Deep Learning Technology to Recognize American Sign Language Alphabet

Alsharif, Bader; Altaher, Ali Salem; Altaher, Ahmed; Ilyas, Mohammad; Alalwany, Easa

doi:10.3390/s23187970

Cited by 8 publications

(3 citation statements)

References 46 publications

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“…Their proposed TSM-ResNet50 model achieved 97.57% accuracy on the ASL alphabet dataset. Alsharif et al [33] obtained accuracies of 99.50%, 99.51%, 99.95%, 99.98%, and 88.59% for recognizing the ASL alphabet using AlexNet, ConvNeXt, EfficientNet, ResNet-50, and Vision Transformer models, respectively.…”

Section: Related Workmentioning

confidence: 99%

Multi-Stream Isolated Sign Language Recognition Based on Finger Features Derived from Pose Data

Akdag,

Baykan

2024

Electronics

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This study introduces an innovative multichannel approach that focuses on the features and configurations of fingers in isolated sign language recognition. The foundation of this approach is based on three different types of data, derived from finger pose data obtained using MediaPipe and processed in separate channels. Using these multichannel data, we trained the proposed MultiChannel-MobileNetV2 model to provide a detailed analysis of finger movements. In our study, we first subject the features extracted from all trained models to dimensionality reduction using Principal Component Analysis. Subsequently, we combine these processed features for classification using a Support Vector Machine. Furthermore, our proposed method includes processing body and facial information using MobileNetV2. Our final proposed sign language recognition method has achieved remarkable accuracy rates of 97.15%, 95.13%, 99.78%, and 95.37% on the BosphorusSign22k-general, BosphorusSign22k, LSA64, and GSL datasets, respectively. These results underscore the generalizability and adaptability of the proposed method, proving its competitive edge over existing studies in the literature.

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Section: Related Workmentioning

confidence: 99%

Multi-Stream Isolated Sign Language Recognition Based on Finger Features Derived from Pose Data

Akdag,

Baykan

2024

Electronics

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“…In addition to this process being easily understandable, implementing it is highly effective and easy. ML has a wide range of applications, including fraud detection in finance [ 42 ], personalized learning in education [ 43 ], disease diagnosis in healthcare [ 44 ], and climate modeling in environmental research [ 45 ]. ML helps solve challenges related to IoV, especially traffic flow prediction and optimization.…”

Section: Introductionmentioning

confidence: 99%

Security and Trust Management in the Internet of Vehicles (IoV): Challenges and Machine Learning Solutions

Alalwany,

Mahgoub

2024

Sensors

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The Internet of Vehicles (IoV) is a technology that is connected to the public internet and is a subnetwork of the Internet of Things (IoT) in which vehicles with sensors are connected to a mobile and wireless network. Numerous vehicles, users, things, and networks allow nodes to communicate information with their surroundings via various communication channels. IoV aims to enhance the comfort of driving, improve energy management, secure data transmission, and prevent road accidents. Despite IoV’s advantages, it comes with its own set of challenges, particularly in the highly important aspects of security and trust. Trust management is one of the potential security mechanisms aimed at increasing reliability in IoV environments. Protecting IoV environments from diverse attacks poses significant challenges, prompting researchers to explore various technologies for security solutions and trust evaluation methods. Traditional approaches have been employed, but innovative solutions are imperative. Amid these challenges, machine learning (ML) has emerged as a potent solution, leveraging its remarkable advancements to effectively address IoV’s security and trust concerns. ML can potentially be utilized as a powerful technology to address security and trust issues in IoV environments. In this survey, we delve into an overview of IoV and trust management, discussing security requirements, challenges, and attacks. Additionally, we introduce a classification scheme for ML techniques and survey ML-based security and trust management schemes. This research provides an overview for understanding IoV and the potential of ML in improving its security framework. Additionally, it provides insights into the future of trust and security enhancement.

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“…In recent years, with the revolution of technology, there has been increased attention to close the communication gap between the deaf and hard-of-hearing community and the rest of the people [7,8]. Despite the significant practical potential of sign language recognition, the problem of effectively recognizing sign language remains an open area of research due to its big challenges, which include differences in the semantic-syntactic structure of written and sign languages, internal factors (e.g., individual performance characteristics, context dependency), and external factors (e.g., lighting, background, shooting angle, etc.).…”

Section: Introductionmentioning

confidence: 99%

Efhamni: A Deep Learning-Based Saudi Sign Language Recognition Application

Al Khuzayem,

Shafi,

Aljahdali

et al. 2024

Sensors

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Deaf and hard-of-hearing people mainly communicate using sign language, which is a set of signs made using hand gestures combined with facial expressions to make meaningful and complete sentences. The problem that faces deaf and hard-of-hearing people is the lack of automatic tools that translate sign languages into written or spoken text, which has led to a communication gap between them and their communities. Most state-of-the-art vision-based sign language recognition approaches focus on translating non-Arabic sign languages, with few targeting the Arabic Sign Language (ArSL) and even fewer targeting the Saudi Sign Language (SSL). This paper proposes a mobile application that helps deaf and hard-of-hearing people in Saudi Arabia to communicate efficiently with their communities. The prototype is an Android-based mobile application that applies deep learning techniques to translate isolated SSL to text and audio and includes unique features that are not available in other related applications targeting ArSL. The proposed approach, when evaluated on a comprehensive dataset, has demonstrated its effectiveness by outperforming several state-of-the-art approaches and producing results that are comparable to these approaches. Moreover, testing the prototype on several deaf and hard-of-hearing users, in addition to hearing users, proved its usefulness. In the future, we aim to improve the accuracy of the model and enrich the application with more features.

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Deep Learning Technology to Recognize American Sign Language Alphabet

Cited by 8 publications

References 46 publications

Multi-Stream Isolated Sign Language Recognition Based on Finger Features Derived from Pose Data

Multi-Stream Isolated Sign Language Recognition Based on Finger Features Derived from Pose Data

Security and Trust Management in the Internet of Vehicles (IoV): Challenges and Machine Learning Solutions

Efhamni: A Deep Learning-Based Saudi Sign Language Recognition Application

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