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Published Paper Details:
Dynamic Hand Gesture Recognition Using Multi- Branch Attention Based Graph and General Deep Learning Model
Authors
Jessy Jose,Adarsha D,Dr. Kusumadhara. S
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Keywords
AI, Machine Learning, Convolutional Neural Networks, Python, Open cv, Tesseract, espeak.
Abstract
Communication is one of the most powerful and essential tools of human existence. It is through communication that individuals express their ideas, convey emotions, form social bonds, and participate meaningfully in society. However, for millions of people around the world living with visual, hearing, or speech impairments, the ability to communicate and navigate their environment independently remains a persistent challenge. These barriers can lead to social isolation, decreased mobility, and reduced quality of life.In response to these challenges, this project presents an integrated assistive technology solution designed to empower individuals with such impairments. The system combines two core functionalities: a voice-assisted navigation system for visually impaired users, and a sign language recognition module for individuals with speech or hearing disabilities.The navigation module uses a combination of sensors (such as ultrasonic or LiDAR) and artificial intelligence to detect obstacles in the user's path. It delivers real-time audio feedback, guiding users safely through their surroundings and reducing the risks associated with independent mobility. This system is especially helpful in urban environments where navigating streets, avoiding obstacles, or crossing roads can be daunting without visual assistance.In parallel, the sign language recognition module utilizes a camera to capture hand gestures and interprets them using computer vision techniques and machine learning algorithms. These gestures are then translated into audible speech or on-screen text, enabling users with speech impairments to communicate more effectively with others, even in environments where interpreters or other aids are unavailable.What sets this project apart is its focus on simplicity, accessibility, and affordability. Designed with the end-user in mind, the system uses cost-effective components and prioritizes portability and ease of use. This ensures that the benefits of advanced assistive technology can reach underserved populations, including those in low-resource settings.Ultimately, this project is not just about technological innovation--it is about human dignity. It aims to restore a sense of independence, confidence, and connection for individuals who are often marginalized by communication and mobility barriers. By leveraging modern technology with compassion and purpose, the system serves as a step toward a more inclusive and equitable society.
IJCRT's Publication Details
Unique Identification Number - IJCRT2506924
Paper ID - 289934
Author type - Indian Author
Page Number(s) - h862-h868
Pubished in - Volume 13 | Issue 6 | June 2025
DOI (Digital Object Identifier) -
No Of Downloads - 90
Author Country - India, 570017, Mysuru, Mysuru, 570017, Science and Technology
Publisher Name - IJPUBLICATION | www.ijcrt.org | ISSN : 2320-2882
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Jessy Jose,Adarsha D,Dr. Kusumadhara. S, "Dynamic Hand Gesture Recognition Using Multi- Branch Attention Based Graph and General Deep Learning Model", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.13, Issue 6, pp.h862-h868, June 2025, Available at :http://www.ijcrt.org/papers/IJCRT2506924.pdf
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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