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

作　　者：Badriyya B.Al-onazi Mohamed K.Nour Hussain Alshahran Mohamed Ahmed Elfaki Mrim M.Alnfiai Radwa Marzouk Mahmoud Othman Mahir M.Sharif Abdelwahed Motwakel 

机构地区：[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

出　　处：《Computers, Materials & Continua》2023年第5期3413-3429,共17页计算机、材料和连续体（英文）

基　　金：Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2023R263);Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia;The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University;supporting this work by Grant Code:22UQU4310373DSR54.

摘　　要：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%.

关 键 词：Machine learning sign language recognition multilayer perceptron deer hunting optimization densenet 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]