GENETIC ALGORITHM AND NEURAL NETWORK FOR OPTICAL CHARACTER RECOGNITION

Abstract

Computer system has been able to recognize writing as human brain does. The method mostly used for character recognition is the backpropagation network. Backpropagation network has been known for its accuracy because it allows itself to learn and improving itself thus it can achieve higher accuracy. On the other hand, backpropagation was less to be used because of its time length needed to train the network to achieve the best result possible. In this study, backpropagation network algorithm is combined with genetic algorithm to achieve both accuracy and training swiftness for recognizing alphabets. Genetic algorithm is used to define the best initial values for the network’s architecture and synapses’ weight thus within a shorter period of time, the network could achieve the best accuracy. The optimized backpropagation network has better accuracy and less training time than the standard backpropagation network. The accuracy in recognizing character differ by 10, 77%, with a success rate of 90, 77% for the optimized backpropagation and 80% accuracy for the standard backpropagation network. The training time needed for backpropagation learning phase improved significantly from 03 h, 14 min and 40 sec, a standard backpropagation training time, to 02 h 18 min and 1 sec for the optimized backpropagation network.