Vishwas H S, Suhruth R, Sourav Nagesh, Sai Nagesh C H, Manasa Sandeep
Keywords:
OpenCV, Python, facial recognition, LSTM, SVM, RNN, ANN.
Abstract
Deaf and mute individuals, who make up
approximately 5% of the global population, often rely
on sign language to communicate with others. However
many of them may not have access to sign language,
causing them to feel disconnected from others. To
address this communication gap, a prototype for an
assistive medium has been designed that allows
individuals to communicate using hand gestures to
recognize different characters, which are then
converted to text in real-time. This system utilizes
various image processing techniques and deep learning
models for gesture recognition. Hand gestures have the
potential to facilitate human-machine interaction and
are an essential part of vision-based gesture recognition
technology. The system involves tracking, segmentation
gesture acquisition, feature extraction, gesture
recognition, and text conversion, all of which are
critical steps in the design process. Overall, this
technology has the potential to help bridge the
communication gap between deaf and mute individuals
and those who can hear and speak.
Article Details
Unique Paper ID: 159899
Publication Volume & Issue: Volume 9, Issue 12
Page(s): 1174 - 1182
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