Number Plate Scanner

Authors

  • Vipul Sharma  Department of Computer Science and Engineering, New Horizon College of Engineering, Bangalore, India
  • Yatharth Mehta  Department of Computer Science and Engineering, New Horizon College of Engineering, Bangalore, India
  • Manushi Dhungana  Department of Computer Science and Engineering, New Horizon College of Engineering, Bangalore, India
  • Ms. Tinu N. S  Department of Computer Science and Engineering, New Horizon College of Engineering, Bangalore, India

Keywords:

Vehicle, License, Number Plate Scanner, Frames, Real Time.

Abstract

Number Plate Scanner (NPS) helps to identify vehicle license plates in an efficient manner without the need for major human resources and has become more and more important the recent years. There are several reasons why their importance has increased. There are a growing number of cars on the roads and all of them have license plates. The rapid development in digital image processing technology has also made it possible to detect and identify license plates at a fast rate. The whole process may be done in less than 50ms. This gives 20 frames per second which is enough to process real-time video streams. Identification of vehicles is useful for many different operators. It can be used by government agencies to find cars that are involved in crime, look up if annual fees are paid or identify persons who violate the traffic rules. U.S., Japan, Germany, Italy, U.K and France are all countries that have successfully applied ALPR in their traffic management.

References

  1. Anuja, P. N., 2011. License Plate Character Recognition System using Neural Network. International Journal of Computer Application, 25(10), pp. 36- 39.
  2. Bharat, B., Singh, S. & Ruchi, S., 2013. License Plate Recognition System using Neural Network and Multi thresolding Technique. International Journal of Computer Applications (0975-8887), 84(5), pp. 45 - 50.
  3. Bo, L., Bin, T. & Ye, L., 2013. Component-based license plate detection using conditional random field model. IEEE Transactions on Intellignet Transportation Systems, 14(4), pp. 1690-1699.
  4. Guo, J. & Liu, Y., 2008. License plate localization and Character segementation with feedback self-learning and hybrid binarization techniques. IEEE Transport Vehicle Technology, 57(3), pp. 1447-1424.M.
  5. Hui, L. & Shen, C., 2016. Reading Car License Plates Using Deep Convolutional Neural Networks and LSTMs Australia:arXiv, pp. 1-17, Technology (ICST)

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Published

2021-05-30

Issue

Section

Research Articles

How to Cite

[1]
Vipul Sharma, Yatharth Mehta, Manushi Dhungana, Ms. Tinu N. S "Number Plate Scanner" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 9, Issue 3, pp.303-306, May-June-2021.