Efficient Two-Step Approach for Automatic Number Plate Detection

Ievgen Gorovyi

Abstract


intelligent transportation systems are rapidly growing mainly due to active development of novel hardware and software solutions. In the paper a problem of automatical number plate detection is considered. An efficient two-step approach based on plate candidates extraction with further classification by neural network is proposed. Stroke width transform and contours detection techniques are utilized for the image preprocessing and extraction of regions of interest. Different local feature sets are used for the final number plate extraction step. Efficiency of the developed method is tested with real datasets.

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A. Mukhtar, A. L. Xia, T.B. Tang, Vehicle Detection Techniques for Collision Avoidance Systems: A Review, IEEE Transactions on Intelligent Transportation Systems, Vol.16 ,No.5,2015, pp. 2318-2338.

J. Greenhalgh and M. Mirmehdi, Recognizing Text-Based Traffic Signs, IEEE Transactions on Intelligent Transportation Systems, Vol.16 ,No.3, 2015, pp. 1360-1369.

L. Yisheng et al, Traffic Flow Prediction With Big Data: A Deep Learning Approach, IEEE Transactions on Intelligent Transportation Systems, Vol.16 ,No.2,2015, pp. 865-873.

S. Du, M. Ibrahim, M.Shehata and W. Badawy, “Automatic License Plate Recognition (ANPR): A state of the art review”, IEEE Trans. On Circuits and Systems for Video Technology, Vol. 23, Issue 2, pp.311-325, 2013.

H. Bai and C.Liu, “A hybrid license plate extraction method based on edge statistics and morphology”, Int.. Conf. Patt. Recog., Vol. 2, pp. 831-834, 2004.

S.L. Chang, L.S. Chen, Y.C. Chung and S.W. Chen, “Automatic license plate recognition”, IEEE Trans. On Intelligent Transportation Systems, Vol. 5, No. 1, 2004.

D.Zheng, D. Zhao, J. Wang, An efficient method of license plate location, Pattern Recognition Letters, Vol. 26, 2005, pp. 2431-2438.

V. Kamat and S.Ganesan, “An efficient implementation of the Hough transform for detecting vehicle license plates using DSPs”, Real-Time Tech. and App. Symp, pp. 58-59, 1995.

P. Wu et al, “License plate extraction in low resolution videos”, Pattern Recog., Vol. 1, pp. 824-827, 2006.

B. Epshtein, Y. Wexler and E. Ofek, “Detecting Text in Natural Scenes with Stroke Width Transform”, IEEE International Conference on Computer Vision and Pattern Recognition, 2010, pp.2963 – 2970.

Q.Ye and D. Doermann, Text detection and recognition in imagery: A survey, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 37, No. 7, 2015, pp. 1480-1500.

J. Canny, “A computational approach to edge detection”, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 8, No. 6, pp. 679-698, 1986.

G.Bradsky and A.Kaehler, Learning OpenCV, O’Really Media Inc., 2008.

S. Suzuki and K. Abe, “Topological structural analysis of digitized binary images by border following”, Computer vision, graphics and image processing, Vol. 30, pp. 32-46, 1985.

I.T. Jolliffe, Principal Component Analysis, 2n edition, Springer, 2002.

P. Viola and M. Jones, “Rapid object detection using a boosted cascade of simple features”, IEEE International Conference on Computer Vision and Pattern Recognition, pp. 511-518, 2001.

S. Haykin, Neural networks: A Comprehensive Foundation, 2nd edition, Pearson Prentice Hall, 823p., 1999.

T. Fawcett, “An introduction to ROC analysis”, Pattern Recognition Letters, Vol. 27, pp. 861-874, 2006.

I. Gorovyi and I. Smirnow, Robust number plate detector based on stroke width transform and neural network, Proceedings of Signal Processing Symposium (SPSympo-2015), Debe, Poland, 2015, pp.163-166.


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