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Author(s): Karan Kumar Giri1, Abhishek Singh2, Mohammad Intiyaj Alam3, Basanta Mahato4

Email(s): 1girikaran366.iimt@gmail.com

Address:

    Department of Electronics and Communication. IIMT College of Engineering, Greater Noida, India.

Published In:   Volume - 3,      Issue - 1,     Year - 2023

DOI: 10.55878/SES2023-3-1-8  

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ABSTRACT:
The study of using computers to understand and represent sign language has made significant strides in recent years. With robotic 'Ralph'-like finger-spelling hands, 'Cyber Gloves' (virtual reality sensors that collect both isolated and continuous signs), camera-based projects like the engaging Copycat American Sign Language game, and more, this essay covers significant efforts in the field. "sign recognition software," and "Hidden Markov Modelling and neural network systems." There are spoken language to sign language translation technologies like "Tessa" (Text and Sign Support Assistant; three-dimensional imaging) and "THETOS" (Text into Sign Language Automatic Translator, which operates in Polish; natural language processing), a Polish project. It is also investigated how this research might be used in education. The "ICICLE" (Interactive Computer Identification and Correction of Language Errors) project, for instance, builds a tutorial system for deaf or hard-of-hearing youngsters that analyses their English writing and provides specialized lessons and recommendations using intelligent computer-aided instruction. The article also takes into account synthesized signs, which are being included in instructional materials and may one day be created by students themselves.

Cite this article:
Karan Kumar Giri, Abhishek Singh, Mohammad Intiyaj Alam, Basanta Mahato The Importance and Challenges of Sign Language Translator- A Review Spectrum of Emerging Sciences, 3 (1), 2023, 36-38. 10.55878/SES2023-3-1-8DOI: https://doi.org/10.55878/SES2023-3-1-8


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[3]. Zhou, Zhihao, Kyle Chen, Xiaoshi Li, Songlin Zhang, Yufen Wu, Yihao Zhou, Keyu Meng et al. "Sign-to-speech translation using machine-learning-assisted stretchable sensor arrays." Nature Electronics 2020, 3(9): 571-578.

[4]. Wong, W. K., Filbert H. Juwono, Brendan Teng Thiam Khoo. "Multi-features capacitive hand gesture recognition sensor: A machine learning approach." IEEE Sensors Journal 21(6) (2021): 8441-8450.

[5]. Kudrinko, Karly, Emile Flavin, Xiaodan Zhu, and Qingguo Li. "Wearable sensor-based sign language recognition: A comprehensive review." IEEE Reviews in Biomedical Engineering 14 (2020): 82-97.

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Author(s): Karan Kumar Giri, Abhishek Singh, Mohammad Intiyaj Alam, Basanta Mahato

DOI: 10.55878/SES2023-3-1-8         Access: Open Access Read More