This study presents a comprehensive survey of existing hand gesture recognition systems, their advantages and disadvantages, and their applications in various environments. Hand gesture recognition has been a topic of intense interest among researchers in Human-Computer Interaction (HCI) because of the prospects it holds for delivering more natural and intuitive user interfaces. Hand detection and segmentation are key steps involved in gesture recognition since they form the initial stages in movement identification and analysis. These operations can be performed effectively with programming languages like Python, which boasts extensive sets of libraries and tools for computer vision and machine learning. An exemplary application of this technology can be found in the SilentSpeak project, which is an innovative software program aiming to enable communication for those who are nonverbal or speech-impaired. SilentSpeak is an instant sign language interpreter, employing sophisticated computer vision and machine learning techniques to identify, classify, and interpret sign language signs, followed by translating them into written or spoken words. As well as bridging the communication divide between the hearing population and the nonverbal, this ability fosters an inclusive social infrastructure. SilentSpeak's technology architecture is multi-layered. HTML5, CSS, and JavaScript are used to implement the user interface. It is dynamic and interactive. The system's core is a machine learning model that does gesture recognition and is interfaced with the front-end via the Flask web framework in Python. Such a design allows the integration of the model and user interface to be simple, thereby enabling real-time gesture recognition and translation.
Hand Gesture Recognition, Human -Computer Interaction (HCI), Computer Vision, Machine Learning, SilentSpeak, Sign Language Interpreter, Real - Time Translation, Flask Framework
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