The paper "Automatic License Plate Recognition" by P. Pavan Raju, Soumyadipta Bose, Surampudi Sathwik, and Mrs. N. Sree Divya, published in the International Journal for Research in Applied Science & Engineering Technology, addresses the challenge of license plate detection in naturalistic environments. The authors aim to improve the quality of license plate images by combining character segmentation and recognition with adversarial Super-Resolution (SR) methods, specifically using the SRGAN approach. This method processes low-resolution (LR) images to generate high-resolution (HR) images, enhancing the accuracy of license plate recognition. The research evaluates the performance of SRGAN using metrics such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). The proposed system is designed to handle various environmental conditions and non-uniform license plate characteristics, making it more robust and reliable compared to existing systems. The paper also discusses related work, including the use of CNN-RNN and a unified neural network for license plate detection and recognition. The results demonstrate the system's effectiveness in real-world scenarios, offering significant advancements in traffic violation detection and enforcement.The paper "Automatic License Plate Recognition" by P. Pavan Raju, Soumyadipta Bose, Surampudi Sathwik, and Mrs. N. Sree Divya, published in the International Journal for Research in Applied Science & Engineering Technology, addresses the challenge of license plate detection in naturalistic environments. The authors aim to improve the quality of license plate images by combining character segmentation and recognition with adversarial Super-Resolution (SR) methods, specifically using the SRGAN approach. This method processes low-resolution (LR) images to generate high-resolution (HR) images, enhancing the accuracy of license plate recognition. The research evaluates the performance of SRGAN using metrics such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). The proposed system is designed to handle various environmental conditions and non-uniform license plate characteristics, making it more robust and reliable compared to existing systems. The paper also discusses related work, including the use of CNN-RNN and a unified neural network for license plate detection and recognition. The results demonstrate the system's effectiveness in real-world scenarios, offering significant advancements in traffic violation detection and enforcement.