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Communication barriers between hearing-impaired individuals and the wider community often create significant challenges in daily interactions. This project presents an intelligent software solution that bridges this gap by translating sign language gestures into readable human language in real-time. Using computer vision and machine learning techniques, the system captures hand signs through a webcam, processes the visual data, and accurately maps each gesture to its corresponding word or phrase. The aim is to enhance accessibility and inclusion by providing a user-friendly platform that allows seamless communication without the need for a human interpreter. This software has the potential to be integrated into various applications, such as education, healthcare, and customer service, making environments more inclusive for the hearing- impaired community.
Keywords:
Sign Language Recognition , Real-Time Gesture Translation, Hearing Impaired Communication , Machine Learning for Sign Language
Cite Article:
"Sign Language Recognition", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 5, page no.a46-a50, May-2025, Available :http://www.ijrti.org/papers/IJRTI2505006.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator