License Plate Detection and Recognition of Moving Vehicle Using Open CV

Authors

  • Vivek Yadav Department of Technology, Shivaji University, Kolhapur, India
  • U. L. Bombale Department of Technology, Shivaji University, Kolhapur, India

DOI:

https://doi.org/10.37591/joipprp.v3i2.762

Keywords:

License plate recognition (LPR), background subtraction, mixture of Gaussian (MOG), template matching

Abstract

License plate detection and recognition system is to localize and extract license plate number of moving vehicle in real time environment. The system works on captured video stream of moving vehicle obtained from real working environments. This system is divided into four steps—video acquisition, vehicle detection, license plate detection, and character recognition. Detection of moving objects, i.e., vehicle in video streams is the first relevant step. For vehicle detection, we have used background subtraction method. Next step is license plate detection which is the process of finding license plate location in the image. For plate localization, several traditional image processing techniques are used such as image filtering, edge detection, morphological operations and contour detection; each plays an important role in the extraction process. The extracted license plates are segmented into individual characters by using a contour algorithm and centre line rule method. In this paper, character segmentation is used for correct license plate detection. This paper presents a new character recognition method which improves recognition accuracy of ambiguous characters. This method uses template matching algorithm and structural features of characters. This method improves accuracy as compared to previous template matching method. This paper presents a method for avoiding repetitive recognition of same license plate. 

Cite this Article Yadav V, Bombale UL. License plate detection and recognition of moving vehicle using Open CV. Journal of Image Processing & Pattern Recognition Progress. 2016; 3(2):      32–42p. 

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Published

2016-07-25

Issue

Vol. 3 No. 2 (2016)

Section

Research Articles

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