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@article{177343,
        author = {Neha Ranjan and Shalini Mhaske and Omkar Pattnaik and Prajakta Dawange and Gitanjali Khatik},
        title = {Deep Learning-Driven Character Classification in Indian Sign Language: A Real-Time Approach},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {463-471},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177343},
        abstract = {It can be quite difficult to communicate with someone who has a hearing loss. The study in this paper aims to investigate the challenges associated with character classification in Indian Sign Language (ISL). Sign dialect communication is ineffective for people with speech inadequacies or hearing impairments. To someone who has never learned this lingo, the signals given by those who are inadequate are confused or jumbled. It is necessary to communicate in both directions. In this work, we present an affirmation in Sign Dialect utilizing Indian Sign Lingo. The client must be able to record hand movements with a webcam for this test, and the system must be able to identify and show the picture's title. The obtained image is processed through a number of planning procedures that incorporate several computer vision methods, such as grayscale conversion, cover operation, and extension. Convolutional Neural Networks (CNN) are utilized for image recognition and display organization. The final perfection of our appearance is 95%.},
        keywords = {Convolutional Neural Network (CNN), Hand Gesture, Sign Language, Sign Language Recognition (SLR).},
        month = {May},
        }