How We Built an AI-Powered Sign Language Recognition Engine
A deep dive into our computer vision pipeline for real-time sign language gesture recognition using TensorFlow, Rokoko motion capture, and custom neural network architectures.
Otabek Hasanov
ML Engineer
Why Sign Language Recognition Matters
Over 70 million deaf people worldwide use sign language as their primary language. Yet digital tools for sign language education remain scarce, especially for Central Asian sign languages. Our goal was to make sign language learning accessible through AI technology.
Data Collection with Rokoko
We used Rokoko motion capture suits to record precise hand and body movements from professional sign language interpreters. This gave us high-quality 3D skeleton data that we used to train our recognition models.
Model Architecture
Our recognition pipeline uses a combination of MediaPipe for real-time hand tracking and a custom LSTM network for temporal gesture classification. The model achieves 94% accuracy on our test set of 500+ signs.
Real-time Inference
We optimized the model for browser-based inference using TensorFlow.js, enabling students to practice sign language with real-time feedback directly in their web browser without any additional hardware.