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@article{207561,
        author = {Nagaraj Nimbure},
        title = {Hand Gesture Control Software System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {3},
        pages = {1607-1610},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=207561},
        abstract = {Gesture control technology has emerged as a revolutionary human-computer interaction paradigm that enables users to control devices and applications through hand and body movements without physical contact. The global gesture recognition market is expected to grow at a CAGR of 15.2% by 2027, driven by increasing demand for contactless interfaces in healthcare, entertainment, and smart homes. This research paper presents a comprehensive study on gesture control software systems, examining existing methodologies, architectural frameworks, and emerging technologies. We categorize gesture recognition approaches into five distinct categories: vision-based, sensor-based, deep learning-based, hybrid, and real-time processing systems. The paper analyzes machine learning algorithms including CNN, RNN, SVM, and transformer networks that have demonstrated superior accuracy in gesture recognition tasks. We propose an integrated gesture control system that combines computer vision with machine learning to achieve robust real-time gesture recognition with minimal latency. The system achieves 94.7% accuracy in recognizing 25 distinct hand gestures across varying lighting conditions and distances. Additionally, we discuss practical applications in virtual reality, augmented reality, smart home automation, and accessibility solutions for individuals with mobility impairments. The proposed framework addresses challenges such as background noise, occlusion handling, and multi-hand gesture disambiguation. Our results demonstrate that hybrid approaches combining multiple sensor modalities outperform single-sensor systems by 12-18% in accuracy metrics. This research contributes to advancing contactless interaction technologies and provides a foundation for next-generation human-computer interfaces.},
        keywords = {Gesture Recognition, Computer Vision, Deep Learning, Human-Computer Interaction, Real-time Processing},
        month = {August},
        }