AI-Based Indian Sign Language Recognition Using Computer Vision: A Review Of Hand Tracking, Feature Learning And Gesture Classification

Indian Sign Language (ISL) is a primary means of communication for many Deaf and hard-of-hearing people, yet few hearing people understand it, creating everyday barriers in education, healthcare and public services. This paper reviews AI-based sign language recognition with a focus on isolated ISL gesture recognition from ordinary camera input. It traces the field from early wearable and colour-glove systems to modern vision pipelines that use real-time hand and pose landmark extraction, convolutional and recurrent networks, and three-dimensional video models. Key datasets, including INCLUDE for ISL, WLASL for word-level American Sign Language and RWTH-PHOENIX for continuous recognition, are discussed alongside the difference between isolated recognition, continuous recognition and full translation. A five-layer architecture is proposed that captures video, preprocesses frames, extracts MediaPipe hand landmarks, classifies gestures with a lightweight neural network and outputs text and speech. The design is presented as a proposed system; no implementation results or accuracy claims are made. Research gaps, objectives, a comparison of feature approaches and challenges such as signer variation, lighting, two-hand occlusion and limited ISL data are analysed.