Supervised Domain Adaptation from Scene Text Recognition for Licence Plate Recognition

License Plate Recognition (LPR) has been extensively studied in the fields of computer vision and pattern recognition, driven by the need for automated traffic surveillance and law enforcement. In this paper, we propose a license plate recognition using Supervised Domain Adaptation (SDA) of Scene Text Recognition framework. The proposed approach combines a CNN-based feature extraction backbone with BiLSTM sequence modeling, originally optimized for broad scene text recognition. We then adapt the pretrained model to license plate data under supervision, ensuring it can reliably capture the unique patterns and character arrangements found on vehicle plates. Furthermore, we analyze the impact of selectively freezing and finetuning specific stages, such as the transformation, feature extraction, sequence modeling, and prediction layers, to highlight the optimal adaptation scheme. The findings underscore the importance of domain-aware architectural choices and provide a practical pathway for deploying automated license plates recognition systems in resource-constrained environments, particularly where labeled data is limited.