Style Reconstruction-Driven Networks for Occlusion-Aware License Plate Recognition

Deep learning-based automatic license plate recognition methods have made significant advancements and are now widely used in real-world applications. Currently, license plate character recognition primarily relies on classification and the Connectionist Temporal Classification approach. While these methods achieve high recognition accuracy, they face challenges in accurately estimating confidence in occlusion scenarios. To solve this problem, we propose a novel style reconstruction-based network that can transform input license plates into standardized images. It computes character prediction confidence using a lightweight matching module, which effectively reduces the confidence score for occluded sections. Our network ingeniously integrates deep learning with traditional character segmentation methods, offering a fresh perspective on license plate recognition. Besides, to address the class imbalance in existing license plate datasets, we propose a novel synthetic license plate generation method that exchanges styles between real and standardized license plates. In order to comprehensively evaluate license plate recognition models across different regions, we construct and release the Chinese Balanced License Plates (CBLP) dataset, which includes over 30,000 images from all provinces in mainland China. Experimental evaluations on multiple datasets demonstrate that our methods achieve state-of-the-art performance. The code and dataset are available at https://github.com/tj-cvrsg/lpsrnet.