Joint License Plate Super-Resolution and Recognition in One Multi-Task Gan Framework

Abstract

License plate recognition (LPR) plays an important role in intelligent transport systems. The existed LPR systems are mostly based on hand-crafted methods for detection, segmentation, and recognition, which cannot accurately recognize the license plate in unconstrained surveillance environments. In this paper, we propose a Multi-Task Generative Adversarial Network (MTGAN) based LPR system, which combines the license plate super-resolution and recognition in one end-to-end framework. In the proposed MTGAN, we design a Fully Connected Network (FCN) as generative network (GN), which can combine knowledge from data distribution and domain prior knowledge of license plate to generate the spatial corresponding and high-resolution plate images in the synthesis pipeline. More important, a multi-task discriminative network is designed in MTGAN to combine the super-resolution and recognition in an adversarial manner to enhance each other. The experiments on the built real-world license plate dataset show that the proposed LPR system can generate high-resolution license plates as well as recognize them with higher accuracy than state-of-the-art LPR systems.