Recognition of Vehicle License Plates Based on Image Processing

• Main Authors: Tae-Gu Kim, Byoung-Ju Yun, Tae-Hun Kim, Jae-Young Lee, Kil-Houm Park, Yoosoo Jeong, Hyun Deok Kim
• Format: Article
• Language: English
• Published: MDPI AG 2021-07-01
• Series: Applied Sciences
• Subjects: deep learning; license plate detection; image processing; SRGAN; CCTV image
• Online Access: https://www.mdpi.com/2076-3417/11/14/6292

In this study, we have proposed an algorithm that solves the problems which occur during the recognition of a vehicle license plate through closed-circuit television (CCTV) by using a deep learning model trained with a general database. The deep learning model which is commonly used suffers with a disadvantage of low recognition rate in the tilted and low-resolution images, as it is trained with images acquired from the front of the license plate. Furthermore, the vehicle images acquired by using CCTV have issues such as limitation of resolution and perspective distortion. Such factors make it difficult to apply the commonly used deep learning model. To improve the recognition rate, an algorithm which is a combination of the super-resolution generative adversarial network (SRGAN) model, and the perspective distortion correction algorithm is proposed in this paper. The accuracy of the proposed algorithm was verified with a character recognition algorithm YOLO v2, and the recognition rate of the vehicle license plate image was improved 8.8% from the original images.