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@article{174965,
        author = {Dhanashree B. Shinde and Yash Bharat Shewale and Himanshu Santosh Deshmukh and Kunal Vikas Chothe and Samruddhi Somnath Baravkar},
        title = {Hand Gesture Recorgnization},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {1199-1201},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174965},
        abstract = {Hand gesture recognition is a crucial field within human-computer interaction, enabling more intuitive and seamless interfaces. This study presents a novel approach to hand gesture recognition using advanced machine learning techniques. We developed a system that accurately classifies a wide range of hand gestures in real-time, leveraging convolutional neural networks (CNNs) for feature extraction and recurrent neural networks (RNNs) for temporal sequence recognition. Our model was trained on a diverse dataset comprising of thousands of labeled hand gesture images. Preliminary results indicate a high accuracy rate of recognition, surpassing existing benchmarks. Furthermore, the system is optimized for low-latency processing, making it suitable for applications in virtual reality, gaming, and assistive technologies. Future work includes expanding the gesture database and improving robustness against various lighting conditions and hand orientations. Our research demonstrates significant strides toward more natural and dynamic user interfaces, fostering more immersive and accessible technology interactions.},
        keywords = {Hand Gesture Recognition, Virtual Reality, High Accuracy Rate, Natural User Interfaces},
        month = {April},
        }