Toward Unified End-to-End License Plate Detection and Recognition for Variable Resolution Requirements

Yilin Gao, Shiyi Mu, Shugong Xu

Abstract: In this paper, we present a new cascade architecture based on a differentiable sample module to satisfy the varied image resolution requirements of license plate detector and recognizer in end-to-end technologies. Based on this module, the network can detect license plates on downsampled low-resolution images and resample them from the original high-definition images to recognize the license plate numbers. Furthermore, since the optimization direction of the detector for the detection boxes and the input requirements of the recognizer are not consistent with each other, we introduce the Bias Detection Head, which decouples the two Bounding Boxes to circumvent this problem. In the meantime, a novel feature fusion module is presented, which simultaneously satisfies the fusion of multi-scale information and the interaction of two Bounding Box features. For the recognizer, we present a unified architecture based on a decoupled attention mechanism for recognizing single and double lines, varying lengths, and tilting on license plates.