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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Main Authors: OLUWASHINA OYENIRAN, EBENEZER OYEBODE
Format: Article
Language:English
Published: Alma Mater Publishing House "Vasile Alecsandri" University of Bacau 2021-10-01
Series:Journal of Engineering Studies and Research
Subjects:
Online Access:https://jesr.ub.ro/1/article/view/278
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author OLUWASHINA OYENIRAN
EBENEZER OYEBODE
author_facet OLUWASHINA OYENIRAN
EBENEZER OYEBODE
author_sort OLUWASHINA OYENIRAN
collection DOAJ
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.
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spelling doaj.art-ef42ab3c65d24a8a84943db18c11497a2024-04-17T19:34:38ZengAlma Mater Publishing House "Vasile Alecsandri" University of BacauJournal of Engineering Studies and Research2068-75592344-49322021-10-01272TRANSFER LEARNING BASED OFFLINE YORÙBÁ HANDWRITTEN CHARACTER RECOGNITION SYSTEMOLUWASHINA OYENIRANEBENEZER OYEBODE 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. https://jesr.ub.ro/1/article/view/278deep learning, Yorùbá, handwritten, character, recognition
spellingShingle OLUWASHINA OYENIRAN
EBENEZER OYEBODE
TRANSFER LEARNING BASED OFFLINE YORÙBÁ HANDWRITTEN CHARACTER RECOGNITION SYSTEM
Journal of Engineering Studies and Research
deep learning, Yorùbá, handwritten, character, recognition
title 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_short TRANSFER LEARNING BASED OFFLINE YORÙBÁ HANDWRITTEN CHARACTER RECOGNITION SYSTEM
title_sort transfer learning based offline yoruba handwritten character recognition system
topic deep learning, Yorùbá, handwritten, character, recognition
url https://jesr.ub.ro/1/article/view/278
work_keys_str_mv AT oluwashinaoyeniran transferlearningbasedofflineyorubahandwrittencharacterrecognitionsystem
AT ebenezeroyebode transferlearningbasedofflineyorubahandwrittencharacterrecognitionsystem