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Analogous sign language communication using gesture detection.

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Title: Analogous sign language communication using gesture detection.
Authors: Agrawal, Ayush Kumar1 (AUTHOR) ayush6295@gmail.com, Kumar, Jayendra1 (AUTHOR), Kumar, Avanish2 (AUTHOR), Arvind, Pratul3 (AUTHOR)
Source: Australian Journal of Electrical & Electronic Engineering. Dec2024, Vol. 21 Issue 4, p486-498. 13p.
Subjects: Language transfer (Language learning), American Sign Language, Convolutional neural networks, Sign language, Speech, Deep learning
Abstract: In this generation, deep learning techniques are widely used for sign language prediction. In this paper, a deep learning model is proposed for American sign language detection using webcam images and transfer learning. The particular model is designed for a real-time sign language detection. The author claims 98% of accuracy for this designed model, when trained with a total of 15 images for each gesture. Jupyter notebook is used as the environment for working out this research. Cuda, cudnn graphic processor upgraders are also utilised in this research for training the model. To make real-time detections easy, a local environment is used rather than a cloud system for implementing the code. The main aim of this research work is to create a model in order to identify and detect the sign language alphabets and some very frequently used gestures. The model is designed based on deep learning by using the convolutional neural networks and single shot detector algorithm to surpass the difficulty that is faced by the speech impaired and normal people. [ABSTRACT FROM AUTHOR]
Copyright of Australian Journal of Electrical & Electronic Engineering is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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PubType: Academic Journal
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  Data: Analogous sign language communication using gesture detection.
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  Data: <searchLink fieldCode="JN" term="%22Australian+Journal+of+Electrical+%26+Electronic+Engineering%22">Australian Journal of Electrical & Electronic Engineering</searchLink>. Dec2024, Vol. 21 Issue 4, p486-498. 13p.
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  Data: <searchLink fieldCode="DE" term="%22Language+transfer+%28Language+learning%29%22">Language transfer (Language learning)</searchLink><br /><searchLink fieldCode="DE" term="%22American+Sign+Language%22">American Sign Language</searchLink><br /><searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Sign+language%22">Sign language</searchLink><br /><searchLink fieldCode="DE" term="%22Speech%22">Speech</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink>
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  Data: In this generation, deep learning techniques are widely used for sign language prediction. In this paper, a deep learning model is proposed for American sign language detection using webcam images and transfer learning. The particular model is designed for a real-time sign language detection. The author claims 98% of accuracy for this designed model, when trained with a total of 15 images for each gesture. Jupyter notebook is used as the environment for working out this research. Cuda, cudnn graphic processor upgraders are also utilised in this research for training the model. To make real-time detections easy, a local environment is used rather than a cloud system for implementing the code. The main aim of this research work is to create a model in order to identify and detect the sign language alphabets and some very frequently used gestures. The model is designed based on deep learning by using the convolutional neural networks and single shot detector algorithm to surpass the difficulty that is faced by the speech impaired and normal people. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of Australian Journal of Electrical & Electronic Engineering is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1080/1448837X.2024.2337495
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      – Code: eng
        Text: English
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        PageCount: 13
        StartPage: 486
    Subjects:
      – SubjectFull: Language transfer (Language learning)
        Type: general
      – SubjectFull: American Sign Language
        Type: general
      – SubjectFull: Convolutional neural networks
        Type: general
      – SubjectFull: Sign language
        Type: general
      – SubjectFull: Speech
        Type: general
      – SubjectFull: Deep learning
        Type: general
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      – TitleFull: Analogous sign language communication using gesture detection.
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            NameFull: Agrawal, Ayush Kumar
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            NameFull: Kumar, Jayendra
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            NameFull: Kumar, Avanish
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            NameFull: Arvind, Pratul
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            – D: 01
              M: 12
              Text: Dec2024
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              Y: 2024
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