DYNAMIC SIGN LANGUAGE RECOGNITION AND TRANSLATION THROUGH DEEP LEARNING: A SYSTEMATIC LITERATURE REVIEW

• Main Authors: YESSANE SHRRIE, NAGENDHRA RAO, Chong, Yuan Ting, Rehman Ullah, Khan, Teh, Chee Siong, Mohamad Hardyman, Barawi, Mohd Shahrizal, Sunar, JOAN SIM, JO JO
• Format: Article
• Language: English
• Published: Little Lion Scientific 2024
• Subjects: PN Literature (General)
• Online Access: http://ir.unimas.my/id/eprint/46637/1/DYNAMIC%20SIGN%20LANGUAGE%20RECOGNITION%20AND%20TRANSLATION%20THROUGH%20DEEP%20LEARNING%20A%20SYSTEMATIC%20LITERATURE%20REVIEW28Vol102No21.pdf; http://ir.unimas.my/id/eprint/46637/; https://www.jatit.org/volumes/Vol102No21/28Vol102No21.pdf

Sign language is the communication tool for deaf and hard-of-hearing (DHH) communities all around the world. But it is still difficult to establish proper communication between hearing and DHH individuals. As a result, numerous explorations and investigations that focused on sign language recognition and translation (SLRT) have garnered significant attention from researchers in related fields. This systematic literature review aims to provide a comprehensive study on current trends of state-of-the-art dynamic SLRT models proposed in 85 journal articles found in the Scopus database from 2020 to 2024. Based on the selected articles, this review produced an in-depth analyzation of dynamic SLRT models in terms of their frameworks, deep learning techniques, datasets, pre-processing techniques, and evaluation metrics used. Additionally, this review also highlights both the advancements and ongoing challenges in the domain. Notably, there have been considerable development in isolated and continuous SLRT models, particularly through the combinations of deep learning algorithms such as Convolutional Neural Network, Recurrent Neural Network and Transformer models, with suitable datasets. However, the complexities and challenges of developing robust continuous SLRT models for real-time SLRT persist. This systematic literature review was prepared to serve as a foundational reference that will assist future studies on dynamic SLRT.