Using Optimized Features for Modified Optical Backpropagation Neural Network Model in Online Handwritten Character Recognition System

One major problem encountered by researchers in developing character recognition system is selection of efficient features (optimal features). In this paper, Particle Swarm Optimization (PSO) is proposed for feature selection. However, backpropagation algorithm has been reported to be an effective and most widely used supervised training algorithm for multi-layered feedforward neural networks but has the shortcomings of longer training time and entrapment into a local minimal. Several research works have been proposed to improve this algorithm but some of these research works were based on ‘learning parameter ’ which in some cases slowed down the training process. Hence, this paper has focused on alleviating the problem of standard backpropagation algorithm based on ‘error adjustment’. To this effect, PSO is integrated with a ‘Modified Optical Backpropagation (MOBP) ’ neural network to enhancement the performance of the classifier in terms of recognition accuracy and recognition time. Experiments were conducted on MOBP neural network and PSO-based MOBP classifiers using 6,200 handwritten character samples (uppercase (A-Z), lowercase (a-z) English alphabet and 10 digits (0-9)) collected from 100 subjects using G-Pen 450 digitizer and the system was tested with 100 character samples written by people who did not participate in the initial data acquisition process. Experimental results show promising results for the PSO-based MOBP classifier in terms of the performance measures