License Plate Recognition Methods Employing Neural Networks

Abstract

Advances in both parallel processing capabilities because of graphical processing units (GPUs) and computer vision algorithms have led to the development of deep neural networks (DNN) and their utilization in real-world applications. Starting from the LeNet-5 architecture of the 1990s, modern deep neural networks may have tens to hundreds of layers to solve complex problems such as license plate detection or recognition tasks. In this manuscript, we present a review of the state-of-the-art methods related to automatic license plate recognition. Since deep networks have demonstrated a remarkable ability to outperform other machine learning techniques, we focus only on neural network based license plate recognition methods. We highlight the particular types of networks, i.e., convolutional, residual recurrent, or long-short-term-memory, used for the specific tasks of license plate detection, extraction, or recognition in different existing works. The presented summary also highlights some of the most widely used data sets for comparison and shares the results reported in the reviewed papers. We also give an overview of the effects of fog, motion, or the use of synthetic data on license plate recognition. Finally, promising directions for future research in this domain are presented.