Double handed dynamic Turkish Sign Language recognition using Leap Motion with meta learning approach

Katilmis, Zekeriya; Karakuzu, Cihan

doi:10.1016/j.eswa.2023.120453

Cited by 7 publications

(3 citation statements)

References 62 publications

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“…The Meta-ELM model was implemented by Katilmi and Karakuzu [12] for the recognition of double-handed dynamic TSL. The Time-Frequency Domain Feature Extraction (TFDFE) method was applied for data regularization and 26 dynamic words were selected from the datasets.…”

Section: Machine Learning-based Sign Language Recognitionmentioning

confidence: 99%

“…In this subsection, the proposed method's effectiveness is compared against various state-of-theart approaches, such as Meta-ELM [12], SVM-CNN [17], LSTMRNN-KNN [18], T-CNN [19], TDDRMN [25], and PSO-CNN [27]. The results of the comparative study are detailed below.…”

Section: Comparative Analysismentioning

confidence: 99%

“…According to the reports provided by the World Federation of the Deaf (WFD), it is evident that 300 SLs are used by deaf and dumb people globally. For instance, Indian SL (ISL), Turkish SL (TSL), Arabic SL (ArSL), American SL (ASL), German SL (GSL), and so on [5]. In deaf and dumb communities, every SL word is performed utilizing single as well as double hands.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Doubled-handed sign language Recognition for deaf and dumb people using Vision Transformers

Vaidhya,

Anand

2024

Preprint

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Sign language is an effective communication tool to convey information to each other, that is a bridge to reduce the communication gap between deaf and dumb people. The word level sign language recognition is a challenging task due to the wide range of body gestures, unidentified signals and hand configuration. To overcome this issue, a novel Inverted Residual Network Convolutional Vision Transformer based Mutation Boosted Tuna Swarm Optimization (IRNCViT-MBTSO) algorithm is proposed for recognizing double-handed sign language. The proposed dataset is designed to identify different dynamic words and the predicted images are preprocessed to enhance the generalization ability of the model and improve image quality. The local features are extracted after performing feature graining and the global features are captured from the preprocessed images by implementing the ViT transformer model. These extracted features are concatenated to generate a feature map and are classified into different dynamic words using the Inverted Residual Feed Forward Network (IRFFN). The TSO algorithm tunes the parameters of the IRNCViT model that is responsible for handling high-dimensional problems and convergence issues. The Mutation operator is introduced in this optimization phase to escape local optimum issues while updating the position of tuna. The performance valuation of this proposed model is performed in terms of recognition accuracy, convergence and visual output of the dataset that showed the highest performance than other state-of-the-art methods.

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Section: Machine Learning-based Sign Language Recognitionmentioning

confidence: 99%

Section: Comparative Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dynamic Doubled-handed sign language Recognition for deaf and dumb people using Vision Transformers

Vaidhya,

Anand

2024

Preprint

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Silent no more: a comprehensive review of artificial intelligence, deep learning, and machine learning in facilitating deaf and mute communication

ZainEldin,

Gamel,

Talaat

et al. 2024

Artif Intell Rev

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People who often communicate via sign language are essential to our society and significantly contribute. They struggle with communication mostly because other people, who often do not understand sign language, cannot interact with them. It is necessary to develop a dependable system for automatic sign language recognition. This paper aims to provide a comprehensive review of the advancements in artificial intelligence (AI), deep learning (DL), and machine learning (ML) technologies that have been used to facilitate communication for individuals who are deaf and mute (D–M). This study explores various applications of these technologies, including sign language interpretation, speech recognition, and text-to-speech synthesis. By examining the current state of research and development in AI, ML, and DL for the D–M field, the survey sheds light on the potential and challenges faced in utilizing AI, deep learning, and ML to bridge the communication gap for the D–M community. The findings of this survey will contribute to a greater understanding of the potential impact of these technologies in improving access to communication for individuals who are D–M, thereby aiding in the development of more inclusive and accessible solutions.

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Efficient deep learning models based on tension techniques for sign language recognition

Attia,

Ahmed,

Alshewimy

2023

Intelligent Systems with Applications

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Double handed dynamic Turkish Sign Language recognition using Leap Motion with meta learning approach

Cited by 7 publications

References 62 publications

Dynamic Doubled-handed sign language Recognition for deaf and dumb people using Vision Transformers

Dynamic Doubled-handed sign language Recognition for deaf and dumb people using Vision Transformers

Silent no more: a comprehensive review of artificial intelligence, deep learning, and machine learning in facilitating deaf and mute communication

Efficient deep learning models based on tension techniques for sign language recognition

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