CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Automatic Number Plate Recognition System (ANPR): A Survey

by Chirag Patel, Dipti Shah, Atul Patel
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 69 - Number 9
Year of Publication: 2013
Authors: Chirag Patel, Dipti Shah, Atul Patel
10.5120/11871-7665

Chirag Patel, Dipti Shah, Atul Patel . Automatic Number Plate Recognition System (ANPR): A Survey. International Journal of Computer Applications. 69, 9 ( May 2013), 21-33. DOI=10.5120/11871-7665

@article{ 10.5120/11871-7665,
author = { Chirag Patel, Dipti Shah, Atul Patel },
title = { Automatic Number Plate Recognition System (ANPR): A Survey },
journal = { International Journal of Computer Applications },
issue_date = { May 2013 },
volume = { 69 },
number = { 9 },
month = { May },
year = { 2013 },
issn = { 0975-8887 },
pages = { 21-33 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume69/number9/11871-7665/ },
doi = { 10.5120/11871-7665 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:29:48.442890+05:30
%A Chirag Patel
%A Dipti Shah
%A Atul Patel
%T Automatic Number Plate Recognition System (ANPR): A Survey
%J International Journal of Computer Applications
%@ 0975-8887
%V 69
%N 9
%P 21-33
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Traffic control and vehicle owner identification has become major problem in every country. Sometimes it becomes difficult to identify vehicle owner who violates traffic rules and drives too fast. Therefore, it is not possible to catch and punish those kinds of people because the traffic personal might not be able to retrieve vehicle number from the moving vehicle because of the speed of the vehicle. Therefore, there is a need to develop Automatic Number Plate Recognition (ANPR) system as a one of the solutions to this problem. There are numerous ANPR systems available today. These systems are based on different methodologies but still it is really challenging task as some of the factors like high speed of vehicle, non-uniform vehicle number plate, language of vehicle number and different lighting conditions can affect a lot in the overall recognition rate. Most of the systems work under these limitations. In this paper, different approaches of ANPR are discussed by considering image size, success rate and processing time as parameters. Towards the end of this paper, an extension to ANPR is suggested.

References
  1. You-Shyang Chen and Ching-Hsue Cheng, "A Delphi-based rough sets fusion model for extracting payment rules of vehicle license tax in the government sector," Expert Systems with Applications, vol. 37, no. 3, pp. 2161-2174, 2010.
  2. Anton Satria Prabuwono and Ariff Idris, "A Study of Car Park Control System Using Optical Character Recognition ," in International Conference on Computer and Electrical Engineering, 2008, pp. 866-870.
  3. A Albiol, L Sanchis, and J. M Mossi, "Detection of Parked Vehicles Using Spatiotemporal Maps," IEEE Transactions on Intelligent Transportation Systems, vol. 12, no. 4, pp. 1277-1291, 2011.
  4. Christos Nikolaos E. Anagnostopoulos, Ioannis E. Anagnostopoulos, Ioannis D. Psoroulas, Vassili Loumos, and Eleftherios Kayafas, License Plate Recognition From Still Images and Video Sequences: A Survey, vol. 9, no. 3, pp. 377-391, 2008.
  5. Christos Nikolaos E. Anagnostopoulos, Ioannis E. Anagnostopoulos, Vassili Loumos, and Eleftherios Kayafas, "A License Plate-Recognition Algorithm for Intelligent Transportation System Applications," pp. 377-392, 2006.
  6. H. Erdinc Kocer and K. Kursat Cevik, "Artificial neural netwokrs based vehicle license plate recognition," Procedia Computer Science, vol. 3, pp. 1033-1037, 2011.
  7. A Roy and D. P Ghoshal, "Number Plate Recognition for use in different countries using an improved segmenation," in 2nd National Conference on Emerging Trends and Applications in Computer Science(NCETACS), 2011, pp. 1-5.
  8. Kaushik Deb, Ibrahim Kahn, Anik Saha, and Kang-Hyun Jo, "An Efficeint Method of Vehicle License Plate Recognition Based on Sliding Concentric Windows and Artificial Neural Network," Procedia Technology, vol. 4, pp. 812-819, 2012.
  9. Lucjan Janowski et al. , "Quality assessment for a visual and automatic license plate recognition," Multimedia Tools and Applications Springer US, pp. 1-18, 2012.
  10. Yifan Zhu, Han Huang, Zhenyu Xu, Yiyu He, and Shiqiu Liu, "Chinese-style Plate Recognition Based on Artificaial Neural Network and Statistics," Procedia Engineering, vol. 15, pp. 3556-3561, 2011.
  11. Fikriye Öztürk and Figen Özen, "A New License Plate Recognition System Based on Probabilistic Neural Networks," Procedia Technology, vol. 1, pp. 124-128, 2012.
  12. Jian Liang, D Dementhon, and D Doermann, "Geometric Rectification of Camera- Captured Document Images," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 9, no. 3, pp. 591-605, 2008.
  13. Xin Fan and Guoliang Fan, "Graphical Models for Joint Segmentation and Recognition of License Plate Characters," IEEE Signal Processing Letters, vol. 16, no. 1, pp. 10-13, 2009.
  14. Lihong Zheng, Xiangjian He, Bijan Samali, and Laurence T. Yang, "An algorithm for accuracy enhancement of license recognition," Journal of Computer and System Sciences, , 2012.
  15. Zhen-Xue Chen, Cheng-Yun Liu, Fa-Liang Chang, and Guo-You Wang, "Automatic License-Plate Location and Recognition Based on Feature Saliance," IEEE Transactions on Vehicular Technology, vol. 58, no. 7, pp. 3781-3785, 2009.
  16. Ch. Jaya Lakshmi, Dr. A. Jhansi Rani, Dr. K. Sri Ramakrishna, and M. KantiKiran, "A Novel Approach for Indian License Recognition System," International Journal of Advanced Engineering Sciences and Technologies, vol. 6, no. 1, pp. 10-14, 2011.
  17. Jianbin Jiao, Qixiang Ye, and Qingming Huang, "A configurabe method for multi-style license plate recognition," Pattern Recognition, vol. 42, no. 3, pp. 358-369, 2009.
  18. Zhigang Zhang and Cong Wang, "The Reseach of Vehicle Plate Recogniton Technical Based on BP Neural Network," AASRI Procedia, vol. 1, pp. 74-81, 2012.
  19. Ying Wen et al. , "An Algorithm for License Plate recognition Applied to Intelligent Transportation System," IEEE Transactions of Intelligent Transportation Systems, pp. 1-16, 2011.
  20. Mehmet Sabih Aksoy and Ahmet Kürsat Türker Gültekin Çag?l, "Number-plate recognition using inductive learning," Robotics and Autonomous Systems, vol. 33, no. 2-3, pp. 149-153, 2000.
  21. Wenjing Jia, Huaifeng Zhang, and Xiangjian He, "Region-based license plate detection," Journal of Network and Computer Applications, vol. 30, no. 4, pp. 1324-1333, November 2007.
  22. Yang Yang, Xuhui Gao, and Guowei Yang, "Study the Method of Vehicle License Locating Based on Color Segmentation," Procedia Engineering , vol. 15, pp. 1324-1329, 2011.
  23. Feng Wang et al. , "Fuzzy-based algorithm for color recognition of license plates," Pattern Recognition Letters, vol. 29, no. 7, pp. 1007-1020, May 2008.
  24. Morteza Zahedi and Seyed Mahdi Salehi, "License plate recognition system based on SIFT features," Procedia Computer Science, vol. 3, pp. 998-1002, 2011.
  25. Mei-Sen Pan, Jun-Biao Yan, and Zheng-Hong Xiao, "Vehicle license plate character segmentation ," Intenational Journal of Automation and Computing, pp. 425-432, 2008.
  26. Kaushik Deb, Andrey Vavilin, Jung-Won Kim, and Kang-Hyun Jo, "Vehicle license plate tilt correction based on the straight lne fitting method and minimizing variance of coordinates of projection point," International Journal of Control, Automation and Systems. , pp. 975-984, 2010.
  27. Francisco Moraes Oliveira-Neto, Lee D. Han, and Myong K Jeong, "Online license plate matching procedures using license-plate recognition machine and new weighted edit distance," Transportation Research Part C: Emerging Technologies, vol. 21, no. 1, pp. 306-320, April 2012.
  28. Xing Yang, Xiao-Li Hao, and Gang Zhao, "License plate location based on trichromatic imaging and color-discrete characteristic," Optik- International Journal for Light and Electron Optics, vol. 123, no. 16, pp. 1486-1491, August 2012.
  29. Cynthia Lum, Julie Hibdon, Breanne Cave, Christopher S. Koper, and Linda Merola, "License plate reader(LRP) police patrols in crime hot spots: an experimental evaluation in two adjacent jurisdictionss," Journal of Experimel Criminology, Springer Netherlands, , pp. 321-345, 2011.
  30. K. V. Suresh, G. Mahesh Kumar, and A. N. Rajagopalan, "Superresolution of license plates in real traffic videos," IEEE Trans. Intell. Transp. Syst, vol. 8, no. 2, pp. 321-331, 2007.
  31. Yushuang Tian, Kim-Hui Yap, and Yu He, "Vehicle license plate super-resolution using soft learning prior," Multimedia Tools and Applications, Springer US, pp. 519-535, 2012.
  32. D. H. Ballard, "Generalizing the Hough Transform to Detect Arbitary Shapes," Pattern Recognition, vol. 13, no. 2, pp. 111-122, 1981.
  33. Shen-Zheng Wang and Hsi-Jian Lee, "A cascade framework for real-time statistical plate recognition system," IEEE Trans. Inf. Forensics security, vol. 2, no. 2, pp. 267-282, 2007.
  34. Prathamesh Kulkarni, Ashish Khatri, Prateek Banga, and Kushal Shah, "Automatic Number Plate Recognition (ANPR)," in RADIOELEKTRONIKA. 19th International Conference, 2009.
  35. Hui Wu and Bing Li, "License Plate Recognition System," in International Conference on Multimedia Technology (ICMT), 2011, pp. 5425-5427.
  36. Abdulkar Sengur and Yanhui Guo, "Color texture image segmentation based on neutrosophic set and wavelet transformation ," Computer Vision and Image Understanding, vol. 115, no. 8, pp. 1134-1144, August 2011.
  37. Jiann-Jone Chen, Chun-Rong Su, W. E. L Grimson, Jun-Lin Liu, and De-Hui Shiue, "Object Segmentation of Database Images by Dual Multiscale Morphological Reconstructions and Retrieval Applications," IEEE Transactions on Image Processing, vol. 21, no. 2, pp. 828-843, 2012.
  38. Mahmood Ashoori Lalimi, Sedigheh Ghofrani, and Des McLernon, "A vehicle license plate detection method using region and edge based methods," Computers & Electrical Engineering, November 2012.
  39. M. S. Sarfraz et al. , "Real-Time automatic license plate recognition for CCTV forensic applications," Journal of Real-Time Image Processing- Springer Berlin/Heidelberg, 2011.
  40. Rongbao Chen and Yunfei Luo, "An Improved License Plate Location Method Based On Edge Detection," Physics Procedia, vol. 24, pp. 1350-1356, 2012.
  41. T Naito, T Tsukada, K Kozuka, and S yamamoto, "Robust license-plate recognition method for passing vehicles under outside environment," IEEE Transactions on Vehicular Technology, vol. 49, no. 6, pp. 2309-2319, 2000.
  42. R Zunino and S Rovetta, "Vector quantization for license-plate location and image coding," IEEE Transactions on Industrial Electornics, vol. 47, no. 1, pp. 159-167, 2000.
  43. Yuntao Cui and Qian Huang, "Extracting character of license pltes from video sApplicationsequences," Machine Vision and Applications, Springer Verlag, p. 308, 1998.
  44. M. H Ter Brugge, J. A. g jhuis, L Spaanenburg, and J. H Stevens, "CNN- Applications in Toll Driving," Journal of VLSI signal processing systems for signal, image and video tehnology, pp. 465-477, 1999.
  45. Vladimir Shapiro and Georgi Gluhchev Dimo Dimov, "Towards a Multinational Car License Plate Recognition system," Machine Vision and Appplcations, Springer-Verlag, pp. 173-183, 2006.
  46. E. N Vesnin and V. A Tsarev, "Segmentation of images of license plates," Pattern Recogniton and Image Analysis, pp. 108-110, 2006.
  47. A Kang, D. J;, "Dynamic programming -based method for extraction of license numbers of speeding vehicles on the highway ," International Journal of Automotive Technology, pp. 205-210, 2009.
  48. P. Viola and M JOnes, "Robust real-time face detection," Int. J. Comput. Vis, vol. 57, no. 2, pp. 137-154, 2004.
  49. Shyang-Lih Chang, Li-Shien Chen, Yun-Chung Chung, and Sei-Wan Chen, "Automatic license plate recogniton," IEEE Transactions on Intelligent Transportation Systems, vol. 5, no. 1, pp. 42-53, 2004.
  50. Rami Al-Hmouz and Subhash Challa, "License plate location based on a probabilistic model," Machin Vision and Applications, Springer-Verlag, pp. 319-330, 2010.
  51. J. K. Chang, Ryoo Seungteak, and Heuiseok Lim, "Real-time vehicle tracking mechanism with license plate recognition from reoad images," The journal of super computing , pp. 1-12, 2011.
  52. Nicolas Thome, Antoine Vacavant, Lionel Robinault, and Serge Miguet, "A cognitive and video-based approach for multinational License Plate Recognition ," Machine Vision and Applications, Springer-Verlag, pp. 389-407, 2011.
  53. Vahid Abolghasemi and Alireza Ahmadyfard, "An edge-based color aided method for license plate detection," Image and Vision Computing , vol. 27, no. 8, pp. 1134-1142, July 2009.
  54. Xiaofeng Zhang, Fengchang Xu, and Yan Su, "Research on the Licnese Plate Recognition based on MATLAB," Procedia Engineering, vol. 15, pp. 1330-1334, 2011.
  55. M. Mirmehdi and M Petrou, "Segmentation of color textures," IEEE Transactions of Pattern Analysis and Machine Intelligence, vol. 22, no. 2, pp. 142-159, 2000.
  56. Y. Amit, D. German, and X. Fan, "A coarse-to-fine strategy for multiclass shape detection," IEEE Trans. Patttern Anal. Mach. Intell, vol. 26, no. 12, pp. 1606-1621, 2004.
  57. M. E Farmer and A. k Jain, "A wrapper based approach to image segmentatin and classification," IEEE Transaction on Image Processing, vol. 14, no. 12, pp. 2060-2072, 2005.
  58. Dewen Zhuang and Shoujue Wang, "Content-Based Image Retrieval Based on Integrating Region Segmentation and Relevance Feedback," in International Conference on Multimedia Technology (ICMT), 2010, pp. 1-3.
  59. Bo Peng, Lei Zhang, and D Zhang, "Automatic Image Segmenation by Dynamic Region Merging," IEEE Transactions on Image Processing, vol. 20, no. 12, pp. 3592-3605, 2011.
  60. Cheng, Chang; Koschan, A; Chen, Chung-Hao; Page, D. L; Abidi, M. A, "Outdoor Scene Image Segmentation Based on Background Recognition and Perceptual Organization," IEEE Transactions on Image Processing, vol. 21, no. 3, pp. 1007-1019, 2012.
  61. Shahid Mehmood, Stefano Cagnoni, Monica Mordonini, and Shoab Ahmad Khan, "An embeded architecture for real-time object detection in digital images based on niching particle swarm optimization," Journal of Real-Time Image Processing, Springer-Verlag, pp. 1-15, 2012.
  62. Fatih Kurugollu, Bülent Sankur, and A. Emre Harmanci, "Image segmentation by relaxation using constraint satisfaction neural network," Image and Vision Computing, vol. 20, no. 7, pp. 483-497, May 2002.
  63. S. H. Ong, N. C. Yeo, K. H Lee, Y. V. Venkatesh, and D. M. Cao, "Segmentation of color images using a two-stage self-organizing network," Image and Vision Computing, vol. 20, no. 4, pp. 279-289, 2002.
  64. Ety Navon, Ofer Miller, and Amir Averbuch, "Color Image segmentation based on adaptive local thresholds," Image and Vision Computing, vol. 23, no. 1, pp. 69-85, January 2005.
  65. Ying Zhuge, Jayaram K. Udupa, and Punam K. Saha, "Vector scale-based fuzzy-connected image segmentation," Computer Vision and Image Understanding, vol. 110, no. 2, pp. 177-193, March 2006.
  66. Daniel Crevier, "Image segmentation algorithm development using ground truth image data sets," Computer Vision and Image Understanding, vol. 112, no. 2, pp. 143-159, November 2008.
  67. Mark Polak, Hong Zhang, and Minghong Pi, "An evaluation metric for image segmentation of multiple objects," Image and Vision Computing , vol. 27, no. 8, pp. 1223-1227, July 2009.
  68. Antonio Carlos Sobiernaski, Eros Comunello, and Ald von Wangenheim, "Leaning a nonlinear distance metric for supervised region-merging image segmentation," Computer Vision and Image Understanding, vol. 115, no. 2, pp. 127-139, February 2011.
  69. Siqi Chen, Daniel Cremers, and Richard J. Radke, "Image segmentation with one shape prior - A template-based formulation," Image and Vision Computing , vol. 30, no. 12, pp. 1032-1042, December 2012.
  70. Hong-Ying Yang, Xiang-Yang, Qin-Yan Wang, and Xian-Jin Zhang, "LS-SVM based image segmentation using color and texture information ," Journal of Visual Communication and Image Representation , vol. 23, no. 7, pp. 1095-1112, October 2012.
  71. Hui Zhang, Jason E. Fritts, and Sally A. Goldman, "Image Segmentation evaluation: A survey of unsupervised methods," Computer Vision and Image Understanding, vol. 110, no. 2, pp. 260-280, May 2008.
  72. J. Phelawan, P. Kittisut, and N. Pornsuwancharoen, "A new technique for distance measurement of between vehicles to vehicles by plate car using image processing," Procedia Engineering, vol. 32, pp. 348-353, 2012.
  73. S Sapna Varshney, N Rajpal, and R Purwar, "Comparative Study of image segmentation techniques and object matching using segmentation," in International Conference on Methods adn Models in Computer Science, 2009, pp. 1-6.
  74. Smith R, "An Overview of the Tesseract OCR Engine," in IEEE Ninth Intenational Conference Proceeding of Document anay and Recognition, 2007.
  75. Chirag Patel, Atul Patel, and Dharmendra Patel, "Optical Character Recognition by Open source OCR Tool Tesseract : A Case Study," International Journal of Computer Applications, Foundation of Computer Science, New York. USA, vol. 55, no. 10, pp. 50-56, October 2012.
  76. Google Code. [Online] (2012). http://code. google. com/p/tesseract-ocr
  77. Anju K. Sadasivan and T. Senthilkumar, "Automatic Character Recognition in Complex Images," Procedia Engineering, vol. 30, pp. 218-225, 2012.
  78. Jerome Connix, License Plate Mania. [Online]. http://www. licenseplatemania. com
Index Terms

Computer Science
Information Sciences

Keywords

Automatic Number Plate Recognition (ANPR) Artificial Neural Network (ANN) Character Segmentation Image Segmentation Number Plate Optical Character Recognition

Powered by PhDFocusTM