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  • Open Access

    ARTICLE

    A Light-Weight Deep Learning-Based Architecture for Sign Language Classification

    M. Daniel Nareshkumar1,*, B. Jaison2

    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3501-3515, 2023, DOI:10.32604/iasc.2023.027848

    Abstract With advancements in computing powers and the overall quality of images captured on everyday cameras, a much wider range of possibilities has opened in various scenarios. This fact has several implications for deaf and dumb people as they have a chance to communicate with a greater number of people much easier. More than ever before, there is a plethora of info about sign language usage in the real world. Sign languages, and by extension the datasets available, are of two forms, isolated sign language and continuous sign language. The main difference between the two types is that in isolated sign… More >

  • Open Access

    ARTICLE

    ASL Recognition by the Layered Learning Model Using Clustered Groups

    Jungsoo Shin, Jaehee Jung*

    Computer Systems Science and Engineering, Vol.45, No.1, pp. 51-68, 2023, DOI:10.32604/csse.2023.030647

    Abstract American Sign Language (ASL) images can be used as a communication tool by determining numbers and letters using the shape of the fingers. Particularly, ASL can have an key role in communication for hearing-impaired persons and conveying information to other persons, because sign language is their only channel of expression. Representative ASL recognition methods primarily adopt images, sensors, and pose-based recognition techniques, and employ various gestures together with hand-shapes. This study briefly reviews these attempts at ASL recognition and provides an improved ASL classification model that attempts to develop a deep learning method with meta-layers. In the proposed model, the… More >

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