TY  - JOUR
KW  - air-writing; Bengali character; human-computer interaction; hand gestures; machine learning
SN  - 2473-6988
IS  - 3
EP  - 6698
VL  - 9
A1  - Kader, Mohammed Abdul
A1  - Ullah, Muhammad Ahsan
A1  - Islam, Md Saiful
A1  - Ferriol Sánchez, Fermín
A1  - Samad, Md Abdus
A1  - Ashraf, Imran
JF  - AIMS Mathematics
AV  - public
UR  - http://doi.org/10.3934/math.2024325
SP  - 6668
ID  - uneatlantico11066
N2  - 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.
Y1  - 2024/02//
TI  - A real-time air-writing model to recognize Bengali characters
ER  -