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@article{173884,
        author = {Rutuja Moon and Vaibhavi Bure and Priyanka Telang and Prajwal Mohod and Rupali Pawade and Prof. Apurva Bhuyar},
        title = {Sign Language Detection Using Computer Vision},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {2444-2460},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=173884},
        abstract = {This project aims to create a system for detecting sign language by utilizing computer vision methods. For those with hearing loss or who are deaf, sign language provides a crucial way to communicate. This approach aims to close the communication gap by translating sign language movements into written text automatically. The suggested solution involves utilizing a camera to capture images of people making sign language gestures, and then processing the image frames to recognize and interpret the hand movements. Algorithms are used to categorize these gestures and associate them with the appropriate words or phrases in sign language.
In the last ten years, sign language interpretation technology has made remarkable progress, working to resolve communication obstacles experienced by individuals with hearing disabilities. This research proposes a groundbreaking system that converts hand gestures into English text, which is then translated into Hindi. The research employs a hybrid methodology, combining software development, machine learning, and user testing to ensure both technical effectiveness and practical usability of the system. This investigation tackles shortcomings in previous methods by improving the speed and precision of gesture recognition.},
        keywords = {Computer vision, gesture recognition, machine learning, multilingual translation, real-time recognition, sign language detection, speed improvement, technical effectiveness, text conversion.},
        month = {March},
        }