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Sign Language Recognition using Deep Learning

Mahyoub, M, Natalia, F, Sudirman, S and Mustafina, J (2023) Sign Language Recognition using Deep Learning. In: Proceedings - 2023 International Conference on Developments in eSystems Engineering(DeSE) , 2023-J. pp. 184-189. (2023 15th International Conference on Developments in eSystems Engineering (DeSE), 9th Jan to 12th Jan 2023, Baghdad and Anbar, Iraq).

1. Sign Language Recognition using Deep Learning.pdf - Accepted Version

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Sign Language Recognition is a form of action recognition problem. The purpose of such a system is to automatically translate sign words from one language to another. While much work has been done in the SLR domain, it is a broad area of study and numerous areas still need research attention. The work that we present in this paper aims to investigate the suitability of deep learning approaches in recognizing and classifying words from video frames in different sign languages. We consider three sign languages, namely Indian Sign Language, American Sign Language, and Turkish Sign Language. Our methodology employs five different deep learning models with increasing complexities. They are a shallow four-layer Convolutional Neural Network, a basic VGG16 model, a VGG16 model with Attention Mechanism, a VGG16 model with Transformer Encoder and Gated Recurrent Units-based Decoder, and an Inflated 3D model with the same. We trained and tested the models to recognize and classify words from videos in three different sign language datasets. From our experiment, we found that the performance of the models relates quite closely to the model's complexity with the Inflated 3D model performing the best. Furthermore, we also found that all models find it more difficult to recognize words in the American Sign Language dataset than the others.

Item Type: Conference or Workshop Item (Paper)
Additional Information: © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > T Technology (General)
T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Computer Science & Mathematics
SWORD Depositor: A Symplectic
Date Deposited: 12 Jun 2023 16:17
Last Modified: 12 Jun 2023 16:17
DOI or ID number: 10.1109/DeSE58274.2023.10100055
URI: https://researchonline.ljmu.ac.uk/id/eprint/19754
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