Authors :
Hemendra Kumar Jain; Pendyala Venkat Subash; Kotla Veera Venkata Satya Sai Narayana; Dr S Sri Harsha; Shaik Asad Ashraf
Volume/Issue :
Volume 8 - 2023, Issue 11 - November
Google Scholar :
https://tinyurl.com/2p8j88zd
Scribd :
https://tinyurl.com/2w7ck53d
DOI :
https://doi.org/10.5281/zenodo.10200393
Abstract :
For the Deaf and hard of hearing people, sign
language is an essential form of communication.
However, because it is visual in nature, it poses special
difficulties for automated detection. The use of
convolutional neural networks (CNNs) for sign language
gesture identification is investigated in this paper. CNNs
are a viable option for understanding sign language
because of their impressive performance in a variety of
computer vision tasks. To prepare sign language images
for training and testing with a CNN model, this study
explores their preparation, which includes scaling,
normalization, and grayscale conversion. Multiple
convolutional and pooling layers precede dense layers for
classification in this TensorFlow and Keras-built model.
The model was trained and validated using a sizable
dataset of sign language movements that represented a
wide variety of signs. For many indications, the CNN
performs well, achieving accuracy levels that are
comparable to those of human recognition. It highlights
how deep learning approaches can help the Deaf
community communicate more effectively and overcome
linguistic barriers.
Keywords :
Sign Language Recognition, Convolutional Neural Networks (CNNs), Visual Communication, Deaf Community, Assistive Technology, Inclusive Communication.
For the Deaf and hard of hearing people, sign
language is an essential form of communication.
However, because it is visual in nature, it poses special
difficulties for automated detection. The use of
convolutional neural networks (CNNs) for sign language
gesture identification is investigated in this paper. CNNs
are a viable option for understanding sign language
because of their impressive performance in a variety of
computer vision tasks. To prepare sign language images
for training and testing with a CNN model, this study
explores their preparation, which includes scaling,
normalization, and grayscale conversion. Multiple
convolutional and pooling layers precede dense layers for
classification in this TensorFlow and Keras-built model.
The model was trained and validated using a sizable
dataset of sign language movements that represented a
wide variety of signs. For many indications, the CNN
performs well, achieving accuracy levels that are
comparable to those of human recognition. It highlights
how deep learning approaches can help the Deaf
community communicate more effectively and overcome
linguistic barriers.
Keywords :
Sign Language Recognition, Convolutional Neural Networks (CNNs), Visual Communication, Deaf Community, Assistive Technology, Inclusive Communication.
