Character recognition remains one of the vital research areas mainly because its application to human-machine and machine-machine communication. One example application that needs this technology is vehicle number plate recognition. With millions of vehicles on the roads today, human resources alone are insufficient in recognizing, tracking or controlling their movements. Another area in character recognition is in virtual scenes. In such scenes, the characters are written in the air by hand

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... captured using a cheap USB camera placed in front of a subject. Such characters are termed "Air Characters" in this work. In this chapter, we present a character recognition method for virtual scenes (air characters) and vehicle number plate recognition using neural networks and evolutionary computation. We combine neural networks learning, image processing and template matching to create a novel character recognition system. To speed up the system and deal with size and orientation issues, we employ a genetic algorithm. Furthermore, to control the size of both the neural network inputs and the template, we also apply a genetic algorithm to guide the search.