Yazeed Alkhrijah^1,2, Shehzad Khalid³, Syed Muhammad Usman^4,*, Amina Jameel³, Danish Hamid⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 1113-1141, 2025, DOI:10.32604/cmes.2025.068726 - 31 July 2025

Abstract Arabic Sign Language (ArSL) recognition plays a vital role in enhancing the communication for the Deaf and Hard of Hearing (DHH) community. Researchers have proposed multiple methods for automated recognition of ArSL; however, these methods face multiple challenges that include high gesture variability, occlusions, limited signer diversity, and the scarcity of large annotated datasets. Existing methods, often relying solely on either skeletal data or video-based features, struggle with generalization and robustness, especially in dynamic and real-world conditions. This paper proposes a novel multimodal ensemble classification framework that integrates geometric features derived from 3D skeletal joint… More >

Khalid M. O. Nahar¹, Ammar Almomani^2,3,*, Nahlah Shatnawi¹, Mohammad Alauthman⁴

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2037-2057, 2023, DOI:10.32604/iasc.2023.038235 - 21 June 2023

Abstract This study presents a novel and innovative approach to automatically translating Arabic Sign Language (ATSL) into spoken Arabic. The proposed solution utilizes a deep learning-based classification approach and the transfer learning technique to retrain 12 image recognition models. The image-based translation method maps sign language gestures to corresponding letters or words using distance measures and classification as a machine learning technique. The results show that the proposed model is more accurate and faster than traditional image-based models in classifying Arabic-language signs, with a translation accuracy of 93.7%. This research makes a significant contribution to the More >

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,*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3413-3429, 2023, DOI:10.32604/cmc.2023.035303 - 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 More >