Sign Language Recognition Using ML and DL

Dynamic Sign Language Recognition (DSLR) is a vital area of research with applications in human–computer interaction, accessibility, and communication. This study explores the utilization of Machine Learning (ML) and Deep Learning (DL) techniques for DSLR, bridging the gap between computer vision and natural language processing. We present an overview of the motivation and context for applying ML and DL to DSLR, encompassing recent advancement from 2010 to 2023. The reviewed works are categorized based on their ML and DL strategies, highlighting their contributions to the field. Additionally, we address critical prerequisites for deploying ML and DL-based DSLR systems in real-world settings, offering a qualitative assessment of how well the surveyed works fulfill these criteria. We conclude by discussing unresolved challenges and future directions in DSLR research. Furthermore, our research will involve tracking emotions, hand gestures, and lip tracing within the context of DSLR. Enhancing the comprehensiveness of sign language interpretation. We conclude by discussing unresolved challenges and future directions in DSLR research.