Skip to main content
Log in

iLPR: an Indian license plate recognition system

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

One of the major concerns of Integrated Traffic Management System (ITMS) in India is the identification of vehicles violating the stop-line at a road crossing. A large number of Indian vehicles do not stop at the designated stop-line and pose serious threat to the pedestrians crossing the roads. The current work reports the technicalities of the \( i \)LPR (Indian License Plate Recognition) system implemented at five busy road-junctions in one populous metro city in India. The designed system is capable of localizing single line and two-line license plates of various sizes and shapes, recognizing characters of standard/ non-standard fonts and performing seamlessly in varying weather conditions. The performance of the system is evaluated with a large database of images for different environmental conditions. We have published a limited database of Indian vehicle images in http://code.google.com/p/cmaterdb/ for non-commercial use by fellow researchers. Despite unparallel complexity in the Indian city-traffic scenario, we have achieved around 92 % plate localization accuracy and 92.75 % plate level recognition accuracy over the localized vehicle images.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21

Similar content being viewed by others

References

  1. Anagnostopoulos CNE, Anagnostopoulos IE, Loumos V, Kayafas E (2006) A license plate-recognition algorithm for intelligent transportation system applications. Intell Transp Syst, IEEE Trans 7(3):377–392

    Article  Google Scholar 

  2. Basu S, Chaudhuri C, Kundu M, Nasipuri M, Basu D (2007) Text line extraction from multi-skewed handwritten documents. Pattern Recogn 40(6):1825–1839

    Article  MATH  Google Scholar 

  3. Basu S, Das N, Sarkar R, Kundu M, Nasipuri M, Basu DK (2009) A hierarchical approach to recognition of handwritten Bangla characters. Pattern Recogn 42(7):1467–1484

    Article  MATH  Google Scholar 

  4. Basu S, Das N, Sarkar R, Kundu M, Nasipuri M, Kumar Basu D (2010) A novel framework for automatic sorting of postal documents with multi-script address blocks. Pattern Recogn 43(10):3507–3521

    Article  MATH  Google Scholar 

  5. Casey RG, Lecolinet E (1996) A survey of methods and strategies in character segmentation. Pattern Anal Mach Intell, IEEE Trans 18(7):690–706

    Article  Google Scholar 

  6. Chang SL, Chen LS, Chung YC, Chen SW (2004) Automatic license plate recognition. Intell Transp Syst, IEEE Trans 5(1):42–53

    Article  MathSciNet  Google Scholar 

  7. Chowdhury SP, Dhar S, Das AK, Chanda B, McMenemy K (2009) “Robust extraction of text from camera images,” in Document Analysis and Recognition, 2009. ICDAR’09. 10th International Conference on, , pp. 1280–1284

  8. Chowdhury SP, Dhar S, Rafferty K, Das AK, Chanda B (2009) Robust extraction of text from camera images using colour and spatial information simultaneously. J Univ Comp Sci 15(18):3325–3342

    Google Scholar 

  9. Gazcón NF, Chesñevar CI, Castro SM (2012) Automatic vehicle identification for Argentinean license plates using intelligent template matching. Pattern Recogn Lett 33(9):1066–1074

    Article  Google Scholar 

  10. Gonzales RC, Woods RE (2002) Digital Image Processing, vol. 6. Prentice Hall,

  11. Gopalan C, Manjula D (2011) Statistical modeling for the detection, localization and extraction of text from heterogeneous textual images using combined feature scheme. SIViP 5(2):165–183

    Article  Google Scholar 

  12. Haralick RM, Shanmugam K, Dinstein IH (1973)“Textural features for image classification,” Systems, Man and Cybernetics, IEEE Transactions on, no. 6, pp. 610–621

  13. Jia W, Zhang H, He X, Piccardi M (2005) “Mean shift for accurate license plate localization,” in Intelligent Transportation Systems, 2005. Proceedings. 2005 IEEE, pp. 566–571.

  14. Jung C, Liu Q, Kim J (2008) A new approach for text segmentation using a stroke filter. Signal Process 88(7):1907–1916

    Article  MATH  Google Scholar 

  15. Jung C, Liu Q, Kim J (2009) A stroke filter and its application to text localization. Pattern Recogn Lett 30(2):114–122

    Article  Google Scholar 

  16. Kasaei SH, Kasaei SM, Kasaei SA (2010) New morphology based method for robust Iranian Car plate detection and recognition. Intell Transp Syst, IEEE Trans 2(2):264–268

    Google Scholar 

  17. Khandelwal A, Choudhury P, Sarkar R, Basu S, Nasipuri M, Das N (2009) “Text line segmentation for unconstrained handwritten document images using neighborhood connected component analysis,” Pattern Recognition and Machine Intelligence, pp. 369–374

  18. Kwaśnicka H, Wawrzyniak B (2002) “License plate localization and recognition in camera pictures,” in 3rd Symposium on Methods of Artificial Intelligence, pp. 243–246.

  19. Llorens, A. Marzal, V. Palazón, Vilar J (2005)“Car license plates extraction and recognition based on connected components analysis and HMM decoding,” Pattern Recognition and Image Analysis, pp. 357–362

  20. Mahini H, Kasaei S, Dorri F (2006) “An efficient features-based license plate localization method,” in Pattern Recognition, 2006. ICPR 2006. 18th International Conference on, vol. 2, pp. 841–844

  21. Nilsson NJ (1982) Principles of artificial intelligence. Symb Comput Berlin: Springer 1:1982

    Google Scholar 

  22. Otsu N (1975) A threshold selection method from gray-level histograms. Automatica 11(285–296):23–27

    Google Scholar 

  23. Pal NR, Pal SK (1993) A review on image segmentation techniques. Pattern Recogn 26(9):1277–1294

    Article  Google Scholar 

  24. Pan X, Ye X, Zhang S (2005) A hybrid method for robust car plate character recognition. Eng Appl Artif Intell 18(8):963–972

    Article  Google Scholar 

  25. Parasuraman K, Kumar P (2010) “An efficient method for indian vehicle license plate extraction and character segmentation,” in IEEE International Conference on Computational Intelligence and Computing Research

  26. Retornaz T, Marcotegui B (2007) “Scene text localization based on the ultimate opening”, in international symposium on mathematical. Morphology 1:177–188

    Google Scholar 

  27. Saha S, Basu S, Nasipuri M, Basu DK (2009) “Development of an automated Red Light Violation Detection System ( RLVDS ) for Indian vehicles,” in National Conference on Computing and Communication Systems (COCOSYS-09), pp. 59–64.

  28. Saha S, Basu S, Nasipuri M, Basu DK (2009)“An Offline Technique for Localization of License Plates for Indian Commercial Vehicles,” in Proc. of National Conference on Computing and Communication Systems (COCOSYS-09), pp. 206–211.

  29. Saha S, Basu S, Nasipuri M, Basu DK (2009) License plate localization from vehicle images: an edge based multi-stage approach. Int J Recent Trends Eng (Comp Sci) 1(1):284–288

    Google Scholar 

  30. Saha S, Basu S, Nasipuri M, Basu DK (2010) Localization of license plates from surveillance camera images: a color feature based ann approach. Int J Comp Appl IJCA 1(23):27–31

    Google Scholar 

  31. Saha S, Basu S, Nasipuri M, Basu DK (2010)“A Hough Transform based Technique for Text Segmentation,” arXiv preprint arXiv:1002.4048

  32. Saha S, Basu S, Nasipuri M, Basu DK (2011) Localization of license plates from Indian vehicle images using iterative edge Map generation technique. J Comput 3(6):48–57

    Google Scholar 

  33. Saha S, Basu S, Nasipuri M (2011) Automatic localization and recognition of license plate characters for Indian vehicles. Int J Comput Sci Emerg Technol 2(4):520–533

    Google Scholar 

  34. Saha S, Basu S, Nasipuri M (2012) “License plate localization using vertical edge Map and Hough transform based technique”, in proceedings of the international conference on information systems design and intelligent applications (INDIA 2012) held in Visakhapatnam. India, January, pp 649–656

    Google Scholar 

  35. Sobel I, Feldman G (1968) “A 3 × 3 isotropic gradient operator for image processing,” in Presented at a talk at the Stanford Artificial Project

Download references

Acknowledgement

Authors are thankful to the CMATER and the SRUVM project of C.S.E. Department, Jadavpur University, for providing necessary infrastructural facilities during the progress of the work. We acknowledge the collaborations with emotions Infomedia Pvt. Ltd., India and Department of Traffic, Kolkata Police, West Bengal, India for collection of the image database and design of the iLPR system. Dr. Saha, is thankful to the authorities of MCKV Institute of Engineering for kindly permitting him to carry on the research work.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Satadal Saha.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Saha, S., Basu, S. & Nasipuri, M. iLPR: an Indian license plate recognition system. Multimed Tools Appl 74, 10621–10656 (2015). https://doi.org/10.1007/s11042-014-2196-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-014-2196-7

Keywords

Navigation