Authors : Sahilee Misal

Volume/Issue : Volume 9 - 2024, Issue 8 - August

Google Scholar : https://tinyurl.com/5fnpsfdc

Scribd : https://tinyurl.com/5en5w84k

DOI : https://doi.org/10.38124/ijisrt/IJISRT24AUG154

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Hand gesture recognition (HGR) has gained significant attention due to its potential for various applications. This paper explores the use of deep learning, specifically Convolutional Neural Networks (CNNs), for HGR using the TensorFlow library. We investigate existing research on CNN-based HGR, focusing on image classification tasks. We then provide a brief overview of CNNs and their suitability for image recognition. Subsequently, we describe the typical workflow of a deep learning-based HGR system, including data preprocessing, hand detection, feature extraction with CNNs, and classification. We highlight the advantages of using TensorFlow to build and train CNN models for HGR. Finally, we conclude by summarizing the key findings from related work and mentioning the specific dataset and number of gestures classified in our research. This work contributes to the growing body of research on CNN-based HGR using TensorFlow and emphasizes its potential for developing accurate and efficient HGR systems.

Keywords : Hand Gesture Recognition, Machine Learning, CNNs, Hand Detection, Feature Extraction, Tensorflow, Image Classification.

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