Browse > Article
http://dx.doi.org/10.5391/JKIIS.2016.26.5.423

An Improved License Plate Recognition Technique in Outdoor Image  

Kim, Byeong-jun (Department of Computer Science and Engineering, Chonbuk National University)
Kim, Dong-hoon (Department of Computer Science and Engineering, Chonbuk National University)
Lee, Joonwhoan (Department of Computer Science and Engineering, Chonbuk National University)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.26, no.5, 2016 , pp. 423-431 More about this Journal
Abstract
In general LPR(License Plate Recognition) in outdoor image is not so simple differently from in the image captured from manmade environment, because of geometric shape distortion and large illumination changes. this paper proposes three techniques for LPR in outdoor images captured from CCTV. At first, a serially connected multi-stage Adaboost LP detector is proposed, in which different complementary features are used. In the proposed detector the performance is increased by the Haar-like Adaboost LP detector consecutively connected to the MB-LBP based one in serial manner. In addition the technique is proposed that makes image processing easy by the prior determination of LP type, after correction of geometric distortion of LP image. The technique is more efficient than the processing the whole LP image without knowledge of LP type in that we can take the appropriate color to gray conversion, accurate location for separation of text/numeric character sub-images, and proper parameter selection for image processing. In the proposed technique we use DBN(Deep Belief Network) to achieve a robust character recognition against stroke loss and geometric distortion like slant due to the incomplete image processing.
Keywords
Outdoor images; MB-LBP; Haar-like; Multi-stage Adaboost; DBN(Deep Belief Network);
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 X. H. Huaifeng Zhang, Wenjing Jia and Q. Wu. "Learningbased license plate detection using global and local features," 18th International Conference on Pattern Recognition, vol. 02, pp. 1102-1105, 2006.
2 Moon-Yong Jin, "Real-Time Vehicle License Plate Recognition System Using Adaptive Heuristic Segmentation Algorithm", Chonbuk National University, 2013.
3 Byung-Gil Han, Jong Taek Lee, Kil-Taek Lim, and Yunsu Chung, "Real-time License Plate Detection in High- Resolution Videos using Fastest Cascade Classifier and Core Patterns," Available http://dx.doi.org/10.4218/etrij.15.2314.0077, [Accessed : July 17, 2016]   DOI
4 Tran Duc Duan, Tran Le Hong Du, Tran Vinh Phuoc, Nguyen Viet Hoang, "Building an Automatic Vehicle License-Plate Recognition System,"Intl. Conf. in Computer Science, vol. 05, pp. 21-24, 2005,
5 Wenjing Jia, Huaifeng Zhang, Xiang-jian He, "Region-based license plate detection", Journal of Network and Computer Applications, vol.30, pp.1324-1333, 2007.   DOI
6 S.-H. Y. Jun-Wei Hsieh and Y.-S. Chen. "Morphology-based license plate detection from complex scenes," the International Conference on Pattern Recognition, vol. 16, pp. 176-180, 2002.
7 Jin-Ho Kim, "Vehicle License Plate Recognition for Smart Tolling by Selective Sharpening", The Journal of the Korea Contents Association, vol. 14, no. 12, pp. 1-9, 2014.
8 Jae-Ho Kim, Dong-Jung Kang, "An Ensemble Classifier Based Method to Select Optimal Image Features for License Plate Recognition", KIEE international transactions on power engineering, vol. 65,no. 1, pp.142-149, 2016.
9 Seon-Hwan Kim, Sung-Kwon Oh, "RBFNNs-based Recognition System of Vehicle License Plate Using Distortion Correction and Local Binarization", KIEE international transactions on power engineering, vol. 65, no. 9, pp.1531-1540, 2016.
10 GE Hinton, S Osindero, YW The, "A fast learning algorithm for deep belief network", Neural computation MIT Press, 2006.
11 JAK Suykens, J Vandewalle, "Least squares support vector machine classifiers", Neural processing letters, 1999.
12 Dong-Hoon Kim, Byeong-Jun Kim, Joonwhoan-Lee "A Cascaded Detector for Vehicle Number Plate from Outdoor Images", The Korean Institute of Communications and Information Sciences, vol. 11, pp.71-72, 2015.
13 Hungwen Li, Mark A Lavin, Ronald J Le Master, "Fast Hough transform: A hierarchical approach", Computer Vision, Graphics, and Image Processing, vol.36, pp.139-161, 1986.   DOI
14 M Welling, "Fisher linear discriminant analysis", Department of Computer Science, University of Toronto, 2005.
15 Shaoqing Ren, Kaiming He, Ross Girshock, Jian Sun, "Faster R-CNN: Toward Real-Time Object Detection with Region Proposal Networks", Advances in Neural Information Processing Systems, vol. 28. 2015.