Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
Sign language is one of the oldest and most natural forms of communication, yet most people do not understand it, making it difficult for deaf and mute individuals to communicate. This research proposes a vision-based machine learning model for real-time sign language translation using convolutional and recurrent neural networks. The system recognizes hand gestures representing the American Sign Language (ASL) alphabet with an achieved accuracy of 98%. The model employs image preprocessing, CNN-based feature extraction, and RNN-based temporal analysis to recognize dynamic gestures effectively. This paper highlights the implementation, results, and future improvements for improving accessibility and inclusivity.
"Sign Language Translator using Machine Learning", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 11, page no.a51-a54, November-2025, Available :http://www.ijrti.org/papers/IJRTI2511007.pdf
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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