Extracting characters of license plates from video sequences

In this paper, we present a new approach to extract characters on a license plate of a moving vehicle, given a sequence of perspective-distortion-corrected license plate images. Different from many existing single-frame approaches, our method simultaneously utilizes spatial and temporal information. We first model the extraction of characters as a Markov random field (MRF), where the randomness is used to describe the uncertainty in pixel label assignment. With the MRF modeling, the extraction of characters is formulated as the problem of maximizing a posteriori probability based on a given prior knowledge and observations. A genetic algorithm with local greedy mutation operator is employed to optimize the objective function. Experiments and comparison study were conducted and some of our experimental results are presented in the paper. It is shown that our approach provides better performance than other single frame methods.