Optimization of ANPR algorithm on android mobile phone

Since the past decades, many researchers proposed their methods to recognize the vehicle number plate. One of the methods is template matching which is executed in the optical character recognition (OCR) step of the automatic number plate recognition (ANPR) system. In previous researches, many resea...

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Main Authors:	Mutholib, Abdul, Gunawan, Teddy Surya, Chebil, Jalel, Kartiwi, Mira
Format:	Conference or Workshop Item
Language:	English
Published:	2013
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://irep.iium.edu.my/34951/ http://irep.iium.edu.my/34951/ http://irep.iium.edu.my/34951/1/Abdul_ANPR_v3.pdf

id	iium-34951
recordtype	eprints
spelling	iium-349512016-05-12T01:30:32Z http://irep.iium.edu.my/34951/ Optimization of ANPR algorithm on android mobile phone Mutholib, Abdul Gunawan, Teddy Surya Chebil, Jalel Kartiwi, Mira TK Electrical engineering. Electronics Nuclear engineering Since the past decades, many researchers proposed their methods to recognize the vehicle number plate. One of the methods is template matching which is executed in the optical character recognition (OCR) step of the automatic number plate recognition (ANPR) system. In previous researches, many researchers are used a high end desktop PC and high resolution camera to implement the ANPR system. In this paper, the optimization of ANPR algorithm on limited hardware of Android mobile phone is presented. First, various steps to optimize ANPR and OCR block using template matching are described. Our proposed algorithm was based on Tesseract library. For comparison purpose, the template matching based OCR will be compared to Artificial Neural Network (ANN) based OCR. The optimization on ANPR was performed as currently there is no image processing tool available on the standard Android mobile phone. By optimization of ANPR, many advantages could be achieved, such as higher recognition accuracy, less resource consumption, and less computational complexity. Results on 30 images showed that the recognition rate was 97.46% while the processing time was 1.13. 2013-11-27 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/34951/1/Abdul_ANPR_v3.pdf Mutholib, Abdul and Gunawan, Teddy Surya and Chebil, Jalel and Kartiwi, Mira (2013) Optimization of ANPR algorithm on android mobile phone. In: 2013 International Conference on Smart Sensor, Measurement, and Applications, 26-27 November 2013, Kuala Lumpur, Malaysia. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6717950
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Mutholib, Abdul Gunawan, Teddy Surya Chebil, Jalel Kartiwi, Mira Optimization of ANPR algorithm on android mobile phone
description	Since the past decades, many researchers proposed their methods to recognize the vehicle number plate. One of the methods is template matching which is executed in the optical character recognition (OCR) step of the automatic number plate recognition (ANPR) system. In previous researches, many researchers are used a high end desktop PC and high resolution camera to implement the ANPR system. In this paper, the optimization of ANPR algorithm on limited hardware of Android mobile phone is presented. First, various steps to optimize ANPR and OCR block using template matching are described. Our proposed algorithm was based on Tesseract library. For comparison purpose, the template matching based OCR will be compared to Artificial Neural Network (ANN) based OCR. The optimization on ANPR was performed as currently there is no image processing tool available on the standard Android mobile phone. By optimization of ANPR, many advantages could be achieved, such as higher recognition accuracy, less resource consumption, and less computational complexity. Results on 30 images showed that the recognition rate was 97.46% while the processing time was 1.13.
format	Conference or Workshop Item
author	Mutholib, Abdul Gunawan, Teddy Surya Chebil, Jalel Kartiwi, Mira
author_facet	Mutholib, Abdul Gunawan, Teddy Surya Chebil, Jalel Kartiwi, Mira
author_sort	Mutholib, Abdul
title	Optimization of ANPR algorithm on android mobile phone
title_short	Optimization of ANPR algorithm on android mobile phone
title_full	Optimization of ANPR algorithm on android mobile phone
title_fullStr	Optimization of ANPR algorithm on android mobile phone
title_full_unstemmed	Optimization of ANPR algorithm on android mobile phone
title_sort	optimization of anpr algorithm on android mobile phone
publishDate	2013
url	http://irep.iium.edu.my/34951/ http://irep.iium.edu.my/34951/ http://irep.iium.edu.my/34951/1/Abdul_ANPR_v3.pdf
first_indexed	2023-09-18T20:50:15Z
last_indexed	2023-09-18T20:50:15Z
_version_	1777409946092044288

Optimization of ANPR algorithm on android mobile phone

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