CRUGA character recognition using Genetic Algorithm - Calamay, Kenneth Lindsey T.; Quejada, Laurence Anthony B. (2007)
Character Recognition in an automated or semi-automated manner has many applications in numerous fields. It is somehow quite impossible to create an algorithm that perfectly recognizes characters because of noise and varying font styles, therefore designing such algorithms with these failures in mind is important so that when mistakes are inevitably made, they will be understandable to the person working with the program. Generally, for more complex recognition problems Artificial Neural Networks (ANNs) are commonly used. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process (i.e. training). NNs using BackPropagation (BP) perform well when applied to character recognition. However, a relatively newer technique-- Genetic Algorithms (GAs) due to its tendency to find the optimal solution seem to be a better training algorithm. Experiments have shown that genetic algorithm is better than backpropagation at finding solutions but at the expense of time and resources.