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@article{188367,
        author = {Tanushree H S and Sujatha and Yuktha P achar and Prithu H S and Keerthi K S},
        title = {Visual and Voice-Assisted Inventory Automation},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {1998-2008},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=188367},
        abstract = {Traditional inventory management systems heavily rely on manual data entry, leading to inefficiencies, human errors, and time-consuming operations. This paper presents an AI-based visual and voice-controlled inventory management framework that leverages deep learning for object recognition and natural language processing for voice command interpretation. The proposed system integrates computer vision techniques for automatic item detection and categorization from images, coupled with voice-controlled interfaces for hands-free inventory operations. Built on a modern technology stack comprising React.js for the frontend, Node.js and Express for the backend, and MongoDB for data persistence, the system ensures real-time synchronization and scalable performance. The AI-powered object detection module utilizes convolutional neural networks trained on diverse product datasets to achieve robust item recognition under varying conditions. Comprehensive evaluation demonstrates the system's effectiveness in reducing manual effort by approximately 75%, improving accuracy to 94.3%, and enabling seamless multi-modal interaction. The framework presents significant potential for deployment in retail stores, warehouses, and business environments, offering a scalable foundation for smart inventory automation and predictive analytics integration.},
        keywords = {Artificial Intelligence, Computer Vision, Voice Recognition, Inventory Management, Deep Learning, Object Detection, Natural Language Processing, Real-Time Systems},
        month = {December},
        }