License plate detection algorithm based on Haar and MB-LBP features

To address the training time-consuming and the phenomenon of weights distortions of AdaBoost in license plate detection, a license plate detection algorithm based on the Haar and MB-LBP (multi-block local binary patterns) features is presented. First, the Haar and the MB-LBP features which make the minimum classification error are calculated, respectively. The best features are chosen after comparing the error of two features to constitute the weak classifiers, and the AdaBoost algorithm is used to obtain a cascade of weak classifiers. Secondly, the weight threshold is added and the updated rules of sample weights are adjusted to avoid the phenomenon of weights distortions in the training process. The proposed algorithm is applied to detect digitals from 0 to 9 in the image. The non-maximum suppression is adopted to merge detection digital region, and the gray-scale transitions of the license plate characteristics are used to filter the candidate region to help locating license plate precisely. The experimental results show that the proposed method can effectively decrease the training time, reduce the number of features, avoid the phenomenon of weights distortions, and obtain a higher detection rate while reducing the false alarm rate.