Browse > Article
http://dx.doi.org/10.5370/KIEE.2016.65.1.142

An Ensemble Classifier Based Method to Select Optimal Image Features for License Plate Recognition  

Jo, Jae-Ho (School of Mechanical Engineering, Pusan National University)
Kang, Dong-Joong (School of Mechanical Engineering, Pusan National University)
Publication Information
The Transactions of The Korean Institute of Electrical Engineers / v.65, no.1, 2016 , pp. 142-149 More about this Journal
Abstract
This paper proposes a method to detect LP(License Plate) of vehicles in indoor and outdoor parking lots. In restricted environment, there are many conventional methods for detecting LP. But, it is difficult to detect LP in natural and complex scenes with background clutters because several patterns similar with text or LP always exist in complicated backgrounds. To verify the performance of LP text detection in natural images, we apply MB-LGP feature by combining with ensemble machine learning algorithm in purpose of selecting optimal features of small number in huge pool. The feature selection is performed by adaptive boosting algorithm that shows great performance in minimum false positive detection ratio and in computing time when combined with cascade approach. MSER is used to provide initial text regions of vehicle LP. Throughout the experiment using real images, the proposed method functions robustly extracting LP in natural scene as well as the controlled environment.
Keywords
License plate number extraction; Maximally stable extremal regions(MSER); Multi-block local gradient patterns(MB-LGP);
Citations & Related Records
연도 인용수 순위
  • Reference
1 Chang, S. L, Chen, L. S., Chung, Y. C. and Chen, S. W., "Automatic license plate recognition," IEEE Trans. Intelligent Transportation Systems 5, 1, 42-53, 2004.   DOI
2 J. A. G. Nijhuis, M. H. ter Brugge, K.A. Helmholt, J. P. W. Pluim, L. Spaanenburg, R. S. Venema, M. A. Westenberg, "Car License Plate Recognition with Neural Networks and Fuzzy Logic.," IEEE Int. Conf. Neural Networks, 5, 2232-2236, 1995.
3 Hontani, H. and Koga, "Character extraction method without prior knowledge on size and position information," IEEE Int. Conf. Vehicle Electronics, 67-72. Intel Company, http://www.intel.com/technology/computing/opencv, 2007.
4 K. Jung, K. I. Kim, and A. K. Jain, "Text information extraction in images and video: a survey," Pattern Recognition, 2004.
5 J. Liang, D. Doermann, and H. Li, "Camera-based analysis of text and documents: a survey," IJDAR, 2005.
6 A. Coates, B. Carpenter, C. Case, S. Satheesh, B. Suresh, T. Wang, D. Wu, and A. Ng, "Text detection and character recognition in scene images with unsupervised feature learning," in Proc. ICDAR, 2011.
7 T. Wang, D. J. Wu, A. Coates, and A. Y. Ng, "End-to-end text recognition with convolutional neural networks," in Proc. ICPR, 2012.
8 Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng, "Reading digits in natural images with unsupervised feature learning," NIPS Workshop on Deep Learning and Unsupervised Feature Learning, 2011.
9 K. Wang, B. Babenko, and S. Belongie, "End-to-end scene text recognition," in Proc. ICCV, 2011.
10 A. Mishra, K. Alahari, and C. Jawahar, "Top-down and bottom-up cues for scene text recognition," in Proc. CVPR, 2012.
11 C. Yao, X. Bai, W. Liu, Y. Ma, and Z. Tu, "Detecting texts of arbitrary orientations in natural images," in Proc. CVPR, 2012.
12 H. Chen, S. Tsai, G. Schroth, D. Chen, R. Grzeszczuk, and B. Girod, "Robust text detection in natural images with edge-enhanced maximally stable extremal regions," in Proc. ICIP, 2011.
13 T. Novikova, O. Barinova, P. Kohli, and V. Lempitsky, "Large-lexicon attribute-consistent text recognition in natural images," in Proc. ECCV, 2012.
14 L. Neumann and J. Matas, "Real-time scene text localization and recognition," in Proc. CVPR, 2012.
15 Y.-F. Pan, X. Hou, and C.-L. Liu, "Text localization in natural scene images based on conditional random field," in Proc. ICDAR, 2009.
16 J. Matas, O. Chum, M. Urban, and T. Pajdla, "Robust wide-baseline stereo from maximally stable extremal regions," Image and Vision Computing, 2004.
17 P. Viola, M. Jones, "Fast and robust classification using asymmetric adaboost and a detector cascade," in: Proceedings of Advances in Neural Information Processing System, pp. 1311-1318, 2001.
18 B. Jun, D. Kim, "Robust face detection using local gradient patterns and evidence accumulation," Pattern Recognition, pp. 3304-3316, 2012.
19 T. Ojala, M. Pietikainen, D. Harwood, "A comparative study of texture measures with classification based on feature distributions," Pattern Recognition pp. 51-59, 1996.
20 S. ZHOU, J YIN, "Robust Face Detection Using Multi-Block Local Gradient Patterns and Extreme Learning Machine," Extreme Learning Machines 2013: Algorithms and Applications, vol. 16, pp.81-94, 2014.
21 S. Brubaker, J. Wu, J. Sun, M. Mullin, J. Rehg, "On the design of cascades of boosted ensembles for face detection," International Journal of Computer Vision 77, pp. 65-86, 2008.   DOI
22 J. Matas, O. Chum, M. Urban, and T. Pajdla, "Robust wide baseline stereo from maximally stable extremal regions.," Proc. of British Machine Vision Conference, pages 384-396, 2002.
23 C. Shi, C. Wang, B. Xiao, Y. Zhang, and S. Gao, "Scene text detection using graph model built upon maximally stable extremal regions," Pattern Recognition Letters, vol.34, no.2, pp.107-116, 2013.   DOI
24 X. Yin, X.-C. Yin, H.-W. Hao, K. Iqbal, "Effective text localization in natural scene images with MSER, geometry-based grouping and AdaBoost," Proc. of ICPR, pp.725-728, 2012.
25 Y. Li, H. Lu, "Scene text detection via stroke width," Proc. of ICPR, pp.681-684, 2012.
26 C. Merino-Gracia, K. Lenc, M. Mirmehdi, "A head-mounted device for recognizing text in natural scenes," Proc. of CBDAR, pp.29-41, 2011.
27 H. Il Koo, D. H. Kim, "Scene text detection via connected component clustering and nontext filtering," IEEE Transactions on Image Processing, vol.22, no.6, pp.2296-2305, 2013.   DOI
28 L. Neumann, J. Matas, "Text localization in realworld images using efficiently pruned exhaustive search," Proc. of ICDAR, pp.687-691, 2011.