Identifying Indian Sign Languages Using Decentralized Deep Learning

Authors

  • Kumar Ayush Research Scholar, Department of Electronics and communication Engineering, SRM Institute of Science and Technology, Chennai, India
  • Swagata Mandal Research Scholar, Department of Electronics and communication Engineering, SRM Institute of Science and Technology, Chennai, India

Keywords:

CNN, Database, Deep Learning, GPU, Machine Learning, ISL, RGB, TensorFlow, TPU

Abstract

Among the main challenges that persons with disabilities encounter on a regular schedule is interaction. The advancement of recognizing signing communicative patterns and techniques made possible by modern technological advancements has significantly contributed to the resolution of this issue. Systems for the identification of hand signals utilizing human intelligence have shown excellent reliability. However, the modeling learning algorithm might take a lot of time because of the vast amount of information needed. We suggest an expedited Indian Signature Identification System that makes use of dispersed intelligence to address this issue. This same concept uses Convolutional Neural Network (CNN) for identifying indications and switching them to English speech. The modeling proposed technique has been distributed over Temporal Unit Operations and Graphics Processors Elements.

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Published

05-12-2022

Issue

Section

Articles

How to Cite

[1]
K. Ayush and S. Mandal, “Identifying Indian Sign Languages Using Decentralized Deep Learning”, IJRESM, vol. 5, no. 11, pp. 241–246, Dec. 2022, Accessed: Jun. 17, 2024. [Online]. Available: https://journal.ijresm.com/index.php/ijresm/article/view/2458