Car license plate recognition is addressed in this paper. Given the development of intelligent transportation systems, it is absolutely essential to implement a strong license plate recognition system. Efforts were made to put forward a novel reliable method for car license plate recognition in Iran. Each license plate recognition system comprises three main parts. The first part is the license plate detection stage. The blue color feature of the license plate margin along with Scale-Invariant Feature Transform (SIFT) algorithm were used for this purpose. The accuracy of the presented method over the database was approximately 90% in less than a second. License plate morphological features were utilized upon character segmentation. Using these features, areas with sizes close to that of the characters of a license plate may be searched. The accuracy of this method was almost 95%. A probabilistic neural network together with a Support Vector Machine (SVM) was employed at the character recognition stage. For this stage, an accuracy of nearly 97% in 55 milliseconds for each license plate was achieved. تفاصيل المقالة

Each license plate recognition system is composed of three main parts, namely, license plate detection, character segmentation and character recognition. In this paper, we focus on the improvement and innovation of the character recognition step. For this purpose, a new hierarchical architecture based on Support Vector Machines (SVMs) is suggested for Persian license plate characters recognition. Clustering characters in the hierarchical architecture is proposed based on a new criterion using a confusion matrix. The criterion and the confusion matrix have not been used for clustering in this way. The hierarchical architecture precision is 97/4% and recognizes the entire characters of a license plate in approximately 60ms. All evaluations and comparisons among the performances of previous methods and the proposed method in this paper have been done in the same hardware and software test bed. The dataset obtained from images in real conditions such as, day, night, different distances and angles. تفاصيل المقالة