Raspberry Pi based ANPR for smart access

Automated Number Plate Recognition (ANPR) is a term that refers to a system that acquire image of a vehicle and recognises the characters on the number plate. The purpose of this paper is to investigate how a Raspberry Pibased ANPR system for smart access can be used to replace the traditional acces...

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Main Authors: Callistus Joann Fernandez, Rosalyn R Porle
Format: Proceedings
Language:English
English
Published: Institute of Electrical and Electronics Engineers 2021
Subjects:
Online Access:https://eprints.ums.edu.my/id/eprint/32522/1/Raspberry%20Pi%20based%20ANPR%20for%20smart%20access.ABSTRACT.pdf
https://eprints.ums.edu.my/id/eprint/32522/2/Raspberry%20Pi%20based%20ANPR%20for%20smart%20access.pdf
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author Callistus Joann Fernandez
Rosalyn R Porle
author_facet Callistus Joann Fernandez
Rosalyn R Porle
author_sort Callistus Joann Fernandez
collection UMS
description Automated Number Plate Recognition (ANPR) is a term that refers to a system that acquire image of a vehicle and recognises the characters on the number plate. The purpose of this paper is to investigate how a Raspberry Pibased ANPR system for smart access can be used to replace the traditional access system for high-rise residents. Number plate recognition was chosen over other systems due to its high level of security. The process of recognising number plates is divided into four stages: image acquisition and preprocessing, extraction, segmentation, and character recognition. Preprocessing involves converting RGB to Grayscale, filtering out noise with a Gaussian Filter, and enhancing the image with Adaptive Thresholding. The number plate extraction step includes morphological operations, image binarization, and contour extraction. The techniques used in segmentation are Connected Component Analysis (CCA) and Boundary Box Analysis (BBA). Character recognition using the KNN method is the final stage. The primary hardware consists of a Raspberry Pi model 4, a Raspberry Pi camera, and servo motors. A total of 120 number plates from 24 different cars were used in the experiments. The number plates are divided into two categories: training and testing, with approximately 83 percent being used for training, which includes approximately 100 plates from four different cars. 17 percent, or approximately 20 number plates from four different cars, are used for testing purposes. The experiment establishes the optimal distance, angle, and height from which to capture the licence plate. At two metres, the system recognises the number plate. The system's design is 85 percent accurate.
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spelling ums.eprints-325222022-05-03T13:25:05Z https://eprints.ums.edu.my/id/eprint/32522/ Raspberry Pi based ANPR for smart access Callistus Joann Fernandez Rosalyn R Porle T1-995 Technology (General) Automated Number Plate Recognition (ANPR) is a term that refers to a system that acquire image of a vehicle and recognises the characters on the number plate. The purpose of this paper is to investigate how a Raspberry Pibased ANPR system for smart access can be used to replace the traditional access system for high-rise residents. Number plate recognition was chosen over other systems due to its high level of security. The process of recognising number plates is divided into four stages: image acquisition and preprocessing, extraction, segmentation, and character recognition. Preprocessing involves converting RGB to Grayscale, filtering out noise with a Gaussian Filter, and enhancing the image with Adaptive Thresholding. The number plate extraction step includes morphological operations, image binarization, and contour extraction. The techniques used in segmentation are Connected Component Analysis (CCA) and Boundary Box Analysis (BBA). Character recognition using the KNN method is the final stage. The primary hardware consists of a Raspberry Pi model 4, a Raspberry Pi camera, and servo motors. A total of 120 number plates from 24 different cars were used in the experiments. The number plates are divided into two categories: training and testing, with approximately 83 percent being used for training, which includes approximately 100 plates from four different cars. 17 percent, or approximately 20 number plates from four different cars, are used for testing purposes. The experiment establishes the optimal distance, angle, and height from which to capture the licence plate. At two metres, the system recognises the number plate. The system's design is 85 percent accurate. Institute of Electrical and Electronics Engineers 2021-09-13 Proceedings PeerReviewed text en https://eprints.ums.edu.my/id/eprint/32522/1/Raspberry%20Pi%20based%20ANPR%20for%20smart%20access.ABSTRACT.pdf text en https://eprints.ums.edu.my/id/eprint/32522/2/Raspberry%20Pi%20based%20ANPR%20for%20smart%20access.pdf Callistus Joann Fernandez and Rosalyn R Porle (2021) Raspberry Pi based ANPR for smart access. https://ieeexplore.ieee.org/document/9573652
spellingShingle T1-995 Technology (General)
Callistus Joann Fernandez
Rosalyn R Porle
Raspberry Pi based ANPR for smart access
title Raspberry Pi based ANPR for smart access
title_full Raspberry Pi based ANPR for smart access
title_fullStr Raspberry Pi based ANPR for smart access
title_full_unstemmed Raspberry Pi based ANPR for smart access
title_short Raspberry Pi based ANPR for smart access
title_sort raspberry pi based anpr for smart access
topic T1-995 Technology (General)
url https://eprints.ums.edu.my/id/eprint/32522/1/Raspberry%20Pi%20based%20ANPR%20for%20smart%20access.ABSTRACT.pdf
https://eprints.ums.edu.my/id/eprint/32522/2/Raspberry%20Pi%20based%20ANPR%20for%20smart%20access.pdf
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