Sign Language Recognition and Classification Model to Enhance Quality of Disabled People

Alrowais, Fadwa; Alotaibi, Saud S.; Dhahbi, Sami; Marzouk, Radwa; Mohamed, Abdullah; Hilal, Anwer Mustafa

doi:10.32604/cmc.2022.029438

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…Table 3 reports an overall comparison analysis of the SLDC-RSAHDL technique in terms of recognition rate (RR) and computation time (CT) [ 23 ]. In Fig.…”

Section: Experimental Evaluationmentioning

confidence: 99%

Automated sign language detection and classification using reptile search algorithm with hybrid deep learning

Alsolai,

Al-Wesabi

et al. 2024

Heliyon

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“…Table 3 reports an overall comparison analysis of the SLDC-RSAHDL technique in terms of recognition rate (RR) and computation time (CT) [ 23 ]. In Fig.…”

Section: Experimental Evaluationmentioning

confidence: 99%

Automated sign language detection and classification using reptile search algorithm with hybrid deep learning

Alsolai,

Al-Wesabi

et al. 2024

Heliyon

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Robust sign language detection for hearing disabled persons by Improved Coyote Optimization Algorithm with deep learning

Asiri,

Motwakel,

Drar

2024

MATH

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<abstract> <p>Sign language (SL) recognition for individuals with hearing disabilities involves leveraging machine learning (ML) and computer vision (CV) approaches for interpreting and understanding SL gestures. By employing cameras and deep learning (DL) approaches, namely convolutional neural networks (CNN) and recurrent neural networks (RNN), these models analyze facial expressions, hand movements, and body gestures connected with SL. The major challenges in SL recognition comprise the diversity of signs, differences in signing styles, and the need to recognize the context in which signs are utilized. Therefore, this manuscript develops an SL detection by Improved Coyote Optimization Algorithm with DL (SLR-ICOADL) technique for hearing disabled persons. The goal of the SLR-ICOADL technique is to accomplish an accurate detection model that enables communication for persons using SL as a primary case of expression. At the initial stage, the SLR-ICOADL technique applies a bilateral filtering (BF) approach for noise elimination. Following this, the SLR-ICOADL technique uses the Inception-ResNetv2 for feature extraction. Meanwhile, the ICOA is utilized to select the optimal hyperparameter values of the DL model. At last, the extreme learning machine (ELM) classification model can be utilized for the recognition of various kinds of signs. To exhibit the better performance of the SLR-ICOADL approach, a detailed set of experiments are performed. The experimental outcome emphasizes that the SLR-ICOADL technique gains promising performance in the SL detection process.</p> </abstract>

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Enhanced Bald Eagle Search Optimizer with Transfer Learning-based Sign Language Recognition for Hearing-impaired Persons

Asiri,

Motwakel,

Drar

2023

Journal of Disability Research

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Sign language recognition and classification for hearing-impaired people is a vital application of computer vision (CV) and machine learning (ML) approaches. It contains developing structures that take sign language gestures carried out by individuals and transform them into textual or auditory output for transmission aspects. It is critical to realize that establishing a robust and correct sign language recognition and classification method is a difficult task because of several challenges like differences in signing styles, occlusions, lighting conditions, and individual variances in hand movements and shapes. Thus, it needs a group of CV approaches, ML systems, and a varied and representative database for training and testing. In this study, we propose an Enhanced Bald Eagle Search Optimizer with Transfer Learning Sign Language Recognition (EBESO-TLSLR) technique for hearing-impaired persons. The presented EBESO-TLSLR technique aims to offer effective communication among hearing-impaired persons and normal persons using deep learning models. In the EBESO-TLSLR technique, the SqueezeNet model is used for feature map generation. For recognition of sign language classes, the long short-term memory (LSTM) method can be used. Finally, the EBESO approach is exploited for the optimal hyperparameter election of the LSTM method. The simulation results of the EBESO-TLSLR method are validated on the sign language dataset. The simulation outcomes illustrate the superior results of the EBESO-TLSLR technique in terms of different measures.

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Sign Language Recognition and Classification Model to Enhance Quality of Disabled People

Cited by 3 publications

References 12 publications

Automated sign language detection and classification using reptile search algorithm with hybrid deep learning

Automated sign language detection and classification using reptile search algorithm with hybrid deep learning

Robust sign language detection for hearing disabled persons by Improved Coyote Optimization Algorithm with deep learning

Enhanced Bald Eagle Search Optimizer with Transfer Learning-based Sign Language Recognition for Hearing-impaired Persons

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