Review
Version 1
Preserved in Portico This version is not peer-reviewed
Hand Gestures Recognition System
Version 1
: Received: 25 June 2024 / Approved: 26 June 2024 / Online: 26 June 2024 (12:24:38 CEST)
How to cite: JAGLI, D. S.; Kumari, N.; Nakirekanti, L. Hand Gestures Recognition System. Preprints 2024, 2024061837. https://doi.org/10.20944/preprints202406.1837.v1 JAGLI, D. S.; Kumari, N.; Nakirekanti, L. Hand Gestures Recognition System. Preprints 2024, 2024061837. https://doi.org/10.20944/preprints202406.1837.v1
Abstract
Abstract: Hand gestures are an essential kind of nonverbal communication that let people engage and be expressive with one another. As technology has progressed, hand gesture recognition systems have become an important field of study, providing a link between human activities and artificial intelligence. An overview of the field of hand gesture recognition systems is given in this abstract, along with an explanation of its importance, difficulties, and possible uses. This abstract begins by examining the role that hand gestures have in communication. It then goes on to show the cultural significance and wide range of variations in gestural expressions that occur in many contexts and societies. Artificial intelligence systems face a unique problem in comprehending and interpreting these motions, which calls for the combination of computer vision, machine learning, and pattern recognition approaches. The abstract goes on to address why people develop hand motions. The purpose behind creating systems to recognize hand gestures is also covered in the abstract, with a focus on how these systems could improve human-computer interaction in several fields, including robotics, virtual reality, healthcare, and assistive technologies. These systems have the potential to enhance accessibility, efficiency, and user experience by allowing people to engage with devices and interfaces naturally and intuitively. The abstract also discusses environmental elements including backdrop clutter and lighting, as well as the technological difficulties in identifying and understanding hand gestures, which include variations in hand forms, orientations, and movements. To tackle these obstacles, strong algorithms that can record and interpret complex hand movements in real-time must be developed.
Keywords
Hand gesture recognition; Sign language interpretation; Media Pipe framework; Machine learning algorithms; Real-time gesture detection
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This 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.
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