Development of an End-to-End Deep Learning Framework for Sign Language Recognition, Translation, and Video Generation-Reference-Cited by-同舟云学术

Development of an End-to-End Deep Learning Framework for Sign Language Recognition, Translation, and Video Generation

Published:2022 Issue: Volume:10 Page:104358-104374
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Natarajan B.¹,Rajalakshmi E.¹,Elakkiya R.¹^ORCID,Kotecha Ketan²^ORCID,Abraham Ajith³^ORCID,Gabralla Lubna Abdelkareim⁴^ORCID,Subramaniyaswamy V.¹^ORCID

Affiliation:

1. School of Computing, SASTRA Deemed University, Thanjavur, India

2. Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International (Deemed University), Pune, India

3. Machine Intelligence Research Laboratories (MIR Labs), Auburn, WA, USA

4. Department of Computer Science and Information Technology, College of Applied, Princess Nourah Bint Abdul Rahman University, Riyadh, Saudi Arabia

Funder

Princess Nourah bint Abdulrahman University Researchers Supporting

Science and Engineering Research Board (SERB), India

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09905589.pdf?arnumber=9905589

Reference86 articles.

1. How2Sign: A Large-scale Multimodal Dataset for Continuous American Sign Language

2. Neural Sign Language Translation

3. Neural machine translation by jointly learning to align and translate;bahdanau;arXiv 1409 0473,2014

4. On the Properties of Neural Machine Translation: Encoder–Decoder Approaches

5. An Instrumented Glove for Recognition of Brazilian Sign Language Alphabet

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