Abstract
Plate recognizer system is an important system. It can be used for automatic parking gate or automatic ticketing system. The purpose of this study is to determine the effectiveness of Genetic Algorithms (GA) in optimizing the number of hidden neurons, learning rate and momentum rate on Backpropagation Neural Network (BPNN) that is applied to the Automatic Plate Number Recognizer (APNR). Research done by building a GA optimized BPNN (GABPNN) and APNR system using image processing methods, including grayscale conversion, top-hat transformation, binary morphological, Otsu threshold and binary image projection. The tests conducted with backpropagation training and recognition test. The result shows that GA optimized backpropagation neural network requires 2230 epochs in the training process to be convergent, which is 36.83% faster than non-optimal backpropagation neural network, while the accuracy is 1,35% better than non-optimized backpropagation neural network.
1. Introduction
In the recent years, automated systems have become an integral part of daily tasks that only a human can do before. Automated systems are meant to help human to do task that involves knowledge, reasoning and experience. The integral part of an automated system is artificial intelligence and one of the application of artificial intelligence in automated system is Optical Character Recognition (OCR). OCR let a computer recognize character through visual interpretation and recognize character automatically without help from human. There are several algorithms that we can use to create OCR system, such as template matching, support vector machine (SVM), hidden markov model, hausdorff distance and artificial neural network. Artificial neural network is the most popular algorithm that has been used by researcher to solve pattern recognition problems1 . Artificial neural network can be used to solve many problems and it can be trained over time to gain its knowledge or to enhance its accuracy in recognizing patterns.
