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A Comprehensive Analysis on Technological Approaches in Sign Language Recognition

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Emergent Converging Technologies and Biomedical Systems

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 841))

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Abstract

Communication is one of the imperative sides of the twenty-first century. We as human being cannot even imagine this world without communicating with each other. For honest communication to happen two persons should agree on a common language that each of them understands. Keeping this context in mind, there will be a necessity for a translator in between a hearing/speech-impaired person and a normal person. But the translators or consultants who understand sign language do not seem to be simply and commonly offered all the days, if offered conjointly, they charge tons. To help the social interaction of hearing-impaired–speech debilitated individuals with the society economical interactive communication tools are made. As the importance of sign language interpretation is increasing day by day so several triple-crown applications for linguistic communication recognition comprise new forms of real-time interpretation with progressed artificial intelligence and image processing approaches. In this paper, we gave an entire outline of various methodologies and processes rooted in deep learning and discussed technological approaches for linguistic communication interpretation, also achieving 95.7% accuracy with two layers of CNN model classifier for the sign gestures of 26 English alphabets with readily available resources. This paper also provides current analysis and advances in this area and tends to distinguish openings and difficulties for future exploration.

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Authors and Affiliations

  1. Lovely Professional University, Phagwara, Punjab, India

    Kamaldeep Sharma, Brajesh Kumar, Deepti Sehgal & Ayan Kaushik

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  1. Kamaldeep Sharma
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  2. Brajesh Kumar
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Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, University Institute of Engineering and Technology, Kurukshetra, Haryana, India

    N. Marriwala

  2. Dean Engineering and Technology, Kurukshetra University, Kurukshetra, Haryana, India

    C. C. Tripathi

  3. Department of Electronic and Communication Engineering, Jaypee University of Information Technology, Solan, Himachal Pradesh, India

    Shruti Jain

  4. Co-Founder of Dew Mobility, Santa Clara University, Santa Clara, CA, USA

    Shivakumar Mathapathi

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Sharma, K., Kumar, B., Sehgal, D., Kaushik, A. (2022). A Comprehensive Analysis on Technological Approaches in Sign Language Recognition. In: Marriwala, N., Tripathi, C.C., Jain, S., Mathapathi, S. (eds) Emergent Converging Technologies and Biomedical Systems . Lecture Notes in Electrical Engineering, vol 841. Springer, Singapore. https://doi.org/10.1007/978-981-16-8774-7_29

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  • DOI: https://doi.org/10.1007/978-981-16-8774-7_29

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-8773-0

  • Online ISBN: 978-981-16-8774-7

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