Sign Language Translator: A Real-Time Communication Solution for the Deaf and Hard-of-Hearing Community
Sankalp Patil, Rishi Isaraddi · 2025
This research introduces a novel vision-based sign language interpretation framework that employs contemporary computer vision alongside advanced machine learning methodologies to transform manual gestures into textual and auditory outputs [1], [2]. The proposed system aims to dissolve communication barriers experienced by individuals with hearing impairments, promoting greater social inclusion and accessibility [3]. Through the integration of skeletal tracking, gesture interpretation algorithms, and language processing techniques, our implementation achieves substantial precision with minimal processing delay, rendering it appropriate for practical deployment [4]. Experimental validation demonstrates a composite accuracy rate of 92%, with false acceptance and rejection rates of 0.2% and 1.3% respectively. User experience assessments reveal strong satisfaction indicators, with 88 % of participants noting intuitive operation and 90 % indicating enthusiasm for continued utilization [5]. This investigation contributes to assistive technology advancements by offering a resilient, user-centered, and culturally appropriate communication solution [6].