Journal of Computer Science 9 (11): 1435-1442, 2013 ISSN: 1549-3636 © 2013 Science Publications doi:10.3844/jcssp.2013.1435.1442 Published Online 9 (11) 2013 (http://www.thescipub.com/jcs.toc) Corresponding Author: Hendy Yeremia, Department of IT, School of Computer Science, Bina Nusantara University, Jakarta-Indonesia 1435 Science Publications JCS GENETIC ALGORITHM AND NEURAL NETWORK FOR OPTICAL CHARACTER RECOGNITION Hendy Yeremia, Niko Adrianus Yuwono, Pius Raymond and Widodo Budiharto Department of IT, School of Computer Science, Bina Nusantara University, Jakarta-Indonesia Received 2013-01-15, Revised 2013-04-16; Accepted 2013-09-19 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. Keywords: Backpropagation Network, Genetic Algorithm, Optical Character Recognition, Optimized Artificial Neural Network 1. INTRODUCTION Human brain consists of 10 11 sets of interconnected neurons to facilitate our reading, breathing, motion and thinking. In term of learning, human brain is superior to a microprocessor. Because of that fact, backpropagation network tries to adapt the ability of human brain to learn by experience (Pinjare and Kumar, 2012). Backropagation is probably the most common method for training forward-feed neural networks. A forward pass using an input pattern propagates through the network and produces an actual output. The backward pass uses the desired outputs corresponding to the input pattern and updates the weights according to the error signal. There are hundreds of papers covering the subject of backward propagation. Unfortunately, many of them tend to exhibit a vast stockpile of equations and complicated partial derivatives with undefined variables to explain a concept that is really quite simple. Quite often, a pseudocode algorithm or an example with pictures is the most effiecient method to convey information.The most popular method used in optical character recognizing is nevertheless backpropagation network. This method weakness is the required time to achieve the best result for recognizing alphabets tends to be long. Backpropagation itself could do the preprocessing phase for alphabet recognition less complex than genetic algorithm (Negnevitsky, 2005). Genetic algorithm would be used to optimize what a standard backpropagation network lacks, architecture and initial weights. This algorithm is often used to find an optimal solution in complex problems (Matic, 2010) by