Exploration of OpenCV for Hand Gesture Recognition Techniques - A Review

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dc.contributor.authorMeher, Kunal
dc.date.accessioned2025-05-20T10:17:19Z
dc.date.available2025-05-20T10:17:19Z
dc.date.issued2024
dc.descriptionuGDX
dc.description.abstractThis paper reviews hand gesture recognition techniques by leveraging the strengths of OpenCV and TensorFlow, two of the most widely used libraries in computer vision and deep learning. OpenCV's sophisticated image processing capabilities are utilized for preprocessing, feature extraction, and establishing a solid foundation for further analysis. TensorFlow is employed to construct and train deep neural networks capable of identifying fine-grained details and subtle variations in hand gestures. This integration allows for precise and accurate differentiation and understanding of a predefined set of gestures, demonstrating the potential for robust hand gesture recognition systems.
dc.identifier.issn1660-6795
dc.identifier.urihttps://atlasuniversitylibraryir.in/handle/123456789/767
dc.language.isoen
dc.publisherNanotechnology Perceptions
dc.subjectvHand Gesture Recognition
dc.subjectOpenCV
dc.subjectTensorFlow
dc.subjectComputer Vision
dc.subjectDeep Learning
dc.subjectImage Processing
dc.subjectNeural Networks
dc.titleExploration of OpenCV for Hand Gesture Recognition Techniques - A Review
dc.typeArticle

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