Utilizing ML for Hand Gesture Recognition
Keywords:
OpenCv, Tensor flow, Machine Learning, Deep Learning, augmented reality, hand gesture recognitionAbstract
Hand gestures are an instinctive and common method of communication during human interactions, representing a form of non-verbal expression. Gesture recognition technology aims to interpret and classify meaningful movements performed by human hands. The motivation behind its development is to revolutionize human-computer interaction, addressing drawbacks found in current systems. The study presents a novel algorithm that eliminates the requirement for image background filtering. This algorithm proves versatile, capable of recognizing various hand gestures and accurately determining the count of raised fingers. By focusing on the hand movements within the region of interest, the proposed system enhances the efficiency and precision of gesture recognition. Gesture recognition has extensive applications, spanning from virtual and augmented reality to healthcare and industrial automation. Through this technology, users can engage more naturally and intuitively with digital environments, while healthcare professionals can provide real-time feedback during rehabilitation exercises. In industrial settings, gesture recognition enables the control of machines and robots, enhancing productivity and reducing manual labor. Advancements in computer vision, machine learning (ML), and sensor technology have greatly improved the accuracy and effectiveness of gesture recognition systems. With further research and development, gesture recognition is poised to revolutionize human-computer interactions across diverse fields, enriching user experiences and optimizing task performance. By continuously refining algorithms and incorporating innovative techniques, gesture recognition is poised to become an integral part of future human-machine interactions.
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