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@article{200679,
        author = {Thasmaishree R and Varsha S and Yashwini Arun and Sinduja B R},
        title = {Sign Bridge - Real-Time Sign Language Recognition and Intelligent Assistive System},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {1799-1802},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=200679},
        abstract = {People who use sign language face ongoing communication difficulties because their conversation partners do not understand their signing method. The existing digital communication technologies have developed better techniques but they still do not enable all users to access real-time communication solutions. The Signbridge system solves this problem by delivering instant hand gesture recognition which transforms gestures into understandable text and speech outputs. The system extracts hand landmark data through MediaPipe which uses computer vision techniques to transform visual input into structured numerical representations. The system uses K-Nearest Neighbors (KNN) algorithm to classify the features through processing. Recent research reveals that landmark-based techniques enhance both the speed of real-time gesture recognition and the reduction of computational requirements [2] [4]. The system combines natural language processing with text-to-speech functions to improve communication understanding and system functionality.},
        keywords = {Sign Language Recognition, Computer Vision, Machine Learning, MediaPipe, KNN, NLP, Text-to-Speech, Assistive Technology},
        month = {May},
        }