Document Type : Research Paper

Authors

• Raja’a M. Mohammed ¹
• Suhad M. Kadhem ²

¹ Baghdad University

² Computer Science, University of Technology , Baghdad, Iraq.

Abstract

Sign language (SL) is Non-verbal communication and a way for the
deaf and mute to communicate without words. A deaf and mute person's hands,
face, and body shows what they want to say. Since the number of deaf and dumb
people is increasing, there must be other ways to learn sign language or
communicate with deaf and dumb people. One of these ways is using advanced
technology to produce systems that help the deaf/dumb, such as creating
recognition and sign language translators. This paper presents an application
that works on the computer for machine translation of Iraqi sign language in
two directions from sign language to Arabic language (text/speech) and from
Arabic language(text) to Iraqi sign language. The proposed system uses a
Convolution Neural Network (CNN) to classify sign language based on its
features to predicate the sign meaning. The sign language to Arabic
language(text/speech) part of the proposed system has an accuracy of 99.3% for
letters.

Keywords

• Sign Language
• Iraqi Sign Language
• Deep Learning
• CNN
• gtts

Main Subjects

• Computer