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@article{177967,
        author = {J. SRAVANTHI and M. SAI KIRAN and N. NARSIMHA and L. SHIVAMANI},
        title = {LIP READING USING DEEP LEARNING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {2487-2492},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177967},
        abstract = {This project presents an automated lip-reading system aimed at enhancing communication for individuals with hearing or speech impairments. By visually analyzing lip movements from video input, the system interprets spoken sentences without relying on audio, offering a non-verbal alternative to traditional speech. Leveraging deep learning techniques, the model extracts and processes both spatial and temporal patterns in lip motion to recognize speech with high accuracy [1], [3], [5]. In addition to sentence-level recognition, the system incorporates multilingual support, enabling it to translate recognized speech into multiple languages. This extends its applicability to diverse user groups and cross-language communication scenarios. The system has potential applications in assistive technology, silent communication interfaces, and accessibility tools. The implementation is based on the GRID dataset (single speaker) and employs machine learning libraries such as TensorFlow for neural network modeling and OpenCV for computer vision tasks.},
        keywords = {Deep Learning, Lip Reading, OpenCV, Tensor Flow.},
        month = {May},
        }