TRANSFER LEARNING BASED OFFLINE YORÙBÁ HANDWRITTEN CHARACTER RECOGNITION SYSTEM

This study presents Transfer Learning-based framework through the use of AlexNet for the development of an offline Yorùbá Handwritten Character Recognition System. The system encompasses the upper and case characters of the Yorùbá language, and tonal letters that have a significant impact on the Yo...

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Autores principales:	OLUWASHINA OYENIRAN, EBENEZER OYEBODE
Formato:	article
Lenguaje:	EN
Publicado:	Alma Mater Publishing House "Vasile Alecsandri" University of Bacau 2021
Materias:	deep learning, Yorùbá, handwritten, character, recognition Technology T Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/6de6887ce8fe42e28526511ce9b49131
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id	oai:doaj.org-article:6de6887ce8fe42e28526511ce9b49131
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spelling	oai:doaj.org-article:6de6887ce8fe42e28526511ce9b491312021-12-02T18:36:09ZTRANSFER LEARNING BASED OFFLINE YORÙBÁ HANDWRITTEN CHARACTER RECOGNITION SYSTEM2068-75592344-4932https://doaj.org/article/6de6887ce8fe42e28526511ce9b491312021-10-01T00:00:00Zhttp://www.jesr.ub.ro/1/article/view/278https://doaj.org/toc/2068-7559https://doaj.org/toc/2344-4932 This study presents Transfer Learning-based framework through the use of AlexNet for the development of an offline Yorùbá Handwritten Character Recognition System. The system encompasses the upper and case characters of the Yorùbá language, and tonal letters that have a significant impact on the Yorùbá language. The model reported network accuracy of 82.8%, validation accuracy of 77.7%, with F1 score of 0.7795, precision of 0.7819 and Recall of 0.7771. While the average recognition time is estimated to 0.371372 seconds. Thus, the technique of deep learning has shown significant improvement when compared to other existing approaches in recognizing standard Yorùbá characters. OLUWASHINA OYENIRANEBENEZER OYEBODEAlma Mater Publishing House "Vasile Alecsandri" University of Bacauarticledeep learning, Yorùbá, handwritten, character, recognitionTechnologyTEngineering (General). Civil engineering (General)TA1-2040ENJournal of Engineering Studies and Research, Vol 27, Iss 2 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	deep learning, Yorùbá, handwritten, character, recognition Technology T Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	deep learning, Yorùbá, handwritten, character, recognition Technology T Engineering (General). Civil engineering (General) TA1-2040 OLUWASHINA OYENIRAN EBENEZER OYEBODE TRANSFER LEARNING BASED OFFLINE YORÙBÁ HANDWRITTEN CHARACTER RECOGNITION SYSTEM
description	This study presents Transfer Learning-based framework through the use of AlexNet for the development of an offline Yorùbá Handwritten Character Recognition System. The system encompasses the upper and case characters of the Yorùbá language, and tonal letters that have a significant impact on the Yorùbá language. The model reported network accuracy of 82.8%, validation accuracy of 77.7%, with F1 score of 0.7795, precision of 0.7819 and Recall of 0.7771. While the average recognition time is estimated to 0.371372 seconds. Thus, the technique of deep learning has shown significant improvement when compared to other existing approaches in recognizing standard Yorùbá characters.
format	article
author	OLUWASHINA OYENIRAN EBENEZER OYEBODE
author_facet	OLUWASHINA OYENIRAN EBENEZER OYEBODE
author_sort	OLUWASHINA OYENIRAN
title	TRANSFER LEARNING BASED OFFLINE YORÙBÁ HANDWRITTEN CHARACTER RECOGNITION SYSTEM
title_short	TRANSFER LEARNING BASED OFFLINE YORÙBÁ HANDWRITTEN CHARACTER RECOGNITION SYSTEM
title_full	TRANSFER LEARNING BASED OFFLINE YORÙBÁ HANDWRITTEN CHARACTER RECOGNITION SYSTEM
title_fullStr	TRANSFER LEARNING BASED OFFLINE YORÙBÁ HANDWRITTEN CHARACTER RECOGNITION SYSTEM
title_full_unstemmed	TRANSFER LEARNING BASED OFFLINE YORÙBÁ HANDWRITTEN CHARACTER RECOGNITION SYSTEM
title_sort	transfer learning based offline yorùbá handwritten character recognition system
publisher	Alma Mater Publishing House "Vasile Alecsandri" University of Bacau
publishDate	2021
url	https://doaj.org/article/6de6887ce8fe42e28526511ce9b49131
work_keys_str_mv	AT oluwashinaoyeniran transferlearningbasedofflineyorubahandwrittencharacterrecognitionsystem AT ebenezeroyebode transferlearningbasedofflineyorubahandwrittencharacterrecognitionsystem
_version_	1718377867334647808

TRANSFER LEARNING BASED OFFLINE YORÙBÁ HANDWRITTEN CHARACTER RECOGNITION SYSTEM

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