An Enhanced Training- Based Arabic Sign Language Virtual Interpreter Using Parallel Recurrent Neural Networks

Abstract

Intelligent machine translation systems have a remarkable importance in integrating people with disabilities in community. Arabic to Arabic sign language systems are limited. Deep Learning (DL) was successfully applied to problems related to music information retrieval, image recognition and text recognition, but its use in sign language recognition is rare. This paper introduces an automatic virtual translation system from Arabic language into Arabic Sign Language (ASL) via a popular DL architecture: The Recurrent Neural Network (RNN). The proposed system uses a deep neural network training-based system for ASL that convolves RNN and Graphical Processing Unit (GPU) parallel processors. The system is evaluated using both objective and subjective measures. Obtained results are towards reducing errors, speeding up avatar and expressing signs and facial expressions in a well-received manner by Deaf. The signing avatar is highly encouraged as a simulator for natural human signs.