Abstract: In current situations many advancements have been made in License Plate (LP) recognition techniques to enhance accuracy, they are often limited to ideal scenarios with accurately annotated training data and restricted situations. Furthermore, monitoring systems frequently employ low-resolution (LR) images or videos. Our project focuses on addressing the challenge of LP detection in digital images captured in naturalistic environments. We aim to improve the quality of LP images by combining character segmentation and recognition with adversarial Super-Resolution (SR) methods. Specifically, we utilize the SRGAN approach, which is capable of processing tiny 72 × 72 LP images. This research investigates the performance of SRGAN across various aspects, including peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). By leveraging these methodologies, we aim to overcome the limitations of existing LP recognition techniques, especially in scenarios where training data is not perfectly annotated and LR images are prevalent. Our approach integrates character segmentation and recognition with SRGAN-based super-resolution techniques to enhance the quality and accuracy of LP images.