Review of License Plate Recognition Techniques with Deep Learning

Abstract

This article reviews vehicle license plate recognition (LPR) using deep learning techniques, which have become essential in intelligent transportation systems, law enforcement, and parking management. Deep learning, particularly Convolutional Neural Networks (CNNs), has replaced traditional methods with more accurate and robust systems capable of handling diverse real-world conditions. The article explores various deep learning approaches in LPR, including fusion, two-stage, end-to-end, multi-branch, and generative methods. Fusion methods combine deep learning with traditional image processing to enhance accuracy. Two-stage methods separate the detection and recognition tasks into different models. End-to-end methods use a single model for both detection and recognition, improving efficiency and reducing errors. Multi-branch methods employ parallel neural network branches to handle different tasks simultaneously, such as plate detection and character recognition. Generative methods use Generative Adversarial Networks (GANs) to create realistic license plate images, boosting recognition performance. The article evaluates these methods on benchmark datasets and identifies challenges, such as improving robustness under extreme weather conditions, developing lightweight models for real-time processing on constrained devices, and addressing privacy and security concerns. This review offers a comprehensive overview of the latest advancements in deep learning-based LPR technology and its potential applications.