Skip to main content
SpringerLink
Log in

A Study on Different Techniques in ALPR System: The Systems Performance Analysis

  • Conference paper
  • First Online:
Recent Trends in Mechatronics Towards Industry 4.0

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 730))

Abstract

License plate recognition and localization are crucial steps in most transportation applications. For instance, trace for stolen vehicles, speed or airport gate monitoring, road traffic monitoring and car parking control. This is required a system to extract automatically and recognize the character of the license plate from the image captured. The automatic license plate recognition (ALPR) system has aroused great interest in the research community, because in certain regions, cities or countries have certain limitations and lack of similarity between different license plates. The ALPR system includes three important components: first step is license plate localization, second step is character segmentation and third step is character recognition. This paper is to give comprehensive reviews of the localization and recognition techniques involved in the license plate recognition system and compares existing effective solutions.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 279.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Xing J, Li J, Xie Z, Liao X, Zeng W (2016) Research and implementation of an improved radon transform for license plate recognition. In: 2016 8th international conference on intelligent human-machine systems and cybernetics (IHMSC), Aug 2016, vol 01, pp 42–45

    Google Scholar 

  2. Ahn C-S, Lee B-G, Yang S-S, Park S-C (2017) Design of car license plate area detection algorithm for enhanced recognition plate. In: 2017 4th international conference on computer applications and information processing technology (CAIPT), Aug 2017, pp 1–4

    Google Scholar 

  3. Feng Y, Li S, Pang T (2018) Research and system design of intelligent license plate recognition algorithm. In: 2018 37th Chinese control conference (CCC), Jul 2018, pp 9209–9213

    Google Scholar 

  4. Crime index by city 2019 mid-year. https://www.numbeo.com/crime/rankings.jsp. Accessed 12 Dec 2019

  5. Pingping L, Wenjing X (2019) Research on recognition technology of license plate image. In: 2019 international conference on computer network, electronic and automation (ICCNEA), Sep 2019, pp 52–56

    Google Scholar 

  6. Chao H, haiying l, huiyuan Z, Valentin (2018) License plate recognition and location algorithm based on compressive sensing. In: 2018 IEEE international conference on information and automation (ICIA), Aug 2018, pp 1593–1598

    Google Scholar 

  7. Lin G, Xue B, Xu B, Chen C (2019) License plate recognition based on mathematical morphology and template matching. In: 2019 Chinese automation congress (CAC), Nov 2019, pp 405–410

    Google Scholar 

  8. Template matching—an overview | ScienceDirect topics. https://www.sciencedirect.com/topics/engineering/template-matching. Accessed 24 May 2020

  9. Pinthong T, Yimyam W, Chumuang N, Ketcham M (2018) License plate tracking based on template matching technique. In: 2018 18th international symposium on communications and information technologies (ISCIT), Sept 2018, pp 299–303

    Google Scholar 

  10. Sánchez J, Monzón N, Salgado A (2018) An analysis and implementation of the Harris corner detector. Image Process On Line 8:305–328

    Article  Google Scholar 

  11. Falih E (2017) Improvement of Corner detection algorithms (Harris, FAST and SUSAN) based on reduction of features space and complexity time, vol 35, p 112

    Google Scholar 

  12. Symmetry features for license plate classification—IET Journals & Magazine. https://ieeexplore-ieee-org.ezproxy.ump.edu.my/document/8548625. Accessed 12 Jun 2020

  13. Kadir K, Kamaruddin M, Nasir H, Safie S, Bakti Z (2014) A comparative study between LBP and Haar-like features for Face Detection using OpenCV, pp 335–339

    Google Scholar 

  14. Fig. 3. Examples of Haar Features used in OpenCV. ResearchGate. https://www.researchgate.net/figure/Examples-of-Haar-Features-used-in-OpenCV_fig3_221317469. Accessed 24 May 2020

  15. Comparison of vehicle detection using Haar-like feature, LBP and HOG technique for feature extraction in cascade classifier. Int J Adv Sci Technol. http://sersc.org/journals/index.php/IJAST/article/view/953. Accessed 12 Jun 2020

  16. Local binary patterns—Scholarpedia. http://www.scholarpedia.org/article/Local_Binary_Patterns. Accessed 24 May 2020

  17. Ingole SK, Gundre SB (2017) Characters feature based Indian vehicle license plate detection and recognition. In: 2017 international conference on intelligent computing and control (I2C2), Jun 2017, pp 1–5

    Google Scholar 

  18. Sabu AM, Das AS (2018) A survey on various optical character recognition techniques. In: 2018 conference on emerging devices and smart systems (ICEDSS), Mar 2018, pp 152–155

    Google Scholar 

  19. Omran SS, Jarallah JA (2017) Iraqi car license plate recognition using OCR. In: 2017 annual conference on new trends in information communications technology applications (NTICT), Mar 2017, pp 298–303

    Google Scholar 

  20. Hargrave M (2020) How deep learning can help prevent financial fraud. Investopedia. https://www.investopedia.com/terms/d/deep-learning.asp. Accessed 24 May 2020

  21. Abedin MdZ, Nath AC, Dhar P, Deb K, Hossain MS (2017) License plate recognition system based on contour properties and deep learning model. In: 2017 IEEE region 10 humanitarian technology conference (R10-HTC), Dec 2017, pp 590–593

    Google Scholar 

  22. Khazri A (2019) Automatic License Plate Detection & Recognition using deep learning. Medium, 10 Oct 2019. https://towardsdatascience.com/automatic-license-plate-detection-recognition-using-deep-learning-624def07eaaf. Accessed 24 May 2020

  23. Shevale K, Gite S (2016) Automatic license plate recognition system using web technologies and image processing for real time retrieval of information. /paper/Automatic-License-Plate-Recognition-System-using-of-Shevale-Gite/62136e9ba5c10161c55a55ec5d3c24dd2d098f5e. Accessed 24 May 2020

    Google Scholar 

  24. (PDF) Performance analysis of vehicle number plate recognition system using template matching techniques. https://www.researchgate.net/publication/325362213_Performance_Analysis_of_Vehicle_Number_Plate_Recognition_System_Using_Template_Matching_Techniques. Accessed 12 Dec 2019

  25. Iranian license plate detection using cascade classifier—IEEE Conference Publication. https://ieeexplore-ieee-org.ezproxy.ump.edu.my/document/8786468. Accessed 12 Jun 2020

  26. Panchal T, Patel H, Panchal A (2016) license plate detection using Harris corner and character segmentation by integrated approach from an image. Procedia Comput Sci 79:419–425

    Article  Google Scholar 

  27. Desai GG, Bartakke PP (2018) Real-time implementation of Indian license plate recognition system. In: 2018 IEEE Punecon, 2018, pp 1–5

    Google Scholar 

  28. Luai Taha Ahmed A-M, Nurhafizah AT, Syamimi S, Mohamad Shaiful AK, Ahmad Afif MF (2018) Development of automated gate using automatic license plate recognition system. Presented at the 10th National Seminar on Underwater System Technology 2018 (NUSYS’18), UMP Pekan, Aug 2018, pp 1–8 [online]. Available at: http://umpir.ump.edu.my/id/eprint/22871/. Accessed 13 Jun 2020

  29. Zhang Z, Wan Y (2019) Improving the accuracy of license plate detection and recognition in general unconstrained scenarios. In: 2019 IEEE symposium series on computational intelligence (SSCI), 2019, pp 1194–1199

    Google Scholar 

  30. Tejas B, Omkar D, Rutuja D, Prajakta K, Bhakti P (2017) Number plate recognition and document verification using feature extraction OCR algorithm. In: 2017 international conference on intelligent computing and control systems (ICICCS), 2017, pp 1317–1320

    Google Scholar 

  31. Puarungroj W, Boonsirisumpun N (2018) Thai license plate recognition based on deep learning. Procedia Comput Sci 135:214–221

    Article  Google Scholar 

  32. Batista J, Imaculada R, César S (2018) Linux embedded system for vehicle license plates recognition. Int J Comput Appl 182:43–46

    Google Scholar 

Download references

Acknowledgements

The authors would like to thank Universiti Malaysia Pahang (UMP) for providing the facilities and fund to complete this project. This work is supported by UMP Internal Grant of RDU1703140.

Author information

Authors and Affiliations

  1. Faculty of Electrical and Electronics Engineering, University Malaysia Pahang, 26000, Pekan, Pahang, Malaysia

    Gan Vi Vi & Ahmad Afif bin Mohd Faudzi

Authors
  1. Gan Vi Vi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ahmad Afif bin Mohd Faudzi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gan Vi Vi .

Editor information

Editors and Affiliations

  1. Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, Pekan, Malaysia

    Ahmad Fakhri Ab. Nasir

  2. Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, Pekan, Malaysia

    Ahmad Najmuddin Ibrahim

  3. Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, Pekan, Malaysia

    Ismayuzri Ishak

  4. Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, Pekan, Malaysia

    Nafrizuan Mat Yahya

  5. Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, Pekan, Malaysia

    Muhammad Aizzat Zakaria

  6. Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, Pekan, Malaysia

    Anwar P. P. Abdul Majeed

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Vi, G.V., Faudzi, A.A.b.M. (2022). A Study on Different Techniques in ALPR System: The Systems Performance Analysis. In: Ab. Nasir, A.F., Ibrahim, A.N., Ishak, I., Mat Yahya, N., Zakaria, M.A., P. P. Abdul Majeed, A. (eds) Recent Trends in Mechatronics Towards Industry 4.0. Lecture Notes in Electrical Engineering, vol 730. Springer, Singapore. https://doi.org/10.1007/978-981-33-4597-3_56

Download citation

Publish with us

Policies and ethics

Search

Navigation