Using Ant's Colony Algorithm for improved segmentation for number plate recognition

In this paper a number plate recognition system has been designed using the ant colony optimization technique. This system can be implemented in surveillance systems, detection of stolen vehicles and checking of vehicles at toll plazas, posts, barriers sand other entry points. In this paper, an ant colony based number plate extraction method is proposed. Ant colony optimization technique serves better results in edge detection while applying image segmentation, so using the concept in number plate recognition promises better accuracy. The Ant colony optimization (ACO) is an optimization algorithm inspired by the natural behavior of ant species that ants deposit pheromone on the ground for foraging. ACO is introduced to give a better image edge detection. The proposed ACO-based edge detection approach is able to establish a pheromone matrix that represents the edge information presented at each pixel position of the image, according to the movements of a number of ants which are dispatched to move on the image. Furthermore, the movements of these ants are driven by the local variation of the image's intensity values. Eventually, this gives the number plate area extracted from the image with improved accuracy. Finally a character recognition model is used to give out the final vehicle license number.