Learning global and local features for license plate detection

This paper proposes an intelligent system that is capable of automatically detecting license plates from static images captured by a digital still camera. A supervised learning approach is used to extract features from license plates, and both global feature and local feature are organized into a cascaded structure. In general, our framework can be divided into two stages. The first stage is constructed by extracting global correlation features and a posterior probability can be estimated to quickly determine the degree of resemblance between the evaluated image region and a license plate. The second stage is constructed by further extracting local dense-SIFT (dSIFT) features for AdaBoost supervised learning approach, and the selected dSIFT features will be used to construct a strong classifier. Using dSIFT as a type of highly distinctive local feature, our algorithm gives high detection rate under various complex conditions. The proposed framework is compared with existing works and promising results are obtained.