Malaysian vehicle license plate recognition using deep learning and computer vision

License plate recognition has become one of the popular topics under deep learning researches. There are many deep learning models and the suitable model for this project chose according to the ability to meet the system operation requirements such as speed, accuracy and precision of the outcome. Th...

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Main Authors: Pugalenthy, Kuken Raj, Mohd Zamri, Ibrahim, Ahmad Afif, Mohd Faudzi, Mohd Rizal, Othman
Format: Conference or Workshop Item
Language:English
English
Published: Springer Science and Business Media Deutschland GmbH 2022
Subjects:
Online Access:http://umpir.ump.edu.my/id/eprint/39530/1/Malaysian%20Vehicle%20License%20Plate%20Recognition%20Using%20Deep%20Learning.pdf
http://umpir.ump.edu.my/id/eprint/39530/2/Malaysian%20vehicle%20license%20plate%20recognition%20using%20deep%20learning%20and%20computer%20vision_ABS.pdf
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author Pugalenthy, Kuken Raj
Mohd Zamri, Ibrahim
Ahmad Afif, Mohd Faudzi
Mohd Rizal, Othman
author_facet Pugalenthy, Kuken Raj
Mohd Zamri, Ibrahim
Ahmad Afif, Mohd Faudzi
Mohd Rizal, Othman
author_sort Pugalenthy, Kuken Raj
collection UMP
description License plate recognition has become one of the popular topics under deep learning researches. There are many deep learning models and the suitable model for this project chose according to the ability to meet the system operation requirements such as speed, accuracy and precision of the outcome. Therefore, YOLO (You Only Look Once) model was used which is fast in processing the more images and produce the output at a single look. YOLO is an algorithm designed for multi object detection in a single neural network where it only sees once and process to detect object as many as possible in a picture. In this paper, YOLOv3 is use to detect the position of car registration plate. Next, image warping and slicing applied to straighten the image so it will be easy to feed into character recognition process. Then, the PyTesseract will be used to read the characters from the image together with RegEx function to eliminate the weak predictions from the PyTesseract results. The results obtained from this approach achieved 100% accuracy in recognizing vehicle car plate from 5 video collected from Universiti Malaysia Pahang (UMP) main entrance security gate CCTV system.
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spelling UMPir395302023-12-07T01:14:33Z http://umpir.ump.edu.my/id/eprint/39530/ Malaysian vehicle license plate recognition using deep learning and computer vision Pugalenthy, Kuken Raj Mohd Zamri, Ibrahim Ahmad Afif, Mohd Faudzi Mohd Rizal, Othman T Technology (General) TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering License plate recognition has become one of the popular topics under deep learning researches. There are many deep learning models and the suitable model for this project chose according to the ability to meet the system operation requirements such as speed, accuracy and precision of the outcome. Therefore, YOLO (You Only Look Once) model was used which is fast in processing the more images and produce the output at a single look. YOLO is an algorithm designed for multi object detection in a single neural network where it only sees once and process to detect object as many as possible in a picture. In this paper, YOLOv3 is use to detect the position of car registration plate. Next, image warping and slicing applied to straighten the image so it will be easy to feed into character recognition process. Then, the PyTesseract will be used to read the characters from the image together with RegEx function to eliminate the weak predictions from the PyTesseract results. The results obtained from this approach achieved 100% accuracy in recognizing vehicle car plate from 5 video collected from Universiti Malaysia Pahang (UMP) main entrance security gate CCTV system. Springer Science and Business Media Deutschland GmbH 2022 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/39530/1/Malaysian%20Vehicle%20License%20Plate%20Recognition%20Using%20Deep%20Learning.pdf pdf en http://umpir.ump.edu.my/id/eprint/39530/2/Malaysian%20vehicle%20license%20plate%20recognition%20using%20deep%20learning%20and%20computer%20vision_ABS.pdf Pugalenthy, Kuken Raj and Mohd Zamri, Ibrahim and Ahmad Afif, Mohd Faudzi and Mohd Rizal, Othman (2022) Malaysian vehicle license plate recognition using deep learning and computer vision. In: Lecture Notes in Electrical Engineering; 6th International Conference on Electrical, Control and Computer Engineering, InECCE 2021 , 23 August 2021 , Kuantan, Pahang. pp. 1011-1023., 842 (274719). ISSN 1876-1100 ISBN 978-981168689-4 https://doi.org/10.1007/978-981-16-8690-0_88
spellingShingle T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
Pugalenthy, Kuken Raj
Mohd Zamri, Ibrahim
Ahmad Afif, Mohd Faudzi
Mohd Rizal, Othman
Malaysian vehicle license plate recognition using deep learning and computer vision
title Malaysian vehicle license plate recognition using deep learning and computer vision
title_full Malaysian vehicle license plate recognition using deep learning and computer vision
title_fullStr Malaysian vehicle license plate recognition using deep learning and computer vision
title_full_unstemmed Malaysian vehicle license plate recognition using deep learning and computer vision
title_short Malaysian vehicle license plate recognition using deep learning and computer vision
title_sort malaysian vehicle license plate recognition using deep learning and computer vision
topic T Technology (General)
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
url http://umpir.ump.edu.my/id/eprint/39530/1/Malaysian%20Vehicle%20License%20Plate%20Recognition%20Using%20Deep%20Learning.pdf
http://umpir.ump.edu.my/id/eprint/39530/2/Malaysian%20vehicle%20license%20plate%20recognition%20using%20deep%20learning%20and%20computer%20vision_ABS.pdf
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AT mohdrizalothman malaysianvehiclelicenseplaterecognitionusingdeeplearningandcomputervision