Authors : Bhagyashri S. Patil , Nilesh Vani
DOI : 10.46335/IJIES.2025.10.6.11
Abstract –Number Plate Recognition (NPR) is a crucial application in intelligent transportation systems, law enforcement, and automated parking management. The advancements in deep learning and computer vision have significantly improved the accuracy of NPR systems. This review paper explores various techniques used for number plate detection and character recognition, emphasizing the role of Python libraries such as OpenCV, YOLO, and Tesseract OCR. The paper highlights recent research, discusses the challenges faced in real-world applications, and suggests future directions for improving system accuracy, robustness, and deployment efficiency. It also examines the impact of different image processing techniques, edge AI solutions, and the role of cloud computing in NPR systems. Furthermore, this review provides insights into the latest trends in artificial intelligence (AI) and machine learning (ML) applications for NPR, addressing limitations in existing models and highlighting potential breakthroughs.
