Authors :
Ch. Pavan Kumar; K. Devika Rani; G. Manikanta; J. Sravan Kumar
Volume/Issue :
Volume 9 - 2024, Issue 4 - April
Google Scholar :
https://tinyurl.com/3mjjt59r
Scribd :
https://tinyurl.com/4rp4t24c
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24APR2173
Abstract :
Sign language serves as a critical means of
communication for individuals with hearing
impairments, enabling them to integrate into society
effectively and express themselves. However,
interpreting and recognizing sign language gestures
present unique challenges due to the dynamic nature of
gestures and spatial dependencies inherent in sign
language communication. As a response, the SignSpeak
project employs advanced machine learning techniques
to address these challenges and enhance accessibility for
the deaf and hard of hearing community. The project
leverages a diverse dataset sourced from Kaggle,
comprising images of sign language gestures captured in
various contexts. The integration of advanced
algorithms, such as 3D Convolutional Neural Networks
(CNNs), and Gated Recurrent Units (GRUs), enables
SignSpeak to recognize and interpret sign language
gestures accurately and in real-time. This integration
allows the model to capture both spatial and temporal
features inherent in sign language, thus enabling more
robust and accurate recognition. The project
encompasses several critical stages, including data
preprocessing, model development, training, and
evaluation. Data preprocessing involves converting the
image data into a suitable format and applying
augmentation techniques to enhance the diversity and
robustness of the dataset. Model development entails
designing a deep learning architecture that combines
CNNs and GRUs to effectively capture spatial and
temporal dependencies in sign language gestures.
Training the model involves optimizing parameters and
hyperparameters to achieve optimal performance.
Evaluation metrics such as accuracy, F1 score, and recall
are utilized to assess the model's performance on both
training and validation datasets. The trained model is
then tested on a separate test dataset to evaluate its real-
world performance and generalization ability.
Experimental results demonstrate the efficacy of the
SignSpeak approach in accurately recognizing and
interpreting sign language gestures. The model achieves
high accuracy scores, demonstrating its potential to
enhance accessibility and inclusion for individuals with
hearing impairments. By providing real-time translation
of sign language into text or speech, SignSpeak
contributes to breaking down communication barriers
and promoting equal participation for all members of
society.
Sign language serves as a critical means of
communication for individuals with hearing
impairments, enabling them to integrate into society
effectively and express themselves. However,
interpreting and recognizing sign language gestures
present unique challenges due to the dynamic nature of
gestures and spatial dependencies inherent in sign
language communication. As a response, the SignSpeak
project employs advanced machine learning techniques
to address these challenges and enhance accessibility for
the deaf and hard of hearing community. The project
leverages a diverse dataset sourced from Kaggle,
comprising images of sign language gestures captured in
various contexts. The integration of advanced
algorithms, such as 3D Convolutional Neural Networks
(CNNs), and Gated Recurrent Units (GRUs), enables
SignSpeak to recognize and interpret sign language
gestures accurately and in real-time. This integration
allows the model to capture both spatial and temporal
features inherent in sign language, thus enabling more
robust and accurate recognition. The project
encompasses several critical stages, including data
preprocessing, model development, training, and
evaluation. Data preprocessing involves converting the
image data into a suitable format and applying
augmentation techniques to enhance the diversity and
robustness of the dataset. Model development entails
designing a deep learning architecture that combines
CNNs and GRUs to effectively capture spatial and
temporal dependencies in sign language gestures.
Training the model involves optimizing parameters and
hyperparameters to achieve optimal performance.
Evaluation metrics such as accuracy, F1 score, and recall
are utilized to assess the model's performance on both
training and validation datasets. The trained model is
then tested on a separate test dataset to evaluate its real-
world performance and generalization ability.
Experimental results demonstrate the efficacy of the
SignSpeak approach in accurately recognizing and
interpreting sign language gestures. The model achieves
high accuracy scores, demonstrating its potential to
enhance accessibility and inclusion for individuals with
hearing impairments. By providing real-time translation
of sign language into text or speech, SignSpeak
contributes to breaking down communication barriers
and promoting equal participation for all members of
society.
