License plate recognition systems are necessary for a wide range of tasks, including law enforcement, surveillance, and toll booth operations, due to the explosive rise in the number of vehicles in use. In current traffic management systems, people violate rules by jumping traffic light signals and over speeding. Our aim is to create a license plate recognition solution to detect the license number and notify the service advisor with the vehicle’s information. By integrating these functionalities, we can report defaulters and speed up the check-in process for enhance and smoother transit. This proposed solution is achieved by using GANs. The Generator accept arbitrary long sequence of geometrically registered license plate images and converts them into a high-resolution counterpart which would become the input for our Discriminator. The discriminator would try to distinguish ground truth images from the generated images which eventually helps better train the Generator model.