Vehicle ID Recognition Using a Combination of Support Vector Machine (SVM) and Gated Convolutional Neural Network (GCNN)

Over time, numerous studies have been conducted to read license plates and recognize license plates. However, it is noteworthy that these studies usually do not have the ability to learn complex structures in images with high accuracy. For this purpose, this paper uses the high capacities of deep neural networks to learn license plate identifiers. The proposed model in this paper includes two main steps: highlighting license plates and reading the ID. In the proposed model, the support vector machine (SVM) network is used to select the best range. After identifying the range of the license plate, its characters must be recognized. In this step, a gated convolutional neural network (GCNN) will be used. The proposed model is evaluated on two datasets, FZU Cars and Stanford Cars, and the results of the experiments show that this model has higher accuracy than other methods presented in both datasets.