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1. Background and Significance Despite technological advances, deaf and hard-of-hearing people face difficulties with language [1]. Deaf people worldwide face important communication gaps because traditional communication tools struggle to understand sign language’s multidimensionality [2]. Computer-sensor-based machine learning and assistive technology are used in sign language recognition (SLR).
1. Introduction Sign language, a structured sequence of hand motions, provides the most efficient means of communication for deaf people. Hard-of-hearing people have traditionally used sign language to interact with each other. The available approaches to the interpretation of sign language utilize vision techniques to translate signs autonomously. This primarily entails two tasks: isolated or word-based recognition and continuous or sentence-based recognition.
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