A PSO-Based Modified Counterpropagation Neural Network Model for Online Handwritten Character Recognition

Online handwriting recognition today has special interest due to increased usage of the hand held devices and it has become a difficult problem because of the high variability and ambiguity in the character shapes written by individuals. 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, ANN as one of the widely used classification techniques yields high accuracy but it is computationally heavy due to its iterative nature. Hence, in this work, PSO is integrated with Modified Counter Propagation Neural Network (MCPN) to enhancement the performance of the classifier in terms of recognition accuracy and recognition time. Experiments were conducted on conventional CPN, MCPN and PSO-based MCPN 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 MCPN classifier in terms of the performance measures. KeywordsArtificial Neural Network, Counterpropagation Neural Network, Character Recognition, Feature Extraction, Feature Selection, PSO, MCPN