Translating Arabic Sign Language (ARSL) To Text Using Artificial Neural Networks

LINA ELSIDDIG, ABDELRAHIM ELSIDDIG and MAYADA MOHAMED, ISMAIL MOHAMED (2017) Translating Arabic Sign Language (ARSL) To Text Using Artificial Neural Networks. In: Fifth International Conference on Advances in Computing, Communication and Information Technology - CCIT 2017, 02-03 September, 2017, Zurich, Switzerland.

[img]
Preview
Text
20170922_040823.pdf - Published Version

Download (601kB) | Preview
Official URL: https://www.seekdl.org/conferences/paper/details/9...

Abstract

A communication gap exists between the hearing and hearing - impaired communities due to a lack of familiarity wi th the means of communication of each. This research attempts to bridge this distance by creating Arabic Sign Language (ArSL) datasets, which there is a lack of, image processing , selecting a feature extraction method and designing a machine learning class ification system capable of translating Arabic Sign Language (ArSL) to text. The system was implemented on MATLAB 2014a using an Artificial Neural Network that was trained on the morphological features of 100 samples to classify input images into 3 alphabe t classes that achieved an accuracy of 73.3%.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Arabic Sign Language, image processsing,features, artificial neural network
Depositing User: Mr. John Steve
Date Deposited: 11 Mar 2019 08:30
Last Modified: 11 Mar 2019 08:30
URI: http://publications.theired.org/id/eprint/389

Actions (login required)

View Item View Item