Sign Language Recognition: High Performance Deep Learning Approach Applyied To Multiple Sign Languages

Abstract

In this paper we present a high performance Deep Learning architecture based on Convolutional Neural Network (CNN). The proposed architecture is effective as it is capable of recognizing and analyzing with high accuracy different Sign language datasets. The sign language recognition is one of the most important tasks that will change the lives of deaf people by facilitating their daily life and their integration into society. Our approach was trained and tested on an American Sign Language (ASL) dataset, Irish Sign Alphabets (ISL) dataset and Arabic Sign Language Alphabet (ArASL) dataset and outperforms the state-of-the-art methods by providing a recognition rate of 99% for ASL and ISL, and 98% for ArASL.