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@article{173752,
        author = {Sunil Kumar K N and Karanam Vengababu and Pranava Raman R and Likhith Yadav M and Kishan R},
        title = {Sign Language to Text and Speech Conversion},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {4765-4772},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=173752},
        abstract = {Human creatures associated with each other to communicate their thoughts, contemplations, and encounters to the people around them. But usually not the case for deaf-mute individuals. Sign dialect clears the way for deaf-mute individuals to communicate. Through sign dialect, communication is conceivable for a deaf-mute individual without the implies of acoustic sounds. The point behind this work is to create a framework for recognizing the sign dialect, which gives communication between individuals with discourse impedance and ordinary individuals, subsequently decreasing the communication crevice between them. Compared to other motions (arm, face, head and body), hand signal plays an important role, because it communicates the user’s sees in less time. Within the current work flex sensor-based signal acknowledgment module is created to recognize English letter sets and few words and a Text-to- Speech synthesizer based on CNN is built to change over the comparing content. Objective: To form a computer program and prepare a show utilizing CNN which takes an picture of hand motion of American Sign Dialect and appears the yield of the specific sign dialect in content arrange changes over it into sound arrange.},
        keywords = {Sign language, gesture recognition, machine learning, computer vision, text-to-speech.},
        month = {April},
        }