Online Analysis of Ingredient Safety, Leveraging OCR and Machine Learning for Enhanced Consumer Product Safety

Navigating the complex world of product ingredients can be a daunting task for health-conscious consumers. Often, ingredient labels are opaque and filled with jargon, hindering informed decision-making about product safety. This project addresses this challenge by developing a novel smartphone-based tool that leverages the power of image processing, Optical Character Recognition (OCR), and Natural Language Processing (NLP) techniques to empower consumers with accessible and transparent product safety assessments. The proposed tool harnesses smartphone cameras to capture ingredient lists. Image processing and OCR technologies extract the data, which is then analyzed by a Large Language Model (LLM) for comprehensive risk assessment. The LLM identifies potentially harmful components, and evaluates their interactions, generating a detailed and user-friendly safety report. The report offers clear explanations of risks, providing actionable insights for informed product choices. By providing an accessible and user-friendly tool, this project fosters transparency, trust, and responsible consumerism, ultimately safeguarding well-being through informed decision-making.