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The project object at building a machine learning model that will be able to classify the various hand gesture used for finger spelling in sign language. In this user independent model, classification machine learning algorithms are trained using set of image data and testing is done on a completely different set of data. For the image dataset, depth images are used, which gave you better reasult. Various machine learning algorithms are applied on the dataset, including convolution neural network(CNN). An attempt is made to increase the accuracy of the CNN model by pre-training it on the image dataset. In this paper, we discuss sign language recognition.
Keywords:
Sign language recognition, computer vision, machine learning, hand tracking, hand gesture recognition, gesture analysis, face recognition.
Cite Article:
"Sign Language Recognition", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.7, Issue 6, page no.375 - 377, June-2022, Available :http://www.ijrti.org/papers/IJRTI2206065.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