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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM44150

Real-Time Bidirectional Translation System Between Text and Indian Sign Language Using Deep Learning and NLP Techniques

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM44150

Volume:

9 |

4 |

Apr 2025

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM44150

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

NVG

Niyati V. Gaonkar

VRG

Vishal R. Gori

AGK

Anket G. Kadam

SHN

Soham H. Nimbalkar

MS

Manoj Sabnis

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Abstract

In this paper, we present a real-time translation system that bridges the communication gap between the hearing and non-hearing communities. Our system converts English text to Indian Sign Language (ISL) and vice versa, using Natural Language Processing (NLP) techniques and deep learning-based gesture recognition. The system supports video-based gesture recognition for ISL and provides accurate text translations in real-time. This study addresses the technical challenges involved, including feature extraction from gestures and translating com- plex ISL sentences using neural networks like LSTM.

KEYWORDS

Indian Sign Language (ISL) Sign Language Translation Gesture Recognition Deep Learning LSTM Model Mediapipe Holistic Text-to-Sign Conversion Dy- namic Gesture Segmentation Fingerspelling Natural Lan- guage Processing (NLP) Pose Estimation Hand Landmark Tracking Real-Time Sign Language Recognition Data Aug- mentation Accessibility Technology

Cite This Article

Niyati V. Gaonkar, Vishal R. Gori, Anket G. Kadam, Soham H. Nimbalkar, Manoj Sabnis, "Real-Time Bidirectional Translation System Between Text and Indian Sign Language Using Deep Learning and NLP Techniques," International Journal of Scientific Research in Engineering and Management, vol. 9, no. 4, Apr 2025. https://doi.org/10.55041/IJSREM44150

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Publication Date

11/3/2025

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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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