Kiran, Pandian and Mohd Azraai, Mohd Razman and Ismail, Mohd Khairuddin and Muhammad Amirul, Abdullah and Ahmad Fakhri, Ab. Nasir and Wan Hasbullah, Mohd Isa (2023) Sign language recognition using deep learning through LSTM and CNN. Mekatronika - Journal of Intelligent Manufacturing & Mechatronics, 5 (1). pp. 67-71. ISSN 2637-0883. (Published)
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Abstract
This study presents the application of using deep learning to detect, recognize and translate sign language. Understanding sign language is crucial for communication between the deaf and mute people and the general society. This helps sign language users to easily communicate with others, thus eliminating the differences between both parties. The objectives of this thesis are to extract features from the dataset for sign language recognition model and the formulation of deep learning models and the classification performance to carry out the sign language recognition. First, we develop methodology for an efficient recognition of sign language. Next is to develop multiple system using three different model which is LSTM, CNN and YOLOv5 and compare the real time test result to choose the best model with the highest accuracy. We used same datasets for all algorithms to determine the best algorithm. The YOLOv5 has achieved the highest accuracy of 97% followed by LSTM and CNN with 94% and 66.67%.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Sign Language; Deaf; CNN; LSTM |
| Subjects: | Q Science > QA Mathematics > QA76 Computer software T Technology > TJ Mechanical engineering and machinery |
| Faculty/Division: | Institute of Postgraduate Studies Faculty of Computing Faculty of Manufacturing and Mechatronic Engineering Technology |
| Depositing User: | Mrs Norsaini Abdul Samat |
| Date Deposited: | 20 Jul 2023 01:28 |
| Last Modified: | 20 Jul 2023 01:28 |
| URI: | http://umpir.ump.edu.my/id/eprint/38079 |
| Download Statistic: | View Download Statistics |
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