Authors :
Abiram R; Vikneshkumar D; Abhishek E T; Bhuvaneshwari S; Joyshree K
Volume/Issue :
Volume 10 - 2025, Issue 4 - April
Google Scholar :
https://tinyurl.com/4d3w5bjj
Scribd :
https://tinyurl.com/d9ka5kb3
DOI :
https://doi.org/10.38124/ijisrt/25apr877
Google Scholar
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Abstract :
Hearing loss and communication challenges impact the lives of millions of individuals, particularly those who are
Deaf and hard of hearing. 43 million Indians and 466 million people worldwide suffer from debilitating hearing loss,
according to the World Health Organization (WHO). This group struggles to find work, healthcare, and education in India.
Given initiatives like the National Policy for Persons with Disabilities and the Right of Persons with Disabilities Act, there
are still gaps in ensuring full inclusion. By 2050, an estimated 2.5 billion individuals would have hearing loss, requiring 700
million people to undergo hearing rehabilitation, according to WHO estimates an extra 1 billion youths are at risk for
unintentional hearing loss due to unsafe listening practices. By bridging the gap between Deaf people and the general
communication world, our project, the Real-Time Sign Language Interpreter, aims to overcome these obstacles. This
innovative technology enables an uninterrupted communication by instantly translating hand movements into text and then
speech using AI and machine learning. Our project provides the equivalent of Beyond: Communication access for people
from the Deaf community, enabling greater participation in education, employment, and social life. Harnessing this
technology can do a lot with relatively low investment which, in turn, can provide an immense social return by making
services available to everyone, regardless of background or circumstances.
Keywords :
Sign Language Recognition, Gesture Recognition, Machine Learning, Computer Vision, AI.
References :
- Siddhant Dani et al., "Survey on the use of CNN and Deep Learning in Image Classification", 2021. https://scholar.google.com/scholar
- Michele. Russo, "AR in the Architecture Domain: State of the Art", Applied Sciences, vol. 11, no. 15, 2021. https://doi.org/10.3390/app11156800
- Agnieszka Mikołajczyk and Michał Grochowski, "Data augmentation for improving deep learning in image classification problem", 2018 international interdisciplinary PhD workshop (IIPhDW). IEEE, 2018. https://ieeexplore.ieee.org/document/8388338
- Moniruzzaman Bhuiyan and Rich Picking, "Gesture-controlled user interfaces what have we done and what's next", Proceedings of the fifth collaborative research symposium on security E-Learning Internet and Networking (SEIN 2009), 2009.
- Tianmei Guo et al., "Simple convolutional neural network on image classification", 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA). IEEE, 2017. https://ieeexplore.ieee.org/document/8078730
- Salima Hassairi, Ridha Ejbali and Mourad Zaied, "A deep stacked wavelet auto-encoders to supervised feature extraction to pattern classification", Multimedia Tools and applications, vol. 77, no. 5, pp. 5443-5459, 2018. https://doi.org/10.1007/s11042-017-4461-z
- Fifth Generation Computer Corporation-"Speaker Independent Connected Speech Recognition.
Hearing loss and communication challenges impact the lives of millions of individuals, particularly those who are
Deaf and hard of hearing. 43 million Indians and 466 million people worldwide suffer from debilitating hearing loss,
according to the World Health Organization (WHO). This group struggles to find work, healthcare, and education in India.
Given initiatives like the National Policy for Persons with Disabilities and the Right of Persons with Disabilities Act, there
are still gaps in ensuring full inclusion. By 2050, an estimated 2.5 billion individuals would have hearing loss, requiring 700
million people to undergo hearing rehabilitation, according to WHO estimates an extra 1 billion youths are at risk for
unintentional hearing loss due to unsafe listening practices. By bridging the gap between Deaf people and the general
communication world, our project, the Real-Time Sign Language Interpreter, aims to overcome these obstacles. This
innovative technology enables an uninterrupted communication by instantly translating hand movements into text and then
speech using AI and machine learning. Our project provides the equivalent of Beyond: Communication access for people
from the Deaf community, enabling greater participation in education, employment, and social life. Harnessing this
technology can do a lot with relatively low investment which, in turn, can provide an immense social return by making
services available to everyone, regardless of background or circumstances.
Keywords :
Sign Language Recognition, Gesture Recognition, Machine Learning, Computer Vision, AI.