# Recognition of Vehicle License Plates Based on Image Processing
## 1 Introduction  
Artificial intelligence (AI) technology is a branch of computer science that includes machine learning (ML) and deep learning (DL). AI can be perceived as using any device to imitate the human cognitive processes such as learning, applying, and solving complex problems, etc. [1]. The AI techniques are well suited to imaging-based fields, as an image is the main source of data for training the AI algorithms. ML is dedicated to deploying algorithms. ML algorithms could be viewed as mapping the observed input data variables into the output results [2]. DL is an artificial network system that simulates the concept of human neurons. These techniques have achieved impressive progress in many areas [1, 2]. With the development of DL, research on various object recognition methods is being actively conducted, and the vehicle license plate recognition field is one such method. Vehicle license plate recognition is frequently used for parking management and speed limit enforcement systems. This technology prevents car-related accidents and crimes. The closed-circuit television (CCTV) cameras installed on roads or buildings are used to store and transmit videos and images. According to the statistics from the National Statistical Office of South Korea, about 1.2 million CCTVs are installed in South Korea as of 2019. However, the data acquired through this system is not being fully utilized. It requires much time and human resources to properly utilize and analyze the images captured by CCTV, which becomes a limiting factor in quick response to an accident or crime. This problem can be effectively addressed by deploying an automated method thatcan quickly detect information, such as the type, color, and license plate of the vehicle. The technology for recognizing a vehicle license plate uses an image obtained through CCTV, which is usually taken from a long distance and with a wide angle of view. This causes problems, such as a limitation of the resolution, motion blur, and perspective distortion due to various environmental changes and camera installation locations. It is desired to have an image processing technology that can accurately recognizes the characters smaller than the image size. With the development of image processing technology, studies have been conducted in areas such as object detection, tracking, and camera captured image recognition technologies [3]. For efficiently managing the vehicles information, research on vehicle license plate recognition is also being actively conducted [4; 5]. In general, the public license plate recognition model is trained based on the front license plate image. The model trained using this technique suffers with the issues of decreased recognition rate when the license plate is recognized in the vehicle image captured using CCTV [6; 7]. This is due to the fact that when a vehicle image is captured in a general environment using CCTV, the license plate is tilted, or recorded in low resolution. A common way to solve this problem is to develop a new database and retrain it to solve the recognition rate problem. However, this requires a high cost and time. To overcome this problem, we have proposed an image processing algorithm which can improve the license plate recognition rate by using the existing database. In CCTV images, the license plates usually appear to be tilted and have low-resolution that results in the lower recognition rate when the existing learning model is applied. To solve this problem, this paper proposed the resolution improvement method and the method for restoring a tilted image. The proposed technique consists of two steps that enhances the recognition rate of the license plate. In the first step, the deep learning-based super-resolution generative adversarial network (SRGAN) algorithm [8] is used to improve the lower resolution image of the license plate. In the second step, the perspective transformation technique is deployed on the tilted license plate image for the correction of perspective distortion.