Automatic Translation From Iraqi Sign Language to Arabic Text or Speech Using CNN | ||
| IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING | ||
| Article 9, Volume 23, Issue 2, June 2023, Pages 112-124 PDF (1.15 M) | ||
| Document Type: Research Paper | ||
| DOI: https://doi.org/10.33103/uot.ijccce.23.2.9 | ||
| Authors | ||
| Raja’a M. Mohammed* 1; Suhad M. Kadhem2 | ||
| 1Baghdad University | ||
| 2Computer 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 | ||
|
Statistics Article View: 113 PDF Download: 96 |
||