American Sign Language Interpreter Using an RGB-D Camera and Machine Learning

Clients

Techstack

Purpose

Deaf and hard-of-hearing signers still hit a wall in everyday conversations with people who don't know sign language. Reading American Sign Language automatically is genuinely hard: gestures unfold as motion over time, not as single frozen poses, so a system has to understand movement, not just shapes.

Description

We built a real-time interpreter that watches a signer through a depth-sensing camera, tracks the position of their hands frame by frame, and feeds those motion sequences into deep-learning models that recognize each gesture as it happens. Pairing a spatial vision model with one tuned for sequences let the system read signs as continuous movement rather than isolated snapshots, and we validated every gesture with rigorous accuracy analysis. The result runs live from an ordinary webcam feed, exactly the kind of hard motion-recognition problem we take on and ship.