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INDIAN SIGN LANGUAGE RECOGNITION USING YOLOV5

Authors

  • FATHIMA P.S

    Amal Jyothi College of Engineering
    Author
  • ANU ROSE JOY

    Amal Jyothi College of Engineering
    Author
  • ANSPIN TITUS

    Amal Jyothi College of Engineering
    Author
  • ANSU MARIUM SHIBU

    Amal Jyothi College of Engineering
    Author
  • ASNA AZEEZ

    Amal Jyothi College of Engineering
    Author

Abstract

In our rapidly advancing technological era,
characterized by the ubiquity of home automation and a demand
for streamlined solutions, a project unfolds with the mission to
address communication challenges for individuals with hearing
and speech impairments. Sign language, a vital mode of expression
for the deaf and mute, forms the focal point of this initiative.
Utilizing sophisticated Deep Learning algorithms including
YOLOv5 the project aims to analyze and interpret sign language
gestures from input images. The ultimate goal is to seamlessly
translate these gestures into text and, subsequently, into audio,
thereby providing an encompassing communication solution. A
diverse dataset, encompassing English letters, numbers, and
words, enhances the system’s proficiency. This endeavor not only
embraces technological progress but, more importantly,
champions inclusivity by breaking down communication barriers
for those who have long faced challenges in expressing themselves
effectively.

Keywords:

YOLOv5, ISL, Words Recognition, Static Gesture Recognition
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Published

06-08-2025

Issue

Section

Articles

How to Cite

[1]
F. P.S, A. ROSE JOY, A. TITUS, A. MARIUM SHIBU, and A. AZEEZ, “INDIAN SIGN LANGUAGE RECOGNITION USING YOLOV5”, IJERA, vol. 4, no. 1, p. 7, Aug. 2025, Accessed: Aug. 12, 2025. [Online]. Available: https://ijera.in/index.php/IJERA/article/view/201

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