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Character Recognition for ALPR Systems: A New Perspective

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Innovations in Electronics and Communication Engineering

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 107))

Abstract

The automatic license plate recognition (ALPR) systems are utilized to locate vehicles’ license (or number) plates and extract the information it contains from the image or video. The paper presents a new method of computing the recognition efficiency in which a successful recognition is only considered if the whole license plate is correctly recognized instead of focusing on individual characters, as it is more useful to consider the license plate as a whole. The recognition efficiency of template matching algorithm and SVM-based feature matching algorithm was determined to be 76.36% and 80%, respectively.

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References

  1. B. Tian, Y. Li, B. Li, D. Wen, Rear-view vehicle detection and tracking by combining multiple parts for complex. IEEE Trans. Intell. Transp. Syst. 15(2), 597–606 (2014)

    Article  Google Scholar 

  2. I.S. Ahmad, B. Boufama, P. Habashi, W. Anderson, T. Elamsy, Automatic license plate recognition: a comparitive study, in IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), (Abu Dhabi, UAE, 2015), pp. 635–640

    Google Scholar 

  3. K.S. Raghunandan, P. Shivakumara, H.A. Jalab, R.W. Ibrahim, G.H. Kumar, U. Pal, T. Lu, Riesz fractional based model for enhancing license plate detection and recognition. IEEE Trans. Circuits Syst. Video Technol. 28(9), 2276–2288 (2018)

    Google Scholar 

  4. A.H. Ashtar, M.J. Nordin, M. Fathy, An Iranian license plate recognition system based on color features. IEEE Trans. Intell. Transp. Syst. 15(4), 1690–1705 (2014)

    Google Scholar 

  5. G. Abo Samram, F. Khalefah, Localization of license plate number using dynamic image processing techniques and genetic algorithms. IEEE Trans. Evol. Comput. 18(2), 244–257 (2014)

    Google Scholar 

  6. R. Panahi, I. Gholampour, Accurate detection and recognition of dirty vehicle plate numbers for high-speed applications. IEEE Trans. Intell. Transp. Syst. 18(4), 767–779 (2017)

    Article  Google Scholar 

  7. Y. Yang, D. Li, Z. Duan, Chinese vehicle license plate recognition using kernel-based extreme learning machine with deep convolutional features. IET Intel. Transport Syst. 12(3), 213–219 (2018)

    Article  Google Scholar 

  8. O. Bulan, V. Kozitsky, P. Ramesh, M. Shreve, Segmentation-and annotation-free license plate recognition with deep localization and failure identification. IEEE Trans. Intell. Transp. Syst. 18(9), 2351–2363 (2017)

    Article  Google Scholar 

  9. M.R. Asif, Q. Chun, S. Hussain, M.S. Fareed, Multiple licence plate detection for Chinese vehicles in dense traffic scenarios. IET Intell. Transp. Syst. 10(8), 535–544 (2016)

    Google Scholar 

  10. S. Rani, P.K. Dahiya, A review of recognition technique used automatic license plate recognition system. Int. J. Comput. Appl. (0975–8887) 121(17) (2015)

    Google Scholar 

  11. H. Kuang, L. Chen, F. Gu, J. Chen, L. Chan, H. Yan, Combining region-of-interest extraction and image enhancement for nighttime vehicle detection. IEEE Intell. Syst. 31(3), 57–65 (2016)

    Google Scholar 

  12. J. Yepez, S.B. Ko, Improved license plate localisation algorithm based on morphological operations. IET Intel. Transport Syst. 12(6), 542–549 (2018)

    Article  Google Scholar 

  13. D. Shan, M. Ibrahim, M. Shehata, W. Badawy, Automatic license plate recognition (ALPR): a state-of-the-art review. IEEE Trans. Circuits Syst. Video Technol. 23(2), 311–325 (2013)

    Article  Google Scholar 

  14. Q. Li, A geometric framework for rectangular shape detection. IEEE Trans. Image Process. 23(9), 4139–4149 (2014)

    Article  MathSciNet  Google Scholar 

  15. L. Xie, T. Ahmad, L. Jin, Y. Liu, S. Zhang, A new CNN-based method for multi-directional car license plate detection. IEEE Trans. Intell. Transp. Syst. 19(2), 507–517 (2018)

    Article  Google Scholar 

  16. S. Khokhar, P.K. Dahiya, A review of recognition techniques in ALPR systems. Int. J. Comput. Appl. (0975–8887) 170(6), 30–32 (2017)

    Google Scholar 

  17. Y. Yuan, W. Zou, Y. Zhao, X. Wang, H. Xuefeng, N. Komodakis, A robust and efficient approach to license plate detection. IEEE Trans. Image Process. 26(3), 1102–1114 (2017)

    Article  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of ECE, GJUS&T, Hisar, India

    Sahil Khokhar

  2. Department of ECE, DCRUST, Murthal, Sonepat, India

    Pawan Kumar Dahiya

Authors
  1. Sahil Khokhar
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  2. Pawan Kumar Dahiya
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Corresponding author

Correspondence to Sahil Khokhar .

Editor information

Editors and Affiliations

  1. Guru Nanak Institutions, Hyderabad, Telangana, India

    H. S. Saini

  2. Guru Nanak Institutions Technical Campus, Hyderabad, Telangana, India

    R. K. Singh

  3. Department of Electronics and Communication Engineering, Jamia Millia Islamia, New Delhi, Delhi, India

    Mirza Tariq Beg

  4. Indian Institute of Technology Ropar, Rupnagar, Punjab, India

    J. S. Sahambi

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Khokhar, S., Dahiya, P.K. (2020). Character Recognition for ALPR Systems: A New Perspective. In: Saini, H.S., Singh, R.K., Tariq Beg, M., Sahambi, J.S. (eds) Innovations in Electronics and Communication Engineering. Lecture Notes in Networks and Systems, vol 107. Springer, Singapore. https://doi.org/10.1007/978-981-15-3172-9_46

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  • DOI: https://doi.org/10.1007/978-981-15-3172-9_46

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-3171-2

  • Online ISBN: 978-981-15-3172-9

  • eBook Packages: EngineeringEngineering (R0)

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