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@article{176361,
        author = {ROOPA V and DHANU SHRI J and VIDHYASHREE C and ABINAYA C and Dr.D.THILAGAVATHY},
        title = {REAL-TIME HAND SPEECH DYNAMIC GESTURE INTERPRETATION},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {7496-7501},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176361},
        abstract = {Real-time gesture recognition plays a crucial role in bridging the communication gap between individuals with speech and hearing impairments and those unfamiliar with sign language. This project presents a real-time hand gesture interpretation system using MediaPipe, OpenCV, and Convolutional Neural Networks (CNNs) to accurately recognize and interpret dynamic hand gestures. The system leverages computer vision and deep learning techniques to detect hand movements, extract key features, and classify gestures in real-time, converting them into meaningful text or speech output.By integrating CNNs, the system enhances recognition accuracy, while MediaPipe ensures efficient hand tracking and gesture segmentation. This approach improves accessibility, making it a valuable tool for assistive communication, smart interfaces, and human-computer interaction. The project aims to provide an inclusive, high-performance solution adaptable to various applications, including education, healthcare, and AI-driven interfaces. Future enhancements may include multi-language support and improved real-time processing for broader usability.},
        keywords = {Gesture Recognition, Deep Learning, Convolutional Neural Networks (CNNs), Human-Computer Interaction,Sign Language Interpretation.},
        month = {April},
        }