CNN Algorithm with SIFT to Enhance the Arabic Sign Language Recognition
Manar Hamza Bashaa1, Faezah Hamad Almasoudy2, Noor S. Sagheer3, Wasan Mueti Hadi4
1Manar Hamza Bashaa, Department of Computer Science, College of Computer Science and Information Technology, Kerbala University, Kerbala, Iraq.
2Faezah Hamad Almasoudy, Department of Animals Production, College of Agriculture, Kerbala University, Kerbala, Iraq.
3Noor S. Sagheer, Department of Computer Science, College of Computer Science and Information Technology, Kerbala University, Kerbala, Iraq.
4Wasan Mueti Hadi, Department of Computer Science, College of Computer Science and Information Technology, Kerbala University, Kerbala, Iraq.
Manuscript received on 24 August 2024 | Revised Manuscript received on 02 September 2024 | Manuscript Accepted on 15 September 2024 | Manuscript published on 30 September 2024 | PP: 12-17 | Volume-12 Issue-10, September 2024 | Retrieval Number: 100.1/ijese.I258412100924 | DOI: 10.35940/ijese.I2584.12100924
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Sign language is used as a primary means of communication by millions of people who suffer from hearing problems. The unhearing people used visual language to interact with each other, Represented in sign language. There are features that the hearing impaired use to understand each other, which are difficult for normal people to understand. Therefore, deaf people will struggle to interact with society. This research aims to introduce a system for recognizing hand gestures in Arabic Sign Language (ArSL) through training the Convolutional Neural Network (CNN) on the images of ArSL gestures launched by the University of Prince Mohammad Bin Fahd, Saudi Arabia. A Scale Invariant Feature Transform (SIFT) algorithm is used for creating the feature vectors that contain shape, finger position, size, center points of palm, and hand margin by extracting the Important features for images of ArSL and transforming them to points of the vector. The accuracy of the proposed system is 97% using the SIFT with CNN, and equal to 94.8% nearly without SIFT. Finally, the proposed system was tried and tested on a group of persons and its effectiveness was proven after considering their observations.
Keywords: Arabic Language, CNN Classification, Deep Learning, Image Classification, SIFT.
Scope of the Article: Artificial Intelligence & Methods