Authors : Ashlesha Dhawanjewar
DOI : 10.46335/IJIES.2024.9.6.1
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Abstract— Hand gesture recognition, particularly in the context of sign language recognition for deaf-mute individuals, has garnered significant attention due to its potential to enhance communication accessibility. This paper reviews recent advancements in hand gesture recognition, focusing on deep learning methodologies. Specifically, we explore the application of convolutional neural networks (CNNs) in efficiently detecting and recognizing hand gestures. By utilizing an input-process- output framework, we analyze various approaches to hand gesture recognition, highlighting their effectiveness in sign language interpretation applications. Through this review, we aim to provide insights into the current state-of-the-art techniques and identify potential areas for further research and improvement in this domain.
