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Published in:

Volume 7 Issue 6
June-2020
eISSN: 2349-5162

Unique Identifier

JETIR2006067

Page Number

459-463

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Title

A COMPARATIVE ANALYSIS ON SIGN LANGUAGE PREDICTION USING MACHINE LEARNING ALGORITHMS

ISSN

2349-5162

Cite This Article

"A COMPARATIVE ANALYSIS ON SIGN LANGUAGE PREDICTION USING MACHINE LEARNING ALGORITHMS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 6, page no.459-463, June-2020, Available :http://www.jetir.org/papers/JETIR2006067.pdf

Abstract

Sign language is a language which is mostly used between the deaf people to communicate among them. As there is a difference between them and us i.e. as we cannot understand what they say through sign language, to communicate with them we build a system called sign language recognition system. There are many classification algorithms present to detect the sign language but here we are going look forward only four algorithms which are KNN, random forests, K-means, SVM). This paper presents a comparative analysis of various machine learning algorithms for the American Sign Language dataset.

Key Words

KNN, ASL, random forests, SVM,k-means

Cite This Article

"A COMPARATIVE ANALYSIS ON SIGN LANGUAGE PREDICTION USING MACHINE LEARNING ALGORITHMS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 6, page no. pp459-463, June-2020, Available at : http://www.jetir.org/papers/JETIR2006067.pdf

Publication Details

Published Paper ID: JETIR2006067
Registration ID: 233913
Published In: Volume 7 | Issue 6 | Year June-2020
DOI (Digital Object Identifier):
Page No: 459-463
ISSN Number: 2349-5162

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Cite This Article

"A COMPARATIVE ANALYSIS ON SIGN LANGUAGE PREDICTION USING MACHINE LEARNING ALGORITHMS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 6, page no. pp459-463, June-2020, Available at : http://www.jetir.org/papers/JETIR2006067.pdf




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