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Sign language is a means of communication when we use gestures to communicate with people, especially the deaf and dumb. In these sets of functions, the features included and the linguistic diversity of the areas have been major obstacles that have led to small ISL research. This paper proposes a hand gestures detection sensor system using the Deep Learning Algorithm, Convolution Neural Network (CNN) to process image and predict gestures. Most sign language tools are available using expensive external sensors.
Our project aims to extend a step forward in this field by making our data set take pictures using a webcam and contains modules such as pre-processing features and features for rendering, training and model testing and sign-to-text conversion. then use different methods to remove features to extract useful information. There are 26 signs in Indian Sign Language corresponding characters where the proposed algorithm provided accurate results of up to 94% of all letters. translate it into text so that ordinary
people can understand it.
Keywords:
I n d i a n S i g n L a n g u a g e ( I S L ) , C o n v o l u t i o n N e u r a l N e t w o r k ( C N N ) , M u l t i l a y e r P e r c e p t r o n ( M L P ) , D e e p L e a r n i n g , D i l a t i o n , M o r p h o l o g i c a l O p e r a t o r ,
Cite Article:
"HAND SIGN (ISL) RECOGNITION MODEL FOR ALPHABETS USING CNN MODEL", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.7, Issue 12, page no.321 - 330, December-2022, Available :http://www.ijrti.org/papers/IJRTI2212041.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