Deep learning algorithms for license plate recognition: A review

License plate recognition serves as a crucial link in the intelligent transportation system and vehicle management. It lies at the heart of enhancing road supervision efficiency and enabling automated services. However, the rapid advancement in this field has resulted in numerous parallel technological pathways, disparate performance assessment standards, and ambiguous future trends. Consequently, there is a pressing need to clarify the technological evolution, evaluate the efficacy and limitations of various methods in complex scenarios, and thereby identify future research directions. To address this gap, we present a systematic review and analysis of the field. Deep learning technology applied in this domain overcomes the limitations of traditional image-processing methods in complex scenarios. This application notably boosts the robustness of license plate localization and character recognition, attracting extensive attention from academia and industry in recent years. In this paper, we first collate and present the public mainstream license plate image datasets and their characteristics. Subsequently, we systematically summarize the evolution of license plate detection and recognition technology. It ranges from traditional methods like edge detection and morphological segmentation based on feature engineering to end-to-end detection frameworks founded on deep learning and character recognition models. We compare and analyze the innovation aspects and application limitations of each algorithm. Through in-depth exploration of the field literature, we identify that the main challenges remain illumination intensity variation, extreme weather interference, multi-angle tilting, non-standard license plate designs, low-resolution images, and character adhesion. The research further indicates that constructing lightweight networks integrating multi-scale features, developing cross-regional license plate universal models, and incorporating traffic-scene prior knowledge into optimization algorithms will be important development directions. Meanwhile, cross-modal methods such as video-stream timing analysis and multi-sensor data fusion are being explored, which are expected to enhance real-time recognition performance in dynamic scenes. This paper offers a theoretical reference for the optimization and innovation of license plate recognition technology in the intelligent transportation system.