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http://dx.doi.org/10.9708/jksci.2022.27.04.019

A Robust Real-Time License Plate Recognition System Using Anchor-Free Method and Convolutional Neural Network  

Kim, Dae-Hoon (Dept. of Computer Science and Engineering, Kangwon National University)
Kim, Do-Hyeon (Department of Research and Development, ZIOVISION Co. Ltd)
Lee, Dong-Hoon (Department of Research and Development, ZIOVISION Co. Ltd)
Kim, Yoon (Dept. of Computer Science and Engineering, Kangwon National University)
Publication Information
Journal of the Korea Society of Computer and Information / v.27, no.4, 2022 , pp. 19-26 More about this Journal
Abstract
With the recent development of intelligent transportation systems, car license plate recognition systems are being used in various fields. Such systems need to guarantee real-time performance to recognize the license plate of a driving car. Also, they should keep a high recognition rate even in problematic situations such as small license plates in low-resolution and unclear image due to distortion. In this paper, we propose a real-time car license plate recognition system that improved processing speed using object detection algorithm based on anchor-free method and text recognition algorithm based on Convolutional Neural Network(CNN). In addition, we used Spatial Transformer Network to increase the recognition rate on the low resolution or distorted images. We confirm that the proposed system is faster than previously existing car license plate recognition systems and maintains a high recognition rate in a variety of environment and quality images because the proposed system's recognition rate is 93.769% and the processing speed per image is about 0.006 seconds.
Keywords
License Plate Recognition; Real-Time; Anchor-Free Method; CNN; Deep Learning;
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Times Cited By KSCI : 5  (Citation Analysis)
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