Abstract:
This thesis explores the development of an automated SL production system using cuttingedge advancements in natural language processing (NLP) & computer vision. Motivated
by the growing need for inclusive communication solutions, especially for the deaf & hardof-hearing community, the project focuses on generating SL poses from input text. The
proposed system leverages a series of interconnected processes, including semantic &
grammar correction, translation, word validation & substitution, pose generation, & pose
stitching. By ensuring language agnosticism & adaptability across multiple languages, the
system aims to bridge communication gaps for deaf individuals in various social, legal, &
corporate settings. This research is grounded in an extensive review of the current
methodologies in SL recognition & production, highlighting the limitations & potential
improvements in existing systems. The final evaluation of the system demonstrates
promising results in producing accurate & comprehensible SL videos, contributing to the
ongoing efforts to promote accessibility & inclusivity for the deaf community.
