Artificial intelligence and vehicle license plate recognition: A literature review - Martínez, Hernán Darío Enríquez; Insuasti, Jesus (Edelweiss Applied Science and Technology, 2025)
This study presents a systematic literature review on the application of artificial intelligence (AI) in vehicle license plate recognition, focusing on neural network-based technologies. The primary objective is to identify recent advancements that enhance traffic control automation and road safety. The research methodology involves a structured search and analysis of 90 significant publications selected from databases such as IEEE Xplore, ScienceDirect, Scopus, and DOAJ. Findings indicate that convolutional neural networks (CNNs) and deep learning models play a crucial role in improving recognition accuracy and efficiency, particularly through optimized image processing techniques and convolutional layers. However, challenges persist due to variations in license plate design and adverse environmental conditions affecting system performance. The study highlights the need for continued research on image preprocessing methods to enhance robustness and adaptability. The conclusions emphasize the critical role of AI-driven recognition systems in modern transportation infrastructure, advocating for further integration of advanced neural network architectures. From a practical perspective, these findings contribute to the development of more reliable and efficient vehicle identification systems, with implications for law enforcement, automated tolling, and smart city initiatives.