Cover
Vol. 22 No. 1 (2026)

Published: June 15, 2026

Pages: 502-511

Original Article

Enhancing American Sign Language Recognition Through Transfer Learning Techniques

Wahhab Muslim Mashloosh 1 Murteza Hanoon Tuama 1 Ghadah Adil Najm 2
1 Department of Computer Techniques Engineering, Imam Al-Kadhum College (IKC), Baghdad, Iraq
2 Department of Science, Directorate of Al Qadissiya Education, Ministry of Education, Qadissiya, Iraq
History
  • Received: November 4, 2023
  • Revised: April 15, 2024
  • Accepted: April 25, 2024
  • Available Online: June 15, 2026
DOI

https://doi.org/10.37917/ijeee.22.1.44

Abstract

Aiming to enhance the accuracy of sign classification in sign language (SL), this research presents an innovative approach that combines hand-engineered characteristics with deep learning (DL) algorithms. The focus is on American Sign Language (ASL), a critical communication tool for the deaf and hard-of-hearing community. The goal is to bridge the existing communication chasm between SL users and the general public by designing a real-time SL recognition system that allows non- SL users to converse with the hearing-impaired individuals. The application and assessment of various machine learning (ML) models, such as VGG19, DenseNet, ResNet50, MobileNet, and NASNetMobile, yielded promising outcomes with superior evalu- ation metrics. These models exhibit utility in the classification of ASL signs as they can differentiate between diverse hand gestures with high accuracy (ACC). The paper highlights the potential of these models across an array of ASL recognition applica- tions, considering factors like computational resources, model dimension, and real-time functionality. The findings endorse the application of ML techniques in SL interpretation, promoting inclusive communication for those with hearing impairment.

Keywords

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