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

E3S Web of Conferences

El Zaar Abdellah¹  Benaya Nabil¹  El Allati Abderrahim¹ 

[1] Laboratory of R&D in Engineering Sciences, FST Al-Hoceima, Abdelmalek Essaadi University;

关键词: sign language recognition;    machine learning;    deep learning;    convolutional neural network;    object recognition;   

DOI  :  10.1051/e3sconf/202235101065

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown