License plate recognition system in unconstrained scenes via a new image correction scheme and improved CRNN

Abstract

Automatic license plate recognition plays an important role in vehicle management. The performance of current license plate recognition systems is significantly affected by large-angle deflection in unconstrained scenes. For addressing this issue, this paper develops a license plate recognition system via a new image correction scheme and an improved CRNN (Convolutional Recurrent Neural Network). In which, the yolov5l is firstly introduced for license plate detection, and it is trained by transfer learning in order to address insufficient data. Then, a new license plate correction scheme and AFF-Net (Adaptive Fusion Feature Segmentation Network) is proposed and applied, which uses the segmentation result of license plate and its original image area for perspective transformation to improve the correction effect. Finally, the channel attention mechanism is added to the CRNN model for license plate recognition, so that a single grid cell of the feature network can obtain more spatial information. The recognition accuracy of the developed license plate recognition system is 98.8 % on CTPFSD (China Temporary Parking Fee System Data), which is much higher than current mainstream license plate recognition systems. More importantly, the developed license plate recognition system has been deployed in the cloud and applied in outdoor parking toll in practice.