Recognition of Vehicle License Plates Based on Image Processing

Abstract

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.

Detection and Recognition of Car Plates in Parking Lots at Baghdad University

“…The suggested system performance was almost 92%. Tae-Gu Kim et al (2021) [13] used closed-circuit television (CCTV) data to recognize car license plates depending on deep learning model training. Unfortunately, this model has a disadvantage with recognition in terms of low resolution and false results with a tilted car license plate.…”

Applying Ternion Stream DCNN for Real-Time Vehicle Re-Identification and Tracking across Multiple Non-Overlapping Cameras

“…As a result, many applications have emerged to improve traffic management, focusing only on vehicle counting in urban streets [ 4 ]. However, plain vehicle counting was proven not to be sufficient for locating and distinguishing between vehicle types or models [ 5 ]. Then vehicle localization and identification [ 6 ] have become an area of interest in the computer vision community for solving vehicle-related criminal activities such as theft in urban areas [ 6 ].…”

“…Then vehicle localization and identification [ 6 ] have become an area of interest in the computer vision community for solving vehicle-related criminal activities such as theft in urban areas [ 6 ]. Additionally, numerous research projects were conducted to solve various environmental challenges in vehicle detection, re-identification, and tracking across multiple camera views [ 5 , 7 ]. However, most of these proposed algorithms are observed offline and are not ideal for real-time tracking [ 8 ].…”

An Evaluation of Various Pre-trained Optical Character Recognition Models for Complex License Plates

“…A modified lightweight YOLOv2 model was used for End-To-End license plate character detection and recognition without segmentation [8]. Additionally, a super-resolution generative adversarial network (SRGAN) model was explored to overcome the low recognition rate of the CCTV [9]. They applied distortion-correction algorithm for the misrecognized characters to enhance the recognition rate [9].…”

“…Additionally, a super-resolution generative adversarial network (SRGAN) model was explored to overcome the low recognition rate of the CCTV [9]. They applied distortion-correction algorithm for the misrecognized characters to enhance the recognition rate [9]. They used YOLO2 model for character recognition.…”