Hand Gesture Recognition Systems: A Review of Vision-based and Sensor-based Approaches

Year : 2024 | Volume :01 | Issue : 01 | Page : 15-20
By

    Akash Rahate

  1. Akash Peddewad

  2. Vaibhav Chamvad

  3. Shruti Raykhelkar

  4. A.M. Chadchankar

  1. Student, Department of Computer Engineering, NBN Singhad School of Engineering, Pune, Maharashtra, India
  2. Student, Department of Computer Engineering, NBN Singhad School of Engineering, Pune, Maharashtra, India
  3. Student, Department of Computer Engineering, NBN Singhad School of Engineering, Pune, Maharashtra, India
  4. Student, Department of Computer Engineering, NBN Singhad School of Engineering, Pune, Maharashtra, India
  5. Assistant Professor, Department of Computer Engineering, NBN Singhad School of Engineering, Pune, Maharashtra, India

Abstract

With 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.

Keywords: Hand-sign, convolutional neural network, computer vision, image preprocessing

[This article belongs to International Journal of Optical Innovations & Research(ijoir)]

How to cite this article: Akash Rahate, Akash Peddewad, Vaibhav Chamvad, Shruti Raykhelkar, A.M. Chadchankar Hand Gesture Recognition Systems: A Review of Vision-based and Sensor-based Approaches ijoir 2024; 01:15-20
How to cite this URL: Akash Rahate, Akash Peddewad, Vaibhav Chamvad, Shruti Raykhelkar, A.M. Chadchankar Hand Gesture Recognition Systems: A Review of Vision-based and Sensor-based Approaches ijoir 2024 {cited 2024 Mar 01};01:15-20. Available from: https://journals.stmjournals.com/ijoir/article=2024/view=133980


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Regular Issue Subscription Review Article
Volume 01
Issue 01
Received October 18, 2023
Accepted November 14, 2023
Published March 1, 2024