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Nowadays, research has become increasingly important in the field of computer vision. The power of technology is leveraged to simplify and ease human existence through developments in machine intelligence and man-machine interfaces. In daily life, communication is crucial, but for physically challenged people, such as the deaf or those with vocal disorders, it can be quite challenging. There is a very small community of sign language users throughout the world. The primary drawback of the sign language community is that individuals who understand it can only communicate with others who do as well. Hence, just a select few people around the world can now communicate with them easily. So, the goal of this research is to develop a system that will enable them to communicate with others by translating sign language into text and audio output. For identifying the numerous movements and indications acquired by the camera, the system makes use of OpenCV. To do this, the sign alphabet is first classified using Convolution Neural Network (CNN), and the hand sign is then detected using OpenCV and fed into the model to provide the final forecast. The signals and motions are then recognized, and the outputs are turned into text and audio.
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Cite Article:
"Real-time Sign Language Detection using Convolutional Neural Network", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.8, Issue 5, page no.292 - 294, May-2023, Available :http://www.ijrti.org/papers/IJRTI2305046.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