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@article{196810,
        author = {Harish Tukaram Gurme and Aditya Ravindranath Kamble and Sunil Biru Sangolkar and Samir Dattatray Karche and Onkar Balasaheb Gawade},
        title = {Sign Language Converter Using ML},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {4545-4550},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196810},
        abstract = {The Sign Language Converter driven by Machine Learning represents a proposed framework created to minimize the communication barrier experienced by deaf or hearing-impaired individuals when interacting with those unfamiliar with sign language. Its objective is to offer a straightforward and efficient means of interaction through contemporary technological approaches. The proposed framework incorporates two primary components: Gesture-to-Text Translation followed by Text-to-Speech Synthesis. In the initial component, hand signs are recorded through an imaging device and subsequently analyzed utilizing machine learning alongside computer vision algorithms. The framework interprets these hand signs and transforms them into coherent written output with satisfactory precision across varying environmental conditions. In the subsequent component, the resulting written text is transformed into spoken language using text-to-speech technology. This feature enables hearing individuals to comprehend the conveyed information with ease, thereby enhancing dialogue quality and interpersonal engagement. The framework is engineered to be intuitive, responsive, and precise in its operation. Its potential applications span educational institutions, professional environments, medical facilities, and community spaces, where it can facilitate improved dialogue and foster greater social integration for individuals with hearing loss.},
        keywords = {},
        month = {April},
        }