Kader, Mohammed Abdul and Ullah, Muhammad Ahsan and Islam, Md Saiful and Ferriol Sánchez, Fermín and Samad, Md Abdus and Ashraf, Imran UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, fermin.ferriol@unini.edu.mx, UNSPECIFIED, UNSPECIFIED (2024) A real-time air-writing model to recognize Bengali characters. AIMS Mathematics, 9 (3). pp. 6668-6698. ISSN 2473-6988
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Abstract
Air-writing is a widely used technique for writing arbitrary characters or numbers in the air. In this study, a data collection technique was developed to collect hand motion data for Bengali air-writing, and a motion sensor-based data set was prepared. The feature set as then utilized to determine the most effective machine learning (ML) model among the existing well-known supervised machine learning models to classify Bengali characters from air-written data. Our results showed that medium Gaussian SVM had the highest accuracy (96.5%) in the classification of Bengali character from air writing data. In addition, the proposed system achieved over 81% accuracy in real-time classification. The comparison with other studies showed that the existing supervised ML models predicted the created data set more accurately than many other models that have been suggested for other languages.
| Item Type: | Article |
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| Uncontrolled Keywords: | air-writing; Bengali character; human-computer interaction; hand gestures; machine learning |
| Subjects: | Subjects > Engineering |
| Divisions: | Europe University of Atlantic > Research > Scientific Production Ibero-american International University > Research > Scientific Production Ibero-american International University > Research > Scientific Production |
| Depositing User: | Sr Bibliotecario |
| Date Deposited: | 29 Feb 2024 14:34 |
| Last Modified: | 29 Feb 2024 14:34 |
| URI: | http://repositorio.funiber.org/id/eprint/11066 |
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