American Sign Language Translation into Text and Speech

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Pranjali Kolawale; Pranav Deore; Dr. Disha Gabhane

American Sign Language Translation into Text and Speech

Authors: Pranjali Kolawale, Pranav Deore, Dr. Disha Gabhane

Unique Paper ID: 179725
Volume: 11
Issue: 12
PageNo: 7893-7896

Keywords: ASL CNN Landmark

Abstract:
Sign language is one of the communication ways for the people who have hearing issues. Common people find it a challenge to communicate with deaf people, that’s where this system is useful. This project presents the development of real-time American sign language translation into text and voice. This System captures the hand gestures using a webcam and processes using the Mediapipe and OpenCv Python libraries. These 3D landmark coordinates are then structured into a consistent format and passed to a trained 2D Convolutional Neural Network (CNN) for classification. The model accurately predicts ASL Gestures. The predicted output is displayed as text, with optional conversion to voice, enabling more accessible and seamless communication across hearing boundaries.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 12
PageNo: 7893-7896

American Sign Language Translation into Text and Speech

Available:https://ijirt.org/article?manuscript=179725

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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