Sign Talk for Blind and Deaf Translating Hand Gestures into Audible and Textual Communication

Year : 2025 | Volume : 12 | Issue : 02 | Page : 20 26
    By

    S. Saraswat,

  • Aditya Gulhane,

  • Sujal Sahu,

  • Jyotiraditya More,

  • Shraddha Wakale,

  1. Professor, Department of Information Technology, Pune District Education Association’s College of Engineering, Manjari (Bk), Pune, Maharashtra, India
  2. Student, Department of Information Technology, Pune District Education Association’s College of Engineering, Manjari (Bk), Pune, Maharashtra, India
  3. Student, Department of Information Technology, Pune District Education Association’s College of Engineering, Manjari (Bk), Pune, Maharashtra, India
  4. Student, Department of Information Technology, Pune District Education Association’s College of Engineering, Manjari (Bk), Pune, Maharashtra, India
  5. Student, Department of Information Technology, Pune District Education Association’s College of Engineering, Manjari (Bk), Pune, Maharashtra, India

Abstract

Sign language serves as a vital communication method for individuals who are deaf or have hearing impairments. It relies on hand gestures, facial cues, and body language to convey messages and express meaning. However, many people who do not know sign language find it hard to communicate with sign language users. The latest progress in artificial intelligence and computing has enabled the creation of systems that can automatically recognize sign language. These systems can help recognize hand gestures in real-time, making it easier for individuals with hearing difficulties to communicate. Traditionally, recognizing gestures required special equipment like sensors or cameras, which could be expensive and not widely available. Now, using deep learning and computer vision techniques, we can recognize gestures directly from regular webcam feeds, which is more affordable and accessible. In our project, we created a real-time hand gesture recognition system that uses a webcam to capture hand signs and processes them with a deep learning model. It then converts these gestures into text and voice outputs. This system aims to help people with hearing and speech challenges by providing a simple communication tool. Our study explains how the system works, how we built it, and how well it performs. The findings indicate that deep learning is capable of accurately identifying hand gestures, thereby enhancing communication for individuals who are deaf or mute.

Keywords: Hand gesture recognition, sign language translation, deep learning, computer vision, CNN

[This article belongs to Journal of Image Processing & Pattern Recognition Progress ]

How to cite this article:
S. Saraswat, Aditya Gulhane, Sujal Sahu, Jyotiraditya More, Shraddha Wakale. Sign Talk for Blind and Deaf Translating Hand Gestures into Audible and Textual Communication. Journal of Image Processing & Pattern Recognition Progress. 2025; 12(02):20-26.
How to cite this URL:
S. Saraswat, Aditya Gulhane, Sujal Sahu, Jyotiraditya More, Shraddha Wakale. Sign Talk for Blind and Deaf Translating Hand Gestures into Audible and Textual Communication. Journal of Image Processing & Pattern Recognition Progress. 2025; 12(02):20-26. Available from: https://journals.stmjournals.com/joipprp/article=2025/view=208936


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Regular Issue Subscription Review Article
Volume 12
Issue 02
Received 25/03/2025
Accepted 16/04/2025
Published 28/04/2025
Publication Time 34 Days
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