Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{166794,
        author = {Rameesa A.B and Bismin V Sherif},
        title = {A Convolutional Neural Network Framework for Robust Hand Gesture Recognition},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {2},
        pages = {1795-1801},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=166794},
        abstract = {Hand gesture recognition has become increasingly relevant in enhancing human-computer interaction across various applications, from virtual reality to assistive technologies. This paper introduces a novel approach using Convolutional Neural Networks (CNNs) to accurately recognize hand gestures and uniquely convert the recognized gestures into audio output. The system employs advanced preprocessing and training techniques to ensure high accuracy. The effectiveness of the proposed CNN model is rigorously compared with traditional models, including K-Nearest Neighbors (KNN), Random Forest, Support Vector Machine (SVM), and Artificial Neural Network (ANN). Experimental results demonstrate the superior performance of the CNN-based approach, offering a robust and innovative solution for real-time gesture recognition and audio feedback.},
        keywords = {Hand Gesture Recognition, Convolution Neural Network, k-Nearest Neighbors, Random Forest, SVM},
        month = {July},
        }