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http://dx.doi.org/10.7471/ikeee.2019.23.4.1337

Korean License Plate Recognition Using CNN  

Hieu, Tang Quang (Dept. of E. E. Engineering, Hongik University)
Yeon, Seungho (Dept. of E. E. Engineering, Hongik University)
Kim, Jaemin (Dept. of E. E. Engineering, Hongik University)
Publication Information
Journal of IKEEE / v.23, no.4, 2019 , pp. 1337-1342 More about this Journal
Abstract
The Automatic Korean license plate recognition (AKLPR) is used in many fields. For many applications, high recognition rate and fast processing speed of ALPR are important. Recent advances in deep learning have improved the accuracy and speed of object detection and recognition, and CNN (Convolutional Neural Network) has been applied to ALPR. The ALPR is divided into the stage of detecting the LP region and the stage of detecting and recognizing the character in the LP region, and each step is implemented with separate CNN. In this paper, we propose a single stage CNN architecture to recognize license plate characters at high speed while keeping high recognition rate.
Keywords
Korean license plate recognition; Deep Learning; CNNs; Detection; Recognition;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 S. Ren, K. He, R. Girshick, and J. Sun, "Faster R-CNN: Towards Real-time Object Detection with Region Proposal Networks," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.39, No.6, pp.1137-1149, 2017. DOI: 10.1109/TPAMI.2016.2577031   DOI
2 J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You only Look Once: Unified, Real-time Object Detection," Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, pp.779-788, 2016. DOI: 10.1109/CVPR.2016.91
3 W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.Y. Fu, et al., "Ssd: Single Shot Multibox Detector," Proceeding of European Conference on Computer Vision, pp.21-37, 2016. DOI: 10.1007/978-3-319-46448-0_2
4 J. Redmon and A. Farhadi, "Yolo9000: Better, Faster, Stronger," Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, pp.6517-6525, 2017. DOI: 10.1109/CVPR.2017.690
5 K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition," Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, pp.770-778, 2016. DOI: 10.1109/CVPR.2016.90
6 T. Lin, P. Goyal, R. Girshick, K. He, and P. Dollar, "Focal Loss for Dense Object Detection," Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, pp.2999-3007, 2017.
7 Q. H. Tang, S. Yeon, and J. Kim, "Deep Learning based Object Detector for Vehicle Recognition on Images Acquired with Fisheye Lens Camera," Journal of Korea Multimedia Society, Vol.22, No.2, pp.128-135, 2018. DOI: 10.9717/kmms.2019.22.2.128   DOI
8 L. Rayson,, "YOLOv3(2018)", https://pjreddie.com/publications/
9 L. Rayson, E. Severo, L. A. Oliveira, G. R. Goncalves, W. R. Schwartz, and D. Menotti, "A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector," 2018 International Joint Conference on Neural Networks (IJCNN), pp.1-10, 2018. DOI: 10.1109/IJCNN.2018.8489629