A GAN-based Framework for License Plate Recognition using YOLOv8 and EasyOCR

This paper introduces a new and novel technique for license plate recognition by leveraging the assistance of GANs (Generative Adversarial Networks) in data augmentation alongside YOLOv8-based object detection combined with EasyOCR text recognition. Conventional systems have always struggled due to scarcity of training data, variability in designs achievable within license plates, and environmental variations. All of these impact the overall performance of such systems in the real world. The suggested model can leverage its large pool of high-quality synthetic images to provide better accuracy and generalization compared to the current approaches-both detection and recognition tasks. This is a colossal leap forward in smart transport systems and hence an advanced step in intelligent monitoring, traffic control, and automated parking systems.