Individual Sign Language Recognition

A machine learning task focused on recognizing individual signs from a sign language, translating single signs independently without considering surrounding context. Unlike continuous sign language recognition which attempts to interpret flowing signed sentences, ISLR identifies discrete signs one at a time, making it suitable for applications where a limited vocabulary of specific signs is needed, such as navigating forms or issuing commands. Current approaches use computer vision techniques including RGB video input, skeleton landmarks, and hand pose estimation. While ISLR is more technically feasible than full sign language translation, challenges remain in accuracy, sensitivity calibration, and handling variations in how different signers articulate the same sign.