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Automatic License Plate Recognition Using Local Binary Pattern and Histogram Matching

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Intelligent Computing Theories and Application (ICIC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10362))

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Abstract

This paper proposes new real time license plate recognition (LPR) system that is capable of motion tracking and recognition of license plate. The best frame taken from the video has been chosen which is found to be about 4 m apart from camera position. For further processing, lower half section of vehicle image has been cropped of sized (450 × 140) while tracking. Local Binary Pattern (LBP) and histogram matching technique are used to detect license plate. Due to the robustness of LBP features, this method can adaptively deal with various changes such as rotation, scaling, and illumination in the license plate. Segmentation of the plate region into disjoint characters has been done with bounding box technique with some modifications. Recognition has been done by calculating histogram features. Minimum distance classifier has been used for features matching. The system is tested on more than 300 images and it gives 96.14% detection and 89.35% of recognition accuracy. This system is designed to recognize license plate of small, medium as well as large vehicles. It is also capable to detect single line and two line license plates format.

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References

  1. Barnich, O., Droogenbroeck, M.V.: Vibe: a universal background subtraction algorithm for video sequences. IN: IEEE Transactions on Image Processing, pp. 1709–1724 (2011)

    Google Scholar 

  2. Elgammal, A., Harwood, D., Davis, L.: Non-parametric Model for Background Subtraction. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 751–767. Springer, Heidelberg (2000). doi:10.1007/3-540-45053-X_48

    Chapter  Google Scholar 

  3. Subudhi, B.N., Nanda, P.K., Ghosh, A.: A change information based fast algorithm for video object detection and tracking. In: IEEE Transactions on Circuits and Systems for Video Technology, pp. 993–1004 (2011)

    Google Scholar 

  4. Chang, J.-K., Ryoo, S., Lim, H.: Real-time vehicle tracking mechanism with license plate recognition from road images. J. Supercomput. 65, 353–364 (2011). Springer

    Article  Google Scholar 

  5. Duan, T.D., Duc, D.A., Du, T.L.H.: Combining hough transform and contour algorithm for detecting vehicles’ license-plates. In: Proceedings International Symposium Intelligent Multimedia Video Speech Process, pp. 747–750 (2004)

    Google Scholar 

  6. Du, S., Ibrahim, M., Shehata, M., Badawy, W.: Automatic license plate recognition (ALPR): a state-of-the-art review. In: IEEE Transactions on Circuits and Systems for Video Technology, pp. 311–325 (2013)

    Google Scholar 

  7. Sirithinaphong, T., Chamnongthai, K.: The recognition of car license plate for automatic parking system. In: Proceedings 5th International Symposium on Signal Processing and its Applications, pp. 455–457 (1998)

    Google Scholar 

  8. Kahraman, F., Kurt, B., Gökmen, M.: License plate character segmentation based on the gabor transform and vector quantization. In: Yazıcı, A., Şener, C. (eds.) ISCIS 2003. LNCS, vol. 2869, pp. 381–388. Springer, Heidelberg (2003). doi:10.1007/978-3-540-39737-3_48

    Chapter  Google Scholar 

  9. Yousef, K.M.A., Al-Tabanjah, M., Hudaib, E., Ikrai, M.: SIFT based automatic number plate recognition. In: 6th International Conference on Information and Communication Systems (ICICS), Amman, pp. 124–129, April 2015

    Google Scholar 

  10. Gorovyi, I.M., Smirnov, I.O.: Robust number plate detector based on stroke width transform and neural network. In: IEEE Conference on Signal Processing Symposium, Debe, pp. 1–4, June 2015

    Google Scholar 

  11. Ghosh, A.K., Sharma, K.D., Islam, Md.N., Biswas, S., Akter, S.: Automatic License Plate Recognition (ALPR) for bangladeshi vehicles. In: Global Journal of Computer Science and Technology, pp. 68–73, December 2011

    Google Scholar 

  12. Wang, Y., Zhang, H., Fang, X., Guo, J.: Low-resolution chinese character recognition of vehicle license plate based on ALBP and gabor filters. In: Seventh International Conference on Advances in Pattern Recognition, ICAPR 2009, Beijing, pp. 302–305, February 2009

    Google Scholar 

  13. Pan, X., Ye, X., Zhang, S.: A hybrid method for robust car plate character recognition. In: IEEE International Conference on Systems, Man and Cybernetics, pp. 4733–4737 (2004)

    Google Scholar 

  14. Zhang, H., et al.: Learning-based license plate detection using global and local features. In: International Conference on Pattern Recognition, Hong Kong, China, pp. 1102–1105 (2006)

    Google Scholar 

  15. Wang, Y.R., Lin, W.H., Horng, S.J.: A sliding window technique for efficient license plate localization based on discrete wavelet transform. Expert Syst. Appl. 38, 3142–3146 (2011)

    Article  Google Scholar 

  16. Kanayama, K., Fujikawa, Y., Fujimoto, K., Horino, M.: Development of vehicle-license number recognition system using real-time image processing and its application to travel-time measurement. In: Proceedings IEEE Conference on Vehicular Technology, pp. 798–804, May 1991

    Google Scholar 

  17. Zhang, S., Zhang, M., Ye, X.: Car plate character extraction under complicated environment. In: Proceedings IEEE International Conference on System Man Cybern, vol. 5, pp. 4722–4726 October 2004

    Google Scholar 

  18. Nijhuis, J.A.G., Brugge, M.H.T., Helmholt, K.A., Pluim, J.P.W., Spaanenburg, L., Venema, R.S., Westenberg, M.A.: Car license plate recognition with neural networks and fuzzy logic. In: Proceedings IEEE International Conference on Neural Network, pp. 2232–223, 6 December 1995

    Google Scholar 

  19. Wang, S.Z., Lee, H.J.: A cascade framework for a real-time statistical plate recognition system. IEEE Trans. Inf. Forensics Secur., 267–282 (2007)

    Google Scholar 

  20. Jia, W., Zhang, H., He, X.: Region-based license plate detection. J. Netw. Comput. Applicat. 30(4), 1324–1333 (2007)

    Article  Google Scholar 

  21. Deb, K., Jo, K.-H.: A vehicle license plate detection method for intelligent transportation system applications. Int. J. Cyber Syst. 40(8), 689–705 (2009)

    Article  Google Scholar 

  22. O’Connor, B., Roy, K.: Facial recognition using modified local binary pattern and random forest. Int. J. Artif. Intell. Appl. (IJAIA) 4(6), 25–33 (2013)

    Google Scholar 

  23. Nigam, A., Krishna, V., Bendale, A., Gupta, P.: Iris recognition using block local binary patterns and relational measures. In: IEEE International Joint Conference on Biometric, pp. 1–6 (2014)

    Google Scholar 

  24. Ojala, T., Pietikainen, M.: A comparative study of texture measures with classification based on feature distributions. Pattern Recogn. 29(1), 51–59 (1996)

    Article  Google Scholar 

  25. Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. In: IEEE Transaction on Pattern Analysis and Machine Intelligence, pp. 1–9, May 2002

    Google Scholar 

  26. Ali, G., Nouar, O., Raphael, C.: Tracking system using camshift and feature points. In: 14th European signal processing conference, Florence, Italy, 4–8 September 2006

    Google Scholar 

  27. Gorai, A., Pal, R., Gupta, P.: Document fraud detection by ink analysis using texture features and histogram matching. In: IJCNN, pp. 4512–4517 (2016)

    Google Scholar 

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Authors and Affiliations

  1. National Institute of Technical Teachers’ Training & Research, Kolkata, 700106, India

    Ashutosh Kumar Bachchan, Apurba Gorai & Phalguni Gupta

Authors
  1. Ashutosh Kumar Bachchan
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  2. Apurba Gorai
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  3. Phalguni Gupta
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Corresponding author

Correspondence to Phalguni Gupta .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Universidad Distrital Francisco José de Caldas, Bogotá, Colombia

    Juan Carlos Figueroa-García

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Bachchan, A.K., Gorai, A., Gupta, P. (2017). Automatic License Plate Recognition Using Local Binary Pattern and Histogram Matching. In: Huang, DS., Jo, KH., Figueroa-García, J. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10362. Springer, Cham. https://doi.org/10.1007/978-3-319-63312-1_3

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  • DOI: https://doi.org/10.1007/978-3-319-63312-1_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63311-4

  • Online ISBN: 978-3-319-63312-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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