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@article{201074,
        author = {Mrs. J. Veerendeswari and Mr. Padhmanabban B and Mr. Mohamed Jaffar B and Mr. Karthikeyan S and Mr. Govardhan C},
        title = {BlindKit: An AI-Powered Perception and Interaction Assistant for Visually Impaired Individuals},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {no},
        pages = {323-330},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=201074},
        abstract = {Visually impaired individuals face significant challenges in perceiving their surroundings, accessing textual information, and interpreting social cues. Traditional assistive tools such as white canes and guide dogs provide limited contextual awareness and lack intelligent interaction capabilities. This paper presents BlindKit, an AI-powered perception and interaction assistant designed to enhance environmental understanding and social awareness for visually impaired users.
The proposed system integrates multiple artificial intelligence modules, including face recognition, emotion detection, optical character recognition (OCR), scene description, and sensory search, into a unified framework. The system captures real-time visual data using a camera module and processes it through computer vision and deep learning models to generate meaningful insights. These insights are converted into audio feedback using text-to-speech technology, enabling hands-free interaction.
Additionally, an SOS module is incorporated to provide emergency assistance. The system adopts a hybrid architecture combining IoT-based data acquisition and desktop-based AI processing to ensure near real-time performance. Experimental evaluation demonstrates promising accuracy across modules, with face recognition achieving up to 95.6% accuracy in controlled environments and OCR achieving over 98% accuracy under high-quality image conditions.
The results indicate that BlindKit significantly improves accessibility by transforming visual and social information into actionable auditory feedback. The proposed system provides a scalable and modular foundation for next-generation assistive technologies aimed at enhancing independence and quality of life for visually impaired individuals.},
        keywords = {Assistive Technology, Visual Impairment, Computer Vision, Face Recognition, Emotion Detection, Optical Character Recognition (OCR), Scene Understanding, Text-to-Speech (TTS), IoT, AI-based Accessibility},
        month = {May},
        }