Citation

Nath, Joyonta Kumar and Alam, S. A. Mahadi and Ullah, Yasir and Dhar, Miso and Dewanjee, Joy and Umair, Muhammad and Kamal, Shahid and Abdullah, Zul Hilmi (2026) Visual deep learning approaches for alphabetic sign language interpretation. Scientific Reports, 16 (1). ISSN 2045-2322

Text s41598-026-57945-w.pdf - Published Version Restricted to Repository staff only Download (3MB)

Abstract

Sign language plays a vital role in enabling interaction for individuals with hearing and speech impairment, making accurate alphabet-level recognition a fundamental requirement for accessible human-computer interaction systems. This paper investigates visual deep learning approaches for interpreting sign language alphabets from image data. A systematic framework is developed to assess the effectiveness of transfer learning-based convolutional neural networks in capturing discriminative hand gesture features. Using a benchmark American Sign Language (ASL) alphabet dataset, several advanced architectures, including ConvNeXtXLarge, EfficientNet, VGG19, and ResNet-50, are examined under a unified experimental protocol. The comparative analysis reveals that ConvNeXtXLarge achieves the uppermost recognition performance, attaining an accuracy of 99.81%, while EfficientNet, VGG19, and ResNet-50 also demonstrate strong results with accuracy of 99.68%, 99.31%, and 97.29%, respectively. These findings emphasize the effectiveness of modern transfer learning strategies in enhancing visual representation learning for fine-grained motion recognition. The proposed evaluation framework offers practical insights into model selection for scalable and reliable sign language interpretation systems, contributing to the advancement of inclusive assistive technologies and real-world visual language understanding applications.

Item Type:	Article
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	01 Oct 2026 01:40
Last Modified:	01 Oct 2026 01:40
URII:	http://shdl.mmu.edu.my/id/eprint/16744

Downloads

Edit (login required)