A genetic algorithm for feature selection in a neuro-fuzzy OCR system

We have worked on the development of a character recognition system in the soft computing paradigm. In this paper we present a genetic algorithm used for feature selection with a Feature Quality Index (FQI) metric. We generate feature vectors by defining fuzzy sets on Hough transform of character pattern pixels. Each feature element is multiplied by a mask vector bit before reaching the input of a multilayer perceptron (MLP). The genetic algorithm operates on the bit string represented by the mask vector to select the best set of features. The method has been tested with three benchmark data sets and the results show a fast convergence of the genetic algorithm