Comparitive Study on Sign Language Recognition System

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ISSN: 2349-6002 ESTD Year: 2014

UGC approved journal no 47859

Comparitive Study on Sign Language Recognition System
Author(s):
Drishti V Raje, Isha A Marlecha, Mithali Devadiga, Sanjana M, Kiran Y C, Poornima R M
Keywords:
Sign Language, Convolutional Neural Network(CNN), Recurrent Neural Network(RNN), Long Short Term Memory(LSTM), Deep Learning, Hand Gestures.
Abstract
WHO shows that about 5% of the world’s total population suffers from hearing and speaking disabilities. These disabled individuals utilize Sign Language as their means of divulgence which is quite difficult for most people to understand and interpret. In order to bridge this communication gap, sign language interpreters may be helpful but they may be unavailable at times or expensive to afford. To make this process easier researchers have worked on sign language recognition systems. The aim of this paper is to draw a comparative study between some of the frequently used algorithms.
Article Details
Unique Paper ID: 158722 Publication Volume & Issue: Volume 9, Issue 10 Page(s): 447 - 454
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