Article
Machine Learning-Based Classification and Recognition of Indian Sign Language
The discourse surrounding communication barriers for deaf individuals is increasingly recognized as a significant issue. People with hearing impairments often utilize various strategies to interact with others, necessitating specific resources to facilitate engagement. Developing a sign language application would greatly benefit deaf users and enhance communication with those unfamiliar with sign language. Our initiative aims to bridge the gap between hearing individuals and those who communicate through signs. The primary objective of this study is to create a perception-based paradigm to distinguish gestures visually. Vision-based systems are particularly effective as they provide a more intuitive means of interaction between humans and computers. This research focuses on 46 different gestures and incorporates both temporal and spatial dimensions from video sequences to classify sign language movements.
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