Abstract
A neuro-fuzzy system for isolated hand-written digit recognition using a similarity fuzzy measure is presented. The system is composed of two main blocks: a first block that normalises the input and compares it with a set of fuzzy patterns, and a second block with a multilayer perceptron (MLP) to perform the definitive classification. The comparison with the fuzzy patterns is carried out via a fuzzy similarity measure that uses the Yager parametric norms and co-norms. Along this work, several values of the parameter have been studied, in order to obtain the optimum. The simplicity of the method makes it extremely quick. Recognition accuracy of the method is about 90% single classification, and close to 97,5% classification scheme.