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@article{203539,
        author = {ASHISH and Mrs. Sangeeta lalwani and Ms. Shreya verma},
        title = {“NeuroSign” “REAL-TIME HAND GESTURE RECOGNITION SYSTEM”},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {12218-12225},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=203539},
        abstract = {The Challenge of Real-World Translation Sign language is the primary communication bridge for the global Deaf and Hard of Hearing (DHH) community. However, developing reliable, cost-effective, software-only translation systems that function in unpredictable, real-world environments remains a formidable technological challenge. The NeuroSign project addresses this gap by documenting the complete evolution of a real-time American Sign Language (ASL) recognition system from a traditional machine learning pipeline to a robust deep learning framework.

NLP Integration and Final Deployment While detecting isolated gestures is a massive technical milestone, fluid human communication requires semantic understanding. To bridge this gap, NeuroSign integrates a sophisticated Natural Language Processing (NLP) pipeline. Utilizing temporal token buffering, the system collects raw YOLO predictions over time. The NLP engine then performs deep contextual analysis, translating the disjointed stream of ASL signs into grammatically coherent, conversational English sentences. Finally, this advanced architecture is packaged as an accessible Flask web application. Featuring a real-time Diagnostic Heads-Up Display (HUD) that tracks live latency, prediction confidence, and expanding transcripts, NeuroSign emerges as a comprehensive, production-ready communication tool empowering the DHH community.},
        keywords = {},
        month = {May},
        }