ABSTRACT License plate recognition (LPR)—also widely referred to as automatic license plate recognition (ALPR)—is a critical component of intelligent transportation systems. Despite substantial accuracy improvements driven by deep learning, reliable LPR deployment in real‐world environments remains challenging due to adverse imaging conditions, domain shifts and practical system constraints. This survey focuses on hybrid deep learning frameworks integrating convolutional and attention‐based models. Distinct from existing surveys, this review emphasises the joint design and evaluation of detection–recognition pipelines analysing how architectural coupling affects robustness and deployability. A unified overview of the LPR pipeline is presented, covering detection, recognition, datasets and deployment considerations. Existing methods are categorised into CNN‐based, transformer‐based and hybrid approaches. For detection, the evolution from convolutional detectors to CNN–transformer hybrids is analysed, highlighting trade‐offs among accuracy, robustness and real‐time performance. For recognition, sequence modelling paradigms from CTC‐based methods to attention‐driven Seq2Seq and transformer architectures are reviewed. In addition, public benchmarks and domain generalisation strategies are examined revealing persistent limitations such as regional bias. A consolidated benchmark comparison across representative architectures is provided to facilitate quantitative assessment. Finally, a holistic evaluation perspective is advocated, and future research directions towards robust and deployable LPR systems are outlined.
Deng et al. (Thu,) studied this question.