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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| Format: | Article |
| Language: | English |
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Alma Mater Publishing House "Vasile Alecsandri" University of Bacau
2021-10-01
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| Series: | Journal of Engineering Studies and Research |
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| Online Access: | https://jesr.ub.ro/index.php/1/article/view/278 |
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| _version_ | 1823858316481134592 |
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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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| format | Article |
| id | doaj-art-1724e2cb6dd54a64880c473b01f94c7c |
| institution | Kabale University |
| issn | 2068-7559 2344-4932 |
| language | English |
| publishDate | 2021-10-01 |
| publisher | Alma Mater Publishing House "Vasile Alecsandri" University of Bacau |
| record_format | Article |
| series | Journal of Engineering Studies and Research |
| spelling | doaj-art-1724e2cb6dd54a64880c473b01f94c7c2025-02-11T11:40:13ZengAlma Mater Publishing House "Vasile Alecsandri" University of BacauJournal of Engineering Studies and Research2068-75592344-49322021-10-0127210.29081/jesr.v27i2.278TRANSFER 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/index.php/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/index.php/1/article/view/278 |
| work_keys_str_mv | AT oluwashinaoyeniran transferlearningbasedofflineyorubahandwrittencharacterrecognitionsystem AT ebenezeroyebode transferlearningbasedofflineyorubahandwrittencharacterrecognitionsystem |