Abstract
The rapid growth of unstructured business data, such as invoices and receipts, has created significant challenges in efficient inventory management. This paper presents an intelligent web-based inventory automation platform that leverages Optical Character Recognition (OCR) and Natural Language Processing (NLP) techniques to streamline stock management processes. The system enables users to input data through text commands or upload documents, including PDFs, images, and spreadsheets, which are processed using OCR for text extraction. The extracted information is further refined using a Large Language Model (LLM)-based mapping approach to align unstructured data with structured inventory records stored in a Supabase database. The proposed system integrates a React-based frontend for user interaction with a FastAPI backend that performs document parsing, context-aware data processing, and intelligent mapping using Groq LLM. This approach provides a scalable and intelligent solution for automating inventory updates, making it suitable for modern enterprise applications dealing with high volumes of semi-structured and unstructured data.
Keywords
Inventory Automation, Optical Character Recognition (OCR), Natural Language Processing (NLP), Large Language Models (LLM), Groq LLM, Document Processing, AI-based Inventory Management.
Citations
IRE Journals:
S R Avinash, Dr. S Parthasarathy "Intelligent Web-Based Inventory Automation Platform using OCR & NLP Techniques" Iconic Research And Engineering Journals Volume 9 Issue 9 2026 Page 2987-2993 https://doi.org/10.64388/IREV9I9-1715732
IEEE:
S R Avinash, Dr. S Parthasarathy
"Intelligent Web-Based Inventory Automation Platform using OCR & NLP Techniques" Iconic Research And Engineering Journals, 9(9) https://doi.org/10.64388/IREV9I9-1715732
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