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A communication gap exists between the hearing and hearing-impaired communities due to a lack of familiarity with 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 classification 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 alphabet classes that achieved an accuracy of 73.3%. Keywords—Arabic Sign Language, image processsing, features, artificial neural network

Translating Arabic Sign Language ARSL To Text Using Artificial Neural Networks