This paper presents a novel approach to enhance the accuracy and quality of license plate (LP) recognition in naturalistic environments. The research focuses on improving LP detection and recognition by integrating character segmentation and recognition with adversarial super-resolution (SR) methods, specifically using the SRGAN approach. The SRGAN is capable of processing low-resolution (LR) LP images, which are commonly encountered in real-world scenarios. The study evaluates the performance of SRGAN using metrics such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). The proposed system aims to overcome the limitations of existing LP recognition techniques, especially in scenarios with imperfectly annotated training data and LR images. The system combines SRGAN-based super-resolution techniques with character segmentation and recognition to enhance the quality and accuracy of LP images. The paper also discusses related work, including the use of CNN-RNN and ALPRNet for LP detection and recognition. The methodology involves a proposed architecture that integrates SRGAN and YOLO for LP detection and recognition. The SRGAN is used to enhance the resolution and quality of LP images, while YOLO is employed for real-time object detection. The results show that the proposed system significantly improves the accuracy of LP recognition in various environmental conditions. The system is designed to be scalable, automated, and efficient, making it suitable for real-world applications. The study concludes that the proposed system offers a promising solution for traffic violation detection and enforcement, contributing to enhanced road safety and improved traffic management.This paper presents a novel approach to enhance the accuracy and quality of license plate (LP) recognition in naturalistic environments. The research focuses on improving LP detection and recognition by integrating character segmentation and recognition with adversarial super-resolution (SR) methods, specifically using the SRGAN approach. The SRGAN is capable of processing low-resolution (LR) LP images, which are commonly encountered in real-world scenarios. The study evaluates the performance of SRGAN using metrics such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). The proposed system aims to overcome the limitations of existing LP recognition techniques, especially in scenarios with imperfectly annotated training data and LR images. The system combines SRGAN-based super-resolution techniques with character segmentation and recognition to enhance the quality and accuracy of LP images. The paper also discusses related work, including the use of CNN-RNN and ALPRNet for LP detection and recognition. The methodology involves a proposed architecture that integrates SRGAN and YOLO for LP detection and recognition. The SRGAN is used to enhance the resolution and quality of LP images, while YOLO is employed for real-time object detection. The results show that the proposed system significantly improves the accuracy of LP recognition in various environmental conditions. The system is designed to be scalable, automated, and efficient, making it suitable for real-world applications. The study concludes that the proposed system offers a promising solution for traffic violation detection and enforcement, contributing to enhanced road safety and improved traffic management.