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Akash Rahate, Akash Peddewad, Vaibhav Chamvad, Shruti Raykhelkar, A.M. Chadchankar
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- Student, Student, Student, Student, Assistant Professor, Department of Computer Engineering, NBN Singhad School of Engineering, Pune, Department of Computer Engineering, NBN Singhad School of Engineering, Pune, Department of Computer Engineering, NBN Singhad School of Engineering, Pune, Department of Computer Engineering, NBN Singhad School of Engineering, Pune, Department of Computer Engineering, NBN Singhad School of Engineering, Pune, Maharashtra, Maharashtra, Maharashtra, Maharashtra, Maharashtra, India, India, India, India, India
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Abstract
nWith many real-world uses, such as sign language translation and human-computer interaction, hand gesture detection is a crucial area of study in the science of computer vision. In this study, we propose a Convolutional Neural Network (CNN) model that uses real-time camera images to recognise hand gestures. A collection of hand motion photographs spanning the English alphabet (A-Z) was gathered, and the images were pre-processed to exclude any backdrop and create black and white versions. The CNN model, which was trained using the pre-processed images, produced a 95% accuracy rate on the test dataset. We used Python to develop the model and combined it with an intuitive software interface to predict hand gestures in real time with the system camera. We used Python to develop the model and combined it with an intuitive software interface to predict hand gestures in real time with the system camera. Our system has the potential to be an important resource in several sectors and domains, such as helping those with speech impairments and controlling technological devices. Real-time hand gesture recognition is made reliable and accurate by the suggested model and technology, which may be applied in a variety of contexts.
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Keywords: Hand-sign, convolutional neural network, computer vision, image preprocessing
n[if 424 equals=”Regular Issue”][This article belongs to International Journal of Optical Innovations & Research(ijoir)]
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References
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| Volume | 01 |
| [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | 01 |
| Received | October 18, 2023 |
| Accepted | November 14, 2023 |
| Published | November 28, 2023 |
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