Sign Language Recognition for Vehicle Control: A Deep Learning and Arduino Approach
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Keywords:
Sign Language Recognition, Deep Learning, Gesture Control, CNN, Arduino IntegrationAbstract
This study presents a novel sign language recognition system designed for vehicle control,
leveraging deep learning and Arduino technology. As traditional human-computer interaction methods
evolve, gesture recognition has emerged as an intuitive alternative, particularly beneficial for individuals
with hearing disabilities. With over 466 million people affected by hearing loss globally, effective
communication tools are increasingly vital. The proposed system utilizes a pre-trained Convolutional
Neural Network (CNN), specifically the VGG19 model, which was fine-tuned on a dataset comprising
4,769 images of hand gestures. The integration of image processing techniques with Arduino enables
real-time gesture recognition and control of vehicle functionalities. The results demonstrate an impressive
accuracy of 99.79% for gesture recognition, validating the effectiveness of deep learning methodologies
in this domain. This approach not only enhances accessibility for users with hearing impairments but also
paves the way for innovative applications in smart environments, interactive gaming, and assistive
technologies.
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References
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