Realtime Hand Gesture Recognition using LSTM model and Conversion into Speech
Author(s):
Sakshi Mankar, Kanishka Mohapatra, Ashwin Avate, Mansi Talavadekar, Surendra Sutar
Keywords:
Gestures, LSTM neural network, ReLU activation function, sign-language, tensorflow.
Abstract
People require communication to communicate with each other. “Specially abled people”, those who have speech or hearing disorder, “Mute” and “Deaf” people respectively, are always dependent on some sort of visual communication. People without visual and hearing disabilities sometimes face difficulties and cannot communicate with specially abled people due to lack of sign language education. Sign language is well received among them and they use it to express themselves. To achieve two-way communication between specially abled people and the general public there is a need to build a system that can interpret the gestures into text and speech. A vision-based technology of hand gesture recognition is an important part of human-computer interaction. Technology like gesture recognition can help us build a framework that can interpret sign language/gesture into text and speech. Gestures by hand which can represent a notion using unique shapes and finger position have a scope for human machine interaction. The major steps involved in designing the system are:  tracking, segmentation, gesture acquisition, feature extraction, gesture recognition and conversion into speech.
Article Details
Unique Paper ID: 154130

Publication Volume & Issue: Volume 8, Issue 10

Page(s): 120 - 124
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