Abstract: Effective inventory management remains a persistent operational challenge for small and medium-sized retail enterprises, many of which continue to depend on manual record-keeping and intuitive judgment rather than data-driven methodologies. These practices yield elevated error rates, inadequate demand forecasting, and reactive rather than proactive restocking decisions, culminating in stock shortages, excess inventory accumulation, and avoidable financial losses. This paper presents the Smart Vision Inventory Advisor, a unified intelligent inventory management framework integrating Optical Character Recognition (OCR)-based product recognition with fuzzy matching algorithms for high-accuracy product identification. The system employs Tesseract OCR for text extraction from product images, followed by a fuzzy matching approach using difflib's get_close_matches and a custom keyword-based scoring mechanism that achieves 90–95% recognition accuracy. The system incorporates Multiple Linear Regression (MLR) for demand forecasting, leveraging features such as day-of-week, month, weather conditions, festival indicators, and discount information. Empirical evaluation demonstrated product recognition accuracy of 91.4%, a 77.1% reduction in cataloging time, and forecasting MAPE of 16.2%, representing a 33.9% improvement over moving-average baselines. Implemented exclusively using open-source technologies on standard consumer hardware, the system demonstrates that advanced AI-driven inventory management is both economically feasible and practically beneficial for the small retail sector.

Keywords — Optical Character Recognition; Inventory Management; Demand Forecasting; Multiple Linear Regression; Fuzzy Matching; Predictive Analytics; Retail Intelligence; Gradio

Downloads: | DOI: 10.17148/IJIREEICE.2026.14405