Real-time Dynamic Sign Recognition using MediaPipe

People live in societies, so communication is necessary to express their needs and ideas. Deaf-mute people use sign language as their means of communication, but most people are unaware of its meaning, so creating an effective system to help them communicate is important. This paper proposes an American Sign Language (ASL) recognition system for 12 dynamic signs in real-time using the MediaPipe framework and a Long Short-Term Memory (LSTM) network. To improve system performance, only the relevant features were extracted from the dynamic sign video, and two new useful features were generated (angles and distances). The proposed system achieved a test accuracy of 97,2%.