License Plate Character Segmentation Using Hidden Markov Chains

We propose a method for segmentation of a line of characters in a noisy low resolution image of a car license plate. The Hidden Markov Chains are used to model a stochastic relation between an input image and a corresponding character segmentation. The segmentation problem is expressed as the maximum a posteriori estimation from a set of admissible segmentations. The proposed method exploits a specific prior knowledge available for the application at hand. Namely, the number of characters is known and its is also known that the characters can be segmented to sectors with equal but unknown width. The efficient algorithm for estimation based on dynamic programming is derived. The proposed method was successfully tested on data from a real life license plate recognition system.