Wearable sign language detection system by using convolutional neural network

Being hearing or speech differently-abled may hinder the communication of an individual, thus the goal of the proposed approach is to reduce the communication gap between a differently-abled and a normal person. We aim to develop a hand Glove that can convert sign language to speech, as well as disp...

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Bibliographic Details
Main Authors: Reddy, Raghavendra, Jain, Nishanth B., Sharma, Palash, V., Likhitha, Assadi, Mustafa Shihab
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
Communication
Hearing
Natural language processing
Neural networks
Programming languages
Sensors
Sign language
Speech
Online Access:Get full text
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Summary:Being hearing or speech differently-abled may hinder the communication of an individual, thus the goal of the proposed approach is to reduce the communication gap between a differently-abled and a normal person. We aim to develop a hand Glove that can convert sign language to speech, as well as display text on an App. In addition, the App will also be able to do the reverse and convert speech into sign language. To create a complete sign language translator App that can translate sign language into speech and vice versa. The proposed approach is aimed to solve the problems faced by hearing and speech impaired people and enable better communication for them. It is implemented by using Arduino, Convolutional Neural Network (CNN) and Natural Language Processing (NLP). Arduino will be combined with various sensors like flex sensors to get motion data. The image recognizer will be made by the use of CNN in python using Keras. All these will be integrated into a Streamlit App created using the dart programming language.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0184057