A Broad Survey on Performance Analysis of Number Plate Recognition from Stationary Images and Video Sequences

  • Authors

    • Tahir Khan
    • Dr J S Yadav
    • Dr Dheeraj Agarwal
    2018-07-15
    https://doi.org/10.14419/ijet.v7i3.10.15652
  • Image Processing, Multilayer Perceptron, Neural Network, Performance Analysis, Optical Character Recognition (OCR)

  • Abstract

    Licensed Number plate recognition plays vital role in smart cities for maintaining Law & Order and traffic management. NPR based system mainly involves four stages namely 1) Image capture & Pre-Processing 2) Number plate area determination 3) Character Segmentations 4) Recognition of all character. This survey paper extensively analyzed the method of extraction of number plate, its platform, performance and execution time.  With the development of Multilayer Perceptron Network accuracy and time in image processing has been achieved up to a great instant. Hence this analysis will help the precise assessment in establishing research and enable developers to assess which strategies are aggressive in present environment.

     

     

     

  • References

    1. [1] Bouchet A, Pastore J and Ballariu V, "Segmeutatiou of Medical images usiug Fuzzy Mathematical Morphology", JCS aud T, Vol7, No.3, Octorber 2007, pp. 256 -262.

      [2] P.Cheu, G.Waug, Y.Yang and lZhou, "Facial expressiou Recoguitiou based ou Rough Set Theory and SVM", Lecture Notes iu

      [3] Computer Science, Springer Berlin/Heidelberg, Rough Sets and Knowledge Technology, Vol. 4062, 2006 pp 772 -777.

      [4] N. Otsu, “A threshold selection method from gray-level histograms,†IEEE Trans. Syst., Man, Cybern., vol. SMC-9, no. 1, pp. 62–66, Jan. 1979.

      [5] P. Kakkar and U. Dutta, “A novel Approach to Recognition of English Characters Using Artificial Neural Networks â€, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, Vol 3, Issue 6, June 2014.

      [6] F. Martin, M. Garcia, and J. L. Alba, “New methods for automatic reading of VLP’s (Vehicle License Plates),†in Proc. IASTED Int. Conf. SPPRA, 2002.

      [7] X. Shi, W. Zhao, and Y. Shen, “Automatic License Plate Recognition System Based on Color Image Processing,†Comput. Sci. Its Applicat., Singapore, May 9–12, 2005, pp. 1159–1168.

      [8] R. Zunino and S. Rovetta, “Vector Quantization for License-Plate Location and Image Coding,†IEEE Trans. Ind. Electron., vol. 47, no. 1, Feb. 2000, pp. 159–167.

      [9] T.D. Duan et al., “Building an Automatic Vehicle License-Plate Recognition System,†Proc. Int. Conf. Comput. Sci., Cantho, Vietnam, Feb. 2005, pp. 59–63.

      [10] Y. Shima, "Extraction of number plate images based on image category classification using deep learning," 2016 IEEE International Symposium on Robotics and Intelligent Sensors (IRIS), Tokyo, 2016, pp. 19-26.

      [11] Mahamad A.K., Saon S., Aziz S.N.O.A. (2014) A Simplified Malaysian Vehicle Plate Number Recognition. In: Herawan T., Ghazali R., Deris M. (eds) Recent Advances on Soft Computing and Data Mining. Advances in Intelligent Systems and Computing, vol 287. Springer, Cham

      [12] Türkyılmaz, İ. and Kaçan, K. (2017), License Plate Recognition System Using Artificial Neural Networks. ETRI Journal, 39: 163-172.

      [13] B. V. Kakani, D. Gandhi and S. Jani, "Improved OCR based automatic vehicle number plate recognition using features trained neural network," IEEE 2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Delhi, 2017, pp. 1-6.

      [14] R. Shandilya and R. K. Sharma, "FPGA implementation of image enhancement technique for Automatic Vehicles Number Plate detection," IEEE 2017 International Conference on Trends in Electronics and Informatics (ICEI), Tirunelveli, 2017, pp. 1010-1017

      [15] S. S. Omran and J. A. Jarallah, "Iraqi car license plate recognition using OCR," IEEE 2017 Annual Conference on New Trends in Information & Communications Technology Applications (NTICT), Baghdad, 2017, pp. 298-303.

      1. Y. Felix, A. Jesudoss and J. A. Mayan, "Entry and exit monitoring using license plate recognition," 2017 IEEE International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM), Chennai, 2017, pp. 227-231.

      [16] Farhat et al., "OCR based feature extraction and template matching algorithms for Qatari number plate," IEEE 2016 International Conference on Industrial Informatics and Computer Systems (CIICS), Sharjah, 2016, pp. 1-5.

      [17] JosephTarigan Nadia Ryanda Diedan and YayaSuryana " Plate Recognition Using Backpropagation Neural Network and Genetic Algorithm ," Science Direct Procedia Computer Science Volume 116, 2017, Pages 365-372

      [18] N. Wang, X. Zhu and J. Zhang, "License Plate Segmentation and Recognition of Chinese Vehicle Based on BPNN," 2016 12th International Conference on Computational Intelligence and Security (CIS), Wuxi, 2016, pp. 403-406

      [19] Yo-Ping Huang, Shi-Yong Lai and Wei-Po Chuang, "A template-based model for license plate recognition," IEEE International Conference on Networking, Sensing and Control, 2004, 2004, pp. 737-742 Vol.2.
      doi: 10.1109/ICNSC.2004.1297038

      [20] D. Hongyao and S. Xiuli, "License Plate Characters Segmentation Using Projection and Template Matching," 2009 International Conference on Information Technology and Computer Science, Kiev, 2009, pp. 534-537

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  • How to Cite

    Khan, T., J S Yadav, D., & Dheeraj Agarwal, D. (2018). A Broad Survey on Performance Analysis of Number Plate Recognition from Stationary Images and Video Sequences. International Journal of Engineering & Technology, 7(3.10), 164-168. https://doi.org/10.14419/ijet.v7i3.10.15652

    Received date: 2018-07-14

    Accepted date: 2018-07-14

    Published date: 2018-07-15