Journal of Advances in Mathematics and Computer Science

Feature extraction and feature selection place an important role in online character recognition and as procedure in choosing the relevant feature that yields minimum classification error. Character recognition has been a good research area for many years because of its potential applications in all the fields. However, most existing classifiers used in recognizing online handwritten characters suffer from poor selection of features and slow convergence which affect recognition accuracy. A genetic algorithm was modified through its fitness function and genetic operators to minimize the character recognition errors. In this paper Modified Genetic Algorithm (MGA) was used to select optimized feature subset of the character to extract discriminant features for classification task. Some of research works have tried to improve online character recognition and their works were based on learning rate and error adjustment which slow down the training process. Thus, to alleviate this problems, a genetic based neural network model was developed using MGA to optimize an existing Modified Optical Backpropagation (MOBP) neural network. Two classifiers (C1 and C2) were formulated from MGA-MOBP such that C1 classified using MGA at classification level while C2 employed MGA at both feature selection level and classification level. The experiment results showed that the developed C2 achieved a better performance with no recognition failure and 99.44 recognition accuracy.