Open Access

ARTICLE

Arabic Sign Language Gesture Classification Using Deer Hunting Optimization with Machine Learning Model

by Badriyya B. Al-onazi¹, Mohamed K. Nour², Hussain Alshahran³, Mohamed Ahmed Elfaki³, Mrim M. Alnfiai⁴, Radwa Marzouk⁵, Mahmoud Othman⁶, Mahir M. Sharif⁷, Abdelwahed Motwakel^8,*

1 Department of Language Preparation, Arabic Language Teaching Institute, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
2 Department of Computer Sciences, College of Computing and Information System, Umm Al-Qura University, Saudi Arabia
3 Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra, Saudi Arabia
4 Department of Information Technology, College of Computers and Information Technology, Taif University, Taif P.O. Box 11099, Taif, 21944, Saudi Arabia
5 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
6 Department of Computer Science, Faculty of Computers and Information Technology, Future University in Egypt, New Cairo, 11835, Egypt
7 Department of Computer Science, Faculty of Computer Science and Information Technology, Omdurman Islamic University, Omdurman, 14415, Sudan
8 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia

* Corresponding Author: Abdelwahed Motwakel. Email:

Computers, Materials & Continua 2023, 75(2), 3413-3429. https://doi.org/10.32604/cmc.2023.035303

Received 15 August 2022; Accepted 13 October 2022; Issue published 31 March 2023

Abstract

Sign language includes the motion of the arms and hands to communicate with people with hearing disabilities. Several models have been available in the literature for sign language detection and classification for enhanced outcomes. But the latest advancements in computer vision enable us to perform signs/gesture recognition using deep neural networks. This paper introduces an Arabic Sign Language Gesture Classification using Deer Hunting Optimization with Machine Learning (ASLGC-DHOML) model. The presented ASLGC-DHOML technique mainly concentrates on recognising and classifying sign language gestures. The presented ASLGC-DHOML model primarily pre-processes the input gesture images and generates feature vectors using the densely connected network (DenseNet169) model. For gesture recognition and classification, a multilayer perceptron (MLP) classifier is exploited to recognize and classify the existence of sign language gestures. Lastly, the DHO algorithm is utilized for parameter optimization of the MLP model. The experimental results of the ASLGC-DHOML model are tested and the outcomes are inspected under distinct aspects. The comparison analysis highlighted that the ASLGC-DHOML method has resulted in enhanced gesture classification results than other techniques with maximum accuracy of 92.88%.

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Keywords

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