Deep learning for license plate recognition: A comprehensive survey of datasets, methods, and future directions

Abstract

License plate recognition (LPR) is an important component of intelligent transportation systems. With the rapid development of deep learning, LPR has evolved from handcrafted pipelines toward data-driven detection and recognition models with substantially improved accuracy and efficiency. However, the progress reported in existing studies is often difficult to interpret clearly due to considerable differences in dataset splits, evaluation protocols, preprocessing strategies, task definitions, and hardware settings. This review examines LPR from a system-level perspective. We discuss dataset bias, limitations of benchmarks, different evaluation criteria, and the growing importance of restoration-aware LPR, including super-resolution for degraded license plate images.