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REAL-TIME AMERICAN SIGN LANGUAGE RECOGNITION WITH NEURAL NETWORKS
ISSN: 2582 - 9130
Publisher: author
REAL-TIME AMERICAN SIGN LANGUAGE RECOGNITION WITH NEURAL NETWORKS
Indexed in
Computer Science and Technology Section
ARTICLE-FACTOR
1.3
Article Basics Score: 3
Article Transparency Score: 2
Article Operation Score: 2
Article Articles Score: 2
Article Accessibility Score: 3
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International Category Code (ICC):
ICC-0202
Publisher: Krishma Publication
International Journal Address (IAA):
IAA.ZONE/2582382689130
eISSN
:
2582 - 9130
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Abstract
Actual-time signal language translator is a crucial milestone in facilitating communication among the deaf community and the general public. Introducing the development and use of yanked sign Language Spelling Translator (ASL) based on the convolutional neural network. We use the pre-skilled Google Net architecture educated inside the ILSVRC2012 database, in addition to the ASL database for Surrey University and Massey university ASL to apply gaining knowledge of switch in this task. We have developed a sturdy version that constantly separates the letters a-e from the original users and any other that separates the spaced characters in maximum cases. Given the limitations of the information sets and the encouraging consequences acquired, we are assured that with similarly studies and further facts, we can produce a totally customized translator for all ASL characters. Keywords: Sign Language, Image Recognition, American Sign Language,...
