CNN Based Real Time Recognition of Hand Gestures for Indian Sign Language

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Published Apr 7, 2025
https://doi.org/10.47164/ijngc.v16i1.1632
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Volume 16, Issue 1, March 2025

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Pradip Patel
L. D. College of Engineering, Ahmedabad, Gujarat, India
Narendra Patel
B.V.M. Engineering College, V.V.Nagar, Gujarat, India

Abstract

Human Centered Computing is a rising field of research having goal of understanding behavior of human being. In this field, Sign Language Recognition is noteworthy application with many challenges. It bridges the communication gap that separates hearing individuals from deaf people. In this article, a computer vision techniques based application for recognising Indian Sign Language (ISL) is presented. Convolutional Neural Networks (CNNs), the most remarkable kind of algorithms for deep learning, are used in its construction. Initially, the self built dataset containing training images has been used to train the system. The system interprets a variety of live camera gestures into text and voice. To further enhance the system's usability, a graphical user interface (GUI) is also built. Additionally, it also offers rotation, translation, and scale invariance. The CNN's weight sharing and sparse connectivity make the suggested system incredibly effective in the context of storage and performance. It is found to be an extremely accurate system with a 99.44% recognition rate during the testing.

Keywords:

Deep Learning, Sign Language, Transformation Invariance, Convolutional Neural Network, Classification

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Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite
Patel, P., & Narendra Patel. (2025). CNN Based Real Time Recognition of Hand Gestures for Indian Sign Language. International Journal of Next-Generation Computing, 16(1). https://doi.org/10.47164/ijngc.v16i1.1632
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