Using Mean Shift for Iranian License Plate Detection

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  • Mohtaram Zandian1,
  • Sedigheh Ghofrani*2,
  1. Islamic Azad University, South Tehran Branch
  2. Islamic Azad University Tehran South Branch

Revised: 2018-07-15

Accepted: 2018-08-20

Published in Issue 2018-12-01

How to Cite

Zandian, M., & Ghofrani, S. (2018). Using Mean Shift for Iranian License Plate Detection. Signal Processing and Renewable Energy (SPRE), 2(4), 61-76. https://oiccpress.com/spre/article/view/7785

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Abstract

In this paper, Mean Shift (MS) as a clustering algorithm is used to localize the Iranian license plate. In this procedure after clustering, based on the optimized MS method, we applied the geometrical features and edge density in order to remove those parts in every cluster, which cannot be the license plate. The main advantages of MS are no need to know the number of clusters and it is completely independent of the Iranian license plate characters colors or background colors. However, for MS implementation, we should only predetermine a parameter named bandwidth. The experimental results show that our proposed method achieves appropriate performance. We should mention that our system accuracy for optical (OP) with 300 images is 94.6% and for infrared (IR) with 80 images is 98.3%.
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