Diving Deep: A Survey of Deep Learning Techniques for Anomaly Detection in Automatic Vehicle Number Plate Recognition Systems

Automatic Vehicle Number Plate Recognition (AVNPR) has witnessed a transformative journey from rule-based methods to deep learning techniques, reshaping its efficacy in diverse applications. This comprehensive review outlines the application of CNNs and RNNs in key aspects of AVNPR, including license plate localization, character segmentation, and recognition. The importance of high-quality datasets in training these models is emphasized. While deep learning has greatly enhanced accuracy, challenges such as computational intensity and privacy concerns require careful consideration. This paper gives the roadmap for researchers, practitioners, and policymakers, delineating the current landscape and ethical considerations. AVNPR technology, which identifies vehicles through their number plates, faces challenges related to non-standardized formats, complex scenes, and environmental conditions, necessitating additional hardware for optimal deployment despite the use of advanced algorithms. Beyond traditional plate recognition, this system integrates anomaly detection to enhance its capabilities in diverse real-world scenarios. Incorporating anomaly detection techniques allows the system to identify and report irregularities, outliers, and unexpected events, ensuring heightened accuracy and reliability. This research aims to pay attention to the deep learning algorithms by reviewing prior work, analyzing extraction, segmentation, and recognition techniques, and offering insights into upcoming trends in this domain.