Authors :
Abhijeet Basant; Shivprasad Chavarattil; Lance Dabreo; Prachi Dalvi
Volume/Issue :
Volume 8 - 2023, Issue 6 - June
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://tinyurl.com/y8r36f4d
DOI :
https://doi.org/10.5281/zenodo.8149817
Abstract :
Lack of speech is a recognized disability that
significantly impacts communication abilities.
Individuals with this disability employ various methods
to interact with others, with sign language being one of
the most prevalent and effective forms of
communication. Sign language allows deaf and hard of
hearing individuals to convey information within their
community and beyond. This study focuses on the
electronic recognition of sign language, encompassing
everything from sign production to text or speech output.
The recognition process involves distinguishing between
fixed and flexible touch gestures, and this study outlines
the steps undertaken. These steps include data
acquisition, preprocessing, data augmentation, feature
extraction, segmentation, and evaluation of the obtained
results. Additionally, this study provides
recommendations for future research in this area,
serving as a guide for further advancements in sign
language recognition.
Keywords :
Mediapipe, Sign language recognition [SLR], CNN, Computer Interaction with Humans.
Lack of speech is a recognized disability that
significantly impacts communication abilities.
Individuals with this disability employ various methods
to interact with others, with sign language being one of
the most prevalent and effective forms of
communication. Sign language allows deaf and hard of
hearing individuals to convey information within their
community and beyond. This study focuses on the
electronic recognition of sign language, encompassing
everything from sign production to text or speech output.
The recognition process involves distinguishing between
fixed and flexible touch gestures, and this study outlines
the steps undertaken. These steps include data
acquisition, preprocessing, data augmentation, feature
extraction, segmentation, and evaluation of the obtained
results. Additionally, this study provides
recommendations for future research in this area,
serving as a guide for further advancements in sign
language recognition.
Keywords :
Mediapipe, Sign language recognition [SLR], CNN, Computer Interaction with Humans.
