paper develops efficient algorithms that compute globally optimal solutions by exploiting the benefits of both swarm intelligence and neuro-evolution in a way to improve the overall performance of a character recognition system. Various adaptations implied to both MLP and RBF networks have been suggested namely: particle swarm optimization (PSO) and the bees algorithm (BA) for characters classification, MLP training or RBF design by co-evolution and effective combinations of MLPs, RBFs or SVMs as an attempt to overcome the drawbacks of old recognition methods. Results proved that networks combination proposals ensure the highest improvement compared to either standard MLP and RBF networks, the co-evolutionary alternatives or other classifiers combination based on common combination rules namely majority voting, the fusion rules of min, max, sum, average, product and Bayes, Decision template and the Behavior Knowledge Space (BKS).