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Chord-Length Shape Features for License Plate Character Recognition

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Journal of Russian Laser Research Aims and scope

Abstract

Despite their recognized merits in terms of discrimination, compactness, and robustness, chord-length shape features have not received a great deal of attention in the literature on license plate recognition. In this paper, we present an innovative k nearest neighbors (kNN) approach for license plate detection and recognition, where a new low-dimensional descriptor that incorporates shape information of plate characters is formed from a finite set of established 1D chord-length signatures. When evaluated on a dataset incorporating a relatively large and diverse collection of plate image data, the proposed approach delivers promising results that compare favorably with those reported in the literature, without sacrificing computational efficiency or stability.

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References

  1. E. Rashedi and H. Nezamabadi-Pour, Multimed. Tools Appl., 77, 2771 (2018).

    Article  Google Scholar 

  2. Y. Liu, H. Huang, J. Cao, and T. Huang, Soft Comput., 22, 24032419 (2018).

    Google Scholar 

  3. S. Bakheet, J. Russ. Laser Res., 38, 61 (2017).

    Article  Google Scholar 

  4. N. Soora and P. Deshpande, IETE J. Res., 64, 280295 (2018).

    Article  Google Scholar 

  5. C.-N. E. Anagnostopoulos, I. E. Anagnostopoulos, I. D. Psoroulas, et al., IEEE Trans. Intell. Transport. Syst., 9, 377 (2008).

    Article  Google Scholar 

  6. M. Garg and S. Goel, ITSI Trans. Elect. Electron. Eng. (ITSI-TEEE), 1, no. 5 (2013).

  7. J. Wang, B. Bacic, and W. Yan, Multimed. Tools Appl., 77, 16791692 (2018).

    Google Scholar 

  8. T. Bj¨orklund, A. Fiandrotti, M. Annarumma, et al., “Automatic license plate recognition with convolutional neural networks trained on synthetic data,” in: IEEE 19th International Workshop on Multimedia Signal Processing (MMSP), Luton (2017), p. 1.

  9. H. Kwa´snicka and B. Wawrzyniak, License plate localization and recognition in camera pictures, November 2002.

  10. F. Ullah, Sensors, 19, no. 3015, 1 (2019).

    Google Scholar 

  11. X. Shi, W. Zhao, and Y. Shen, “Automatic license plate recognition system based on color image processing,” in: Proceedings of the 2005 International Conference on Computational Science and Its Applications, Springer-Verlag (2005), p. 1159.

  12. P. Comelli, P. Ferragina, M. N. Granieri, and F. Stabile, IEEE Trans. Vehicular Technol., 44, 790 (1995).

    Article  Google Scholar 

  13. T. Chen, “License plate text localization using dwt and neural network,” in: IEEE International Conference on Granular Computing, August 2009, GRC’09, p. 73.

  14. S. N. H. S. Abdullah, “License plate recognition using multilayer neural networks,” in: International Conference on Computing Informatics, June 2006, p. 1.

  15. H. Caner, H. S. Gecim, and A. Z. Alkar, IEEE Trans. Vehicular Technol., 57, 2675 (2008).

    Article  Google Scholar 

  16. B. Ron and J. Erez, “A real-time vehicle license plate recognition (lpr) system,” Vision and Image Sciences Lab., IIT Winter Project (2002).

    Google Scholar 

  17. F. Kahraman, B. Kurt, and M. G¨okmen, “License plate character segmentation based on the Gabor transform and vector quantization,” in: ISCIS Proc. (2003), p. 381.

  18. V. Laxmi and H. Rohil, Int. J. Comput. Appl., 99, 29 (2013).

    Google Scholar 

  19. C.-M. Wang and J.-H. Liu, “License plate recognition system,” in: 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), August 2015, p. 1708.

  20. P. Mendes, J. Neves, A. Tavares, and D. Menotti, “Towards an automatic vehicle access control system: License plate location,” in: Systems, Man, and Cybernetics IEEE International Conference, 2011, p. 2916.

  21. N. U. Khan, K. V. Arya, and M. Pattanaik, “An efficient image noise removal and enhancement method,” in: Systems, Man, and Cybernetics IEEE International Conference, 2010, p. 3735.

  22. U. Pal and B. Chaudhuri, Pattern Recognition, 37, 1887 (2004).

    Article  Google Scholar 

  23. S. Sadek, A. Al-Hamadi, B. Michaelis, and U. Sayed, “An SVM approach for activity recognition based on chord-length-function shape features,” in: IEEE International Conference on Image Processing (ICIP’12), Florida, U.S.A., October 2012, p. 767.

  24. S. Sadek, B. Michaelis, A. Al-Hamadi, and U. Sayed, J. ISRN Machine Vision, 1, 1 (2012).

    Google Scholar 

  25. S. Sadek, A. Al-Hamadi, B. Michaelis, and U. Sayed, EURASIP J. Adv. Signal Process., Vol. 2011, Article ID 540375, doi:https://doi.org/10.1155/2011/540375.

  26. S. Bakheet and A. Al-Hamadi, British J. Math. Comput. Sci., 17, 1 (2016).

    Article  Google Scholar 

  27. J. Yang, L. Zhang, J. Yang, and D. Zhang, Pattern Recognition, 44, 1387 (2011).

    Article  Google Scholar 

  28. S. Sadek, A. Al-Hamadi, B. Michaelis, and U. Sayed, “Human action recognition via affine moment invariants,” in: The 21st International Conference on Pattern Recognition (ICPR’12), Tsukuba Science City, Japan, November 2012, p. 218.

  29. S. Sadek, A. Al-Hamadi, and B. Michaelis, WSEAS Trans. Inform. Sci. Appl., 10, 116 (2013).

    Google Scholar 

  30. S. Sadek, A. Al-Hamadi, B. Michaelis, and U. Sayed, “Towards robust human action retrieval in video,” in: Proceedings of the British Machine Vision Conference (BMVC’10), Aberystwyth, UK, September 2010.

  31. Z. Liu, Q. Pan, and J. Dezert, Pattern Recognition, 46, 834 (2013).

    Article  Google Scholar 

  32. S. Sadek, A. Al-Hamadi, B. Michaelis, and U. Sayed, “Human activity recognition: A scheme using multiple cues,” in: Proceedings of the International Symposium on Visual Computing (ISVC’10), Las Vegas, Nevada, USA, November 2010, Vol. 1, p. 574.

  33. S. Sadek, A. Al-Hamadi, M. Elmezain, et al., “Human activity recognition using temporal shape moments,” in: IEEE International Symposium on Signal Processing and Information Technology (ISSPIT’10), Luxor, Egypt, December 2010, p. 79.

  34. T. Cover and P. Hart, IEEE Trans. Inform. Theory, 13, 21 (1967).

    Article  Google Scholar 

  35. Z. Voulgaris and G. D. Magoulas, “Extensions of the k nearest neighbour methods for classification problems,” in: Proceedings of the 26th IASTED International Conference on Artificial Intelligence and Applications, Anaheim, CA, USA, 2008, p. 2328.

  36. E. Elnahrawy, “Log-based chat room monitoring using text categorization: A comparative study,” in: International Association of Science and Technology for Development Conference on Information and Knowledge Sharing, ACTA Press (2002).

  37. F. Bajramovic, F. Mattern, N. Butko, and J. Denzler, ACIVS, 4179, 1186 (2006).

    Google Scholar 

  38. Y. Yang, T. Ault, T. Pierce, and C. W. Lattimer, “Improving text categorization methods for event tracking,” in: Proceedings of the 23rd Annual International ACM SIGIR, July 2000, p. 65.

  39. S. H. M. Kasaei and S. M. M. Kasaei, “Extraction and recognition of the vehicle license plate for passing under outside environment,” in: The European Conference on Intelligence and Security Informatics (EISIC), 2011, p. 234.

  40. R. Azad, F. Davami, and B. Azad, Adv. Comput. Sci., 2, 64 (2013).

    Google Scholar 

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Authors and Affiliations

  1. Faculty of Computers and Information, University of Sohag, Sohag, P.O Box 82524, Egypt

    Samy Bakheet

  2. Institute for Information Technology and Communications (IIKT) Otto von Guericke University Magdeburg, 39106, Magdeburg, Germany

    Samy Bakheet & Ayoub Al-Hamadi

Authors
  1. Samy Bakheet
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  2. Ayoub Al-Hamadi
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Correspondence to Samy Bakheet.

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Bakheet, S., Al-Hamadi, A. Chord-Length Shape Features for License Plate Character Recognition. J Russ Laser Res 41, 156–170 (2020). https://doi.org/10.1007/s10946-020-09861-1

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  • DOI: https://doi.org/10.1007/s10946-020-09861-1

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