Estimation of Automatic License Plate Recognition Using Deep Learning Algorithms

Kattur Soundarapandian Ravichandran

doi:10.31181/sdmap21202512

Authors

Kattur Soundarapandian Ravichandran Department of Mathematics, Amrita School of Physical Sciences, Amrita Vishwa Vidyapeetham, India Author https://orcid.org/0000-0003-2397-461X

DOI:

https://doi.org/10.31181/sdmap21202512

Keywords:

Automatic License Plate Recognition, Preprocessing, Deep Learning, YOLO, Optical Character Recognition

Abstract

Automatic License Plate Recognition (ALPR) or Automatic Number Plate Recognition (ANPR) is the technology responsible for reading the License plates of a vehicle in an image or a video sequence using optical character recognition (OCR). With the latest advancements in Deep Learning and Computer Vision, these tasks can be done in a matter of milliseconds. Much work has been proposed in recent days for number plate identification through OCR with the standard datasets available in an open database. But, in a real-time scenario, there are many external events like rain, heavy wind, and other reasons; the top of the plates may be affected by pasting sand or hair-like objects on it. The resultant images are called noisy input images. In this paper, we have applied various preprocessing techniques like noise removal algorithm, extended hair-removal algorithm, etc., before applying deep learning algorithms, including the latest version of YOLO v8. Finally, it is concluded that the proposed model performs better than the recent state of the technology proposed.

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