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@article{173401,
        author = {B.Jhansi and A. Sai Manju and Y. Vinay Somaraju and B.V. Mani Kumar and B. Srinivas Raja},
        title = {Voice Assistant Traffic Sign Recognition Using Cnn},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {233-238},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=173401},
        abstract = {This paper centers on developing a software-based recognition module that integrates with a vehicle’s onboard camera system. using openCV, the system preprocesses images through resizing, color normalization, and edge detection. A Convolutional Neural Network (CNN), trained with TensorFlow, Keras & Image Data Generator, enhances classification accuracy by augmenting and preprocessing traffic sign datasets. Once a traffic sign is identified, real-time voice feedback is provided using text-to-speech conversion, allowing drivers to receive alerts without distractions. The backend, built with Django, manages the entire pipeline, ensuring seamless processing, model execution, and user interaction. The results show that the system accurately recognizes traffic signs even in different lighting and weather conditions and it correctly identifies and announces the traffic sign in real-time. By combining CNN-based image recognition with voice feedback, this system greatly improves driver assistance, making driving safer.},
        keywords = {Traffic Sign Recognition, Convolutional Neural Networks (CNN), Image Data Generator, OpenCV, Deep Learning, Real-Time Detection, Voice Assistance, Computer Vision, Machine Learning, Django, TensorFlow, Keras, Driver Assistance, Road Safety.},
        month = {March},
        }