Research on license plate recognition based on graphically supervised signal-assisted training

Experiments show that compared with LPRNet, this study improves the accuracy in all test sets of the Chinese City Parking Dataset (CCPD) dataset, where the average accuracy is improved by 5.86%, the maximum accuracy by 10.9%, the average character precision by 2.1%, and the average recall by 6.9%, indicating that this study can achieve higher accuracy while keeping it lightweight. This study also provides new ideas for other deep learning image recognition tasks.