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@article{189068,
        author = {Arpan Tyagi and Dev Joshi and Manish Kushwaha and Nitu Pandey},
        title = {Hand gesture recognition},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {4731-4736},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=189068},
        abstract = {Hand gesture recognition (HGR) has emerged as a significant area of research in the fields of human–computer interaction (HCI), computer vision, and artificial intelligence. It enables natural and intuitive communication between humans and machines by interpreting hand movements and postures as input commands. This paper presents a comprehensive study on hand gesture recognition techniques, focusing on image-based and sensor-based approaches. Traditional methods employing feature extraction and machine learning algorithms are compared with recent advancements using deep learning architectures such as Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). The study also explores challenges including background noise, illumination variations, occlusion, and inter-user variability, which affect system accuracy and robustness. Furthermore, applications of HGR in virtual reality, sign language interpretation, robotics, and assistive technologies are discussed. The results highlight that integrating deep learning with efficient preprocessing and real-time optimization significantly enhances recognition accuracy and computational efficiency, paving the way for more immersive and accessible interactive systems.},
        keywords = {Hand Gesture Recognition, Human–Computer Interaction, Deep Learning, Computer Vision, Sign Language Recognition, CNN, Vision Transformer},
        month = {December},
        }