RECOGNITION OF DYNAMIC LITHUANIAN LANGUAGE GESTURES

Abstract

This paper proposes a method for automated Lithuanian hands gestures data collection and for Lithuanian hands gestures classification. The dataset of 1100 samples was collected for 10 different classes of Lithuanian hands gesture. The features of hands gestures were extracted with CNN network. The classification was made with LSTM network. The trained LSTM network classified the Lithuanian hands gestures with 85% accuracy.