Fifth International Conference on Advances in Computing, Communication and Information Technology - CCIT 2017
Author(s) : LINA ELSIDDIG ABDELRAHIM ELSIDDIG , MAYADA MOHAMED ISMAIL MOHAMED
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%.