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A Comprehensive Review of CNN-Based Sign Language Translation System

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Proceedings of Data Analytics and Management

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 572))

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Abstract

One of the most crucial tools for connecting with others is communication. Effective communication skills can smooth our path and improve our interactions with people in our daily lives by allowing us to understand and be understood by others. Many deaf and mute people rely on sign languages as their primary mode of communication. Recent research in sign language translation systems (SLTS) has yielded impressive results. The aim of the paper is to study the existing translation mechanism of sign language. The review starts with the classification of sign language systems and contemplates country-wise sign languages, different data sets used for the development of the sign language translation system, the architecture of convolution neural network (CNN)-based models, and their performances. It is intended that this study will serve as a road map for future research and knowledge development in the field of sign language recognition as well as translation system in the field of CNN.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Baba Mastnath University, Rohtak, Haryana, India

    Seema & Priti Singla

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  1. Seema
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  2. Priti Singla
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Correspondence to Seema .

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Editors and Affiliations

  1. Maharaja Agrasen Institute of Technology, Rohini, Delhi, India

    Ashish Khanna

  2. The Karkonosze University of Applied Sciences in Jelenia Góra, Jelenia Góra, Poland

    Zdzislaw Polkowski

  3. Tijuana Institute of Technology, Tijuana, Baja California, Mexico

    Oscar Castillo

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Seema, Singla, P. (2023). A Comprehensive Review of CNN-Based Sign Language Translation System. In: Khanna, A., Polkowski, Z., Castillo, O. (eds) Proceedings of Data Analytics and Management . Lecture Notes in Networks and Systems, vol 572. Springer, Singapore. https://doi.org/10.1007/978-981-19-7615-5_31

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