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@article{176553,
        author = {Chirantan Dev and Nishu Sinha and Aditya Raj and Ms. Sikha Singh},
        title = {SIGN LANGUAGE TRANSLATOR TO TEXT & SPEECH USING MACHINE LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {5765-5772},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176553},
        abstract = {Deaf and mute people can only express their needs and emotions through sign language. The majority of non-deaf-mute people do not comprehend sign language, which makes it difficult for deaf-mute persons to communicate in social situations. In recent years, there has been a lot of interest in sign language interpretation applications and systems. We examine research on machine learning, image processing, artificial intelligence, and animation tools for sign language detection and interpretation in this study. Illustrations are provided for the two reverse sign language interpretation procedures. Recent studies on translating sign language to speech and text using lip reading, hand gestures, and facial expression interpretation are covered in this paper. This work presents a real-time machine learning-based sign language translator that converts hand motions into audio and text using real-time machine learning. We have used computer vision techniques and Convolutional Neural Networks (CNNs) to recognize hand motions.},
        keywords = {CNN Algorithm, Gesture Recognition, OpenCV, Sign Language, YOLO},
        month = {April},
        }