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@article{169908,
        author = {Putta Sanjana and Budiga Sai Krishna and Nukala Nithin and Cemarla Sai Darahas and Rohini Jadhav},
        title = {SIGN LANGUAGE RECOGNITION},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {2725-2729},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=169908},
        abstract = {Sign Language Recognition (SLR) technology aims to facilitate communication between hearing and hearing-impaired individuals by translating sign language into text or speech. With over 7,000 unique sign languages worldwide, SLR faces challenges due to diverse motions, hand shapes, and regional variations. Modern SLR systems use machine learning and computer vision techniques, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to capture gestures and facial expressions for accurate translation. Despite hurdles in handling regional dialects and high computational demands, SLR holds significant potential for enhancing accessibility in education, customer service, and daily interactions. Continued innovation in this field promises to make communication more inclusive and accessible.},
        keywords = {Sign Language Recognition (SLR), Automatic Sign Language Recognition (ASLR), Machine Learning, Computer Vision, Gesture Recognition, Deep Learning, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs).},
        month = {November},
        }