Employing Deep Learning Approaches for Automatic License Plate Recognition: A Review

Employing deep learning approaches has resulted in magnificent perfections in computer vision applications in recent years. In addition, Deep Neural Networks (DNNs) have shown to be remarkable alternatives for common shallow machine learning techniques like Support Vector Machines (SVM). Deep learning provides great solutions for both classic and modern image processing, feature extraction and object detection problems. Considering the advantages of utilizing DNNs in a wide range of computer vision fields, this paper presents a concise review of different deep learning approaches employed in Automatic License Plate Recognition (ALPR) systems. In such systems, deep learning techniques have been utilized in various phases of ALPR including license plate detection, character segmentation and Optical Character Recognition (OCR). Additionally, a comprehensive overview of common DNN architectures is introduced for better clarification and classification of introduced methods.

کلیدواژه ها:

Deep Learning ، Deep Neural Networks ، Automatic License Plate Recognition ، Intelligent Transportation Systems ، Image Processing.