Automated Text Extraction and Generation from Labels using Optical Character Recognition
Author : Dr. Krishnaveni Sakkarapani, Preetha R and Kiruthika R
Abstract :
The goal of this study, "Automated Text Extraction and Generation from Labels Using OCR" is to develop an AI-driven system that enhances efficiency in large-scale manufacturing and distribution by automating text extraction from product labels. In order to extract crucial product features, production information, and barcode numbers from label photos, this study uses Ollama's LLaMA 3.2-Vision and LLaMA 3 software. The data is then methodically stored in an Excel file for additional analysis. Hugging Face embeddings are incorporated to ensure high-quality text production and increase the extracted content's accuracy and coherence. Users may easily create structured material, extract text, and add photos using a web interface built on Streamlit. The findings demonstrate how incorporating computer vision, OCR, and NLP into inventory management and product tracking can improve supply chain transparency, error detection, and traceability while reducing manual labour and inconsistent data. Businesses may improve decision-making, expedite processes, and use AI-powered automation for predictive analytics and smart supply chains by automating label-based data management. In the end, this will turn traditional product monitoring into a data-driven, intelligent process.
Keywords :
AI-driven system, LLaMA 3.2-Vision, LLaMA 3, Ollama, Stramlit-based web interface, AI-powered automation for smart supply chains.
