License Plate Automatic Recognition Model based on PCA-SVM

Abstract

License plate recognition is one of the important research topic in computer vision, image processing and pattern recognition technology in the field of intelligent transportation applications. It is an important part of the intelligent traffic management, project management of highways, urban transport and parking and plays a decisive role. In order to increase the license plate recognition precisions, this paper proposes a license plate automatic recognition model (PCA-SVM) which combines principal component analysis and Support Vector Machine. First, the structural features and image grey features, such as license plate outline and strokes orders are extracted; then, the principal component analysis fuses the two types of features, reduces the dimensions and removes redundancy. Finally, Support Vector Machine is applied to establish structural feature recognition model and grey feature recognition model and final recognition results are evaluated by high confidence discrimination rules. The simulation results illustrate PCA-SVM can increase the license plate recognition precisions, and the recognition speed is fast. It can be applied in practical license plate recognition.