License Plate Recognition

Authors

  • B. Likith Ram  Department of Information Technology, Vasireddy Venkatadri Institute of Technology, Guntur, Andhra Pradesh, India
  • P. Naga Sai Teja  Department of Information Technology, Vasireddy Venkatadri Institute of Technology, Guntur, Andhra Pradesh, India
  • Y. Sai Avinash Kumar  Department of Information Technology, Vasireddy Venkatadri Institute of Technology, Guntur, Andhra Pradesh, India
  • Ch. Sai Raj  Department of Information Technology, Vasireddy Venkatadri Institute of Technology, Guntur, Andhra Pradesh, India

DOI:

https://doi.org/10.32628/CSEIT2063126

Keywords:

Bilateral Filtering, Canny Edge Detection Algorithm, Tesseract OCR

Abstract

License Plate Recognition (LPR) system is an application of computer vision and image processing technology that takes video of vehicles and take the vehicle frame as input image and by extracting their number plate from whole vehicle image, it displays the number plate information into text. The overall accuracy and efficiency of whole LPR system depends on number plate extraction phase as character segmentation and character recognition phases are also depend on the output of this phase. Higher be the quality of captured input vehicle image more will be the chances of proper extraction of vehicle number plate area. The approach used to segment the image is bilateral filtering algorithm and canny edge detection algorithm. Then we predict the license plate from processed image using py–tesseract OCR and match the retrieved text which is vehicle number plate with database. Finally we get the details of the particular vehicle from the database.

References

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Published

2020-06-30

Issue

Section

Research Articles

How to Cite

[1]
B. Likith Ram, P. Naga Sai Teja, Y. Sai Avinash Kumar, Ch. Sai Raj, " License Plate Recognition" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 6, Issue 3, pp.500-504, May-June-2020. Available at doi : https://doi.org/10.32628/CSEIT2063126