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. 2022 Aug 24;10(9):1606.
doi: 10.3390/healthcare10091606.

Atom Search Optimization with Deep Learning Enabled Arabic Sign Language Recognition for Speaking and Hearing Disability Persons

Radwa Marzouk  1   2 , Fadwa Alrowais  3 , Fahd N Al-Wesabi  4 , Anwer Mustafa Hilal  5
Affiliations

Atom Search Optimization with Deep Learning Enabled Arabic Sign Language Recognition for Speaking and Hearing Disability Persons

Radwa Marzouk et al. Healthcare (Basel). .

Abstract

Sign language has played a crucial role in the lives of impaired people having hearing and speaking disabilities. They can send messages via hand gesture movement. Arabic Sign Language (ASL) recognition is a very difficult task because of its high complexity and the increasing intraclass similarity. Sign language may be utilized for the communication of sentences, letters, or words using diverse signs of the hands. Such communication helps to bridge the communication gap between people with hearing impairment and other people and also makes it easy for people with hearing impairment to express their opinions. Recently, a large number of studies have been ongoing in developing a system that is capable of classifying signs of dissimilar sign languages into the given class. Therefore, this study designs an atom search optimization with a deep convolutional autoencoder-enabled sign language recognition (ASODCAE-SLR) model for speaking and hearing disabled persons. The presented ASODCAE-SLR technique mainly aims to assist the communication of speaking and hearing disabled persons via the SLR process. To accomplish this, the ASODCAE-SLR technique initially pre-processes the input frames by a weighted average filtering approach. In addition, the ASODCAE-SLR technique employs a capsule network (CapsNet) feature extractor to produce a collection of feature vectors. For the recognition of sign language, the DCAE model is exploited in the study. At the final stage, the ASO algorithm is utilized as a hyperparameter optimizer which in turn increases the efficacy of the DCAE model. The experimental validation of the ASODCAE-SLR model is tested using the Arabic Sign Language dataset. The simulation analysis exhibit the enhanced performance of the ASODCAE-SLR model compared to existing models.

Keywords: atom search optimization; deep learning; disabled persons; quality of life; sign language recognition.

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Conflict of interest statement

The authors declare that they have no conflict of interest. The manuscript was written with the contribution of all authors. All authors have approved the final version of the manuscript.

Figures

Figure 1
Figure 1
Structure of CapsNet model.
Figure 2
Figure 2
Flowchart of ASO technique.
Figure 3
Figure 3
Confusion matrix of ASODCAE-SLR approach under entire dataset.
Figure 4
Figure 4
Confusion matrix of ASODCAE-SLR approach under 70% of TR data.
Figure 5
Figure 5
Confusion matrix of ASODCAE-SLR approach under 30% of TS data.
Figure 6
Figure 6
TRA and VLA analysis ASODCAE-SLR approach.
Figure 7
Figure 7
TRL and VLL analysis ASODCAE-SLR approach.
Figure 8
Figure 8
Precision-recall analysis ASODCAE-SLR approach.
Figure 9
Figure 9
ROC analysis ASODCAE-SLR approach.
Figure 10
Figure 10
Comparative analysis of ASODCAE-SLR approach with existing algorithms.
See this image and copyright information in PMC

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