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Gesture Connect Gesture Recognition for Sign Language

Umesh Pinjarkar, Heena Sheikh, Saurabh Tripathi, Kaustubh Sonkusale

Abstract


A natural method of human-computer interaction, hand gesture recognition is a hot topic in computer vision and machine learning research. This field offers a wide range of potential applications, allowing users to interact with robots and system interfaces in a more straightforward and natural way without requiring additional hardware. One of the primary goals of gesture recognition research is to create systems that can identify gestures and use them to send data or control a device. But gestures need to be represented in both the temporal and spatial domains; a hand posture is the hand's static structure, whereas a gesture is its dynamic mobility. Researchers studying gesture recognition in human-machine interaction have shown that hands are among the most important instruments for human communication in daily life, which has led to the use of this technology in a range of settings. Therefore, the main objective of research on gesture recognition in the context of human-computer interaction (HCI) is to develop systems that can recognize certain human gestures and use them to operate devices or transmit information. To do this, real-time gesture recognition and quick, incredibly reliable hand detection are necessary for vision-based hand gesture interfaces. With the aid of machine learning methods, this work proposes a sufficiently generic approach that may be used in a variety of HCIs.


Keywords


Gesture Recognition, Machine learning, Robotics, Vision, Sign language

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References


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DOI: https://doi.org/10.37591/rtsrt.v10i2.7696

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