A hybrid method for robust car plate character recognition

Image based car plate recognition is an indispensable part of intelligent traffic system. The quality of the images taken for car plates, especially for Chinese car plates, however, may sometimes be very poor, due to the operating conditions. Furthermore, there exist some "similar" characters, such as "8" and "B", "7" and "T" and so on. They are less distinguishable because of noises and/or distortions. To achieve robust and high recognition performance, in this paper, a two-stage hybrid recognition system combining statistical and structural recognition method is proposed. Car plate image are skew corrected and normalized before recognition. In the first stage, four statistical subclassifiers recognize the input character independently, and the recognition results are combined using the Bayes method. If the output of the first stage contains characters that belong to prescribed sets of similarity characters, structure recognition method is used to further classify these character images. Experiments show that our recognition system is very efficient and robust. As part of an intelligent traffic system, the system has been in successful commercial use