Hypertuned Deep Convolutional Neural Network for Sign Language Recognition

Abdul Mannan; Ahmed Abbasi; Abdul Rehman Javed; Anam Ahsan; Thippa Reddy Gadekallu; Qin Xin

doi:10.1155/2022/1450822

Hypertuned Deep Convolutional Neural Network for Sign Language Recognition

Comput Intell Neurosci. 2022 Apr 30:2022:1450822. doi: 10.1155/2022/1450822. eCollection 2022.

Authors

Abdul Mannan¹, Ahmed Abbasi¹, Abdul Rehman Javed¹, Anam Ahsan², Thippa Reddy Gadekallu³, Qin Xin⁴

Affiliations

¹ Department of Cyber Security, Air University, Islamabad, Pakistan.
² Department of Computer Science, University of Lahore, Lahore, Pakistan.
³ Vellore Institute of Technology, Vellore, Tamil Nadu, India.
⁴ Faculty of Science and Technology, University of the Faroe Islands, Vestarabryggja 15 FO 100, Torshavn, Faroe Islands.

Abstract

Sign language plays a pivotal role in the lives of impaired people having speaking and hearing disabilities. They can convey messages using hand gesture movements. American Sign Language (ASL) recognition is challenging due to the increasing intra-class similarity and high complexity. This paper used a deep convolutional neural network for ASL alphabet recognition to overcome ASL recognition challenges. This paper presents an ASL recognition approach using a deep convolutional neural network. The performance of the DeepCNN model improves with the amount of given data; for this purpose, we applied the data augmentation technique to expand the size of training data from existing data artificially. According to the experiments, the proposed DeepCNN model provides consistent results for the ASL dataset. Experiments prove that the DeepCNN gives a better accuracy gain of 19.84%, 8.37%, 16.31%, 17.17%, 5.86%, and 3.26% as compared to various state-of-the-art approaches.

Publication types

Retracted Publication

MeSH terms

Gestures
Humans
Movement
Neural Networks, Computer*
Recognition, Psychology
Sign Language*