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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM35998

Sign Detection Model for the Deaf People using Deep Learning

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM35998

Volume:

8 |

6 |

Jun 2024

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM35998

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

AVP

Amruta V. Patil

DHD

Dr. Hiren Dand

DK

Dr.Sandeep Kadam

SP

Sanjaykumar Pingat

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Abstract

Sign detection is a crucial task in various computer vision applications, including autonomous driving, robotics, and augmented reality. This paper proposes a novel sign detection model based on deep learning techniques. The proposed model leverages convolutional neural networks (CNNs) to automatically learn discriminative features from sign images, enabling robust detection performance across diverse environmental conditions.

KEYWORDS

sign detection deep learning convolution neural networks computer vision autonomous driving robotics augmented reality.

Cite This Article

Amruta V. Patil, Dr. Hiren Dand, Dr.Sandeep Kadam, Sanjaykumar Pingat, "Sign Detection Model for the Deaf People using Deep Learning," International Journal of Scientific Research in Engineering and Management, vol. 8, no. 6, Jun 2024. https://doi.org/10.55041/IJSREM35998

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Publication Date

6/20/2024

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Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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