Hand Gesture Alphabet Recognition for American Sign Language using Deep Learning

Authors(2) :-Krutika S. Kale, Milind B. Waghmare

Speech impairment limits a person's capacity to speak and communicate with others, forcing them to adopt other communication methods such as sign language. Sign language is not that widely used technique by the deaf. To solve this problem, we developed a powerful hand gesture detection tool that can easily monitor both dynamic and static hand motions with ease. Gesture recognition aims to translate sign language into voice or text for individuals who have a rudimentary comprehension of that, which will be a tremendous help in communication between deaf-mute and hearing people. We describe the design and implementation of an American Sign Language (ASL) fingerspelling translator based on spatial feature identification using a convolutional neural network.

Authors and Affiliations

Krutika S. Kale
Department of Computer Science and Engineering, Government College of Engineering Amravati, India
Milind B. Waghmare
Department of Computer Science and Engineering, Government College of Engineering Amravati, India

Sign Language Recognition, Deep learning, image processing, American sign Language, Hand gesture detection

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Publication Details

Published in : Volume 8 | Issue 5 | September-October 2021
Date of Publication : 2021-10-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 213-220
Manuscript Number : IJSRSET218521
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

Krutika S. Kale, Milind B. Waghmare, " Hand Gesture Alphabet Recognition for American Sign Language using Deep Learning, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 8, Issue 5, pp.213-220, September-October-2021. Available at doi : https://doi.org/10.32628/IJSRSET218521      Citation Detection and Elimination     |     
Journal URL : https://ijsrset.com/IJSRSET218521

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