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Journal of Intelligent Systems and Internet of Things

Volume 18 , Issue 2 , PP: 304-314, 2026 | Cite this article as | XML | Html | PDF | Full Length Article

Integrating Artificial Intelligence Driven Computer Vision Framework for Enhanced Sign Language Recognition in Hearing and Speech-Impaired People

Inderjeet Kaur 1 , P. Udayakumar 2 , B. Arundhati 3 , M. V. Rajesh 4 , Naif Almakayeel 5 * , Elvir Akhmetshin 6

  • 1 Department of Computer Science and Engineering, Ajay Kumar Garg Engineering College Ghaziabad, India - (inderjeetk@gmail.com)
  • 2 Department of Computer Science and Engineering, Akshaya College of Engineering and Technology, Kinathukadavu, Coimbatore, India - (udayakumarp@acetcbe.edu.in)
  • 3 Department of EEE, Vignan's Institute of Engineering for Women, Visakhapatnam, Andhra Pradesh, India - (b_arundhati@rediffmail.com)
  • 4 Department of Information Technology, Aditya University, Surampalem, India - (rajesh.masina@adityauniversity.in)
  • 5 Department of Industrial Engineering, College of Engineering, King Khalid University, Abha, Saudi Arabia - (halmakaeel@kku.edu.sa)
  • 6 Department of Science, Innovations and Technology, Mamun University, 220900, Khiva, Uzbekistan - (akhmetshin@mamunedu.uz)

    • Doi: https://doi.org/10.54216/JISIoT.180221

    Received: April 07, 2025 Revised: June 26, 2025 Accepted: August 18, 2025
    Abstract

    Sign language (SL) detection and classification for deaf persons is an essential application of machine learning (ML) and computer vision (CV) techniques. It covers emerging forms, which acquire SL implemented by entities and convert them into auditory or textual output. It is highly significant to understand that determining a correct and robust SL detection approach is a very challenging due to many tasks such as alterations in occlusions, and lighting states in hand actions and forms. Consequently, the CV and ML models is must for testing and training. A Hand gesture detection method discovers beneficial for hearing and speaking-impaired individuals by creating usage of convolutional neural network (CNN) and human-computer interface (HCI) for classifying the constant signals of SL. In this article, an Improved Fennec Fox Algorithm for Deep Learning-Based Sign Language Recognition in Hearing and Speaking Impaired People (IFFADL-SLRHSIP) technique is proposed. The presented IFFADL-SLRHSIP technique main intention is to provide effectual communication between deaf and dumb persons and normal persons utilizing CV and artificial intelligence techniques. In the IFFADL-SLRHSIP model, the enhanced SqueezeNet model is used to capture the intricate patterns and nuances of SL gestures. For detection of the SL classification process, the recurrent neural network (RNN) method is used. To optimize model performance, the improved fennec fox algorithm (IFFA) is applied for parameter tuning, enhancing the model's precision and efficiency. The experimental outputs of the IFFADL-SLRHSIP algorithm are legalized on the SL dataset. The simulation outcomes demonstrate the greater outcomes of the IFFADL-SLRHSIP approach in terms of diverse measures.

    Keywords :

    Sign Language Recognition , Fennec Fox Algorithm , Computer Vision , Deep Learning , Hearing-Impaired Person

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    Cite This Article As :
    Kaur, Inderjeet. , Udayakumar, P.. , Arundhati, B.. , V., M.. , Almakayeel, Naif. , Akhmetshin, Elvir. Integrating Artificial Intelligence Driven Computer Vision Framework for Enhanced Sign Language Recognition in Hearing and Speech-Impaired People. Journal of Intelligent Systems and Internet of Things, vol. , no. , 2026, pp. 304-314. DOI: https://doi.org/10.54216/JISIoT.180221
    Kaur, I. Udayakumar, P. Arundhati, B. V., M. Almakayeel, N. Akhmetshin, E. (2026). Integrating Artificial Intelligence Driven Computer Vision Framework for Enhanced Sign Language Recognition in Hearing and Speech-Impaired People. Journal of Intelligent Systems and Internet of Things, (), 304-314. DOI: https://doi.org/10.54216/JISIoT.180221
    Kaur, Inderjeet. Udayakumar, P.. Arundhati, B.. V., M.. Almakayeel, Naif. Akhmetshin, Elvir. Integrating Artificial Intelligence Driven Computer Vision Framework for Enhanced Sign Language Recognition in Hearing and Speech-Impaired People. Journal of Intelligent Systems and Internet of Things , no. (2026): 304-314. DOI: https://doi.org/10.54216/JISIoT.180221
    Kaur, I. , Udayakumar, P. , Arundhati, B. , V., M. , Almakayeel, N. , Akhmetshin, E. (2026) . Integrating Artificial Intelligence Driven Computer Vision Framework for Enhanced Sign Language Recognition in Hearing and Speech-Impaired People. Journal of Intelligent Systems and Internet of Things , () , 304-314 . DOI: https://doi.org/10.54216/JISIoT.180221
    Kaur I. , Udayakumar P. , Arundhati B. , V. M. , Almakayeel N. , Akhmetshin E. [2026]. Integrating Artificial Intelligence Driven Computer Vision Framework for Enhanced Sign Language Recognition in Hearing and Speech-Impaired People. Journal of Intelligent Systems and Internet of Things. (): 304-314. DOI: https://doi.org/10.54216/JISIoT.180221
    Kaur, I. Udayakumar, P. Arundhati, B. V., M. Almakayeel, N. Akhmetshin, E. "Integrating Artificial Intelligence Driven Computer Vision Framework for Enhanced Sign Language Recognition in Hearing and Speech-Impaired People," Journal of Intelligent Systems and Internet of Things, vol. , no. , pp. 304-314, 2026. DOI: https://doi.org/10.54216/JISIoT.180221

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