Food Ingredient Analysis Integrating OCR and LLM for Enhanced Consumer Safety

The evolution of food products has become rapid with time. There is a significant rise in the consumption of packaged food items, which has slowly started to become a concern for consumer health. Understanding the ingredients present in the packaged food is of utmost importance-but it is often overlooked. Consumption of such food items may lead to severe health complications to the consumers posed by uninformed consumption (e.g., allergens, additives, dietary mismatches). This paper proposes a solution to this problem-food ingredient scanner, that analyses the ingredients mentioned on the label of packaged food items and provides output to the consumers accordingly, helping them to understand the complex term easily. Through the combination of Text Extraction, Optical Character Recognition and Large Language Model, the system acts as a digital nutrition advising assistant that provides an insight of the ingredients-which may be necessary for the consumers to know. The system also includes a risk assessment module that highlights the potential health risks, allergens and dietary restrictions/considerations. Currently, a Minimum Viable Product (MVP) is developed to implement the core features. This result highlights the potential of the tool, to bridge the gap between the consumer’s lack of understanding of food ingredients that lead them to confusion, with a system that helps them understand the ingredients and provide valuable information that will provide them the insights, helping them decide whether to consume the packaged food item or not. This solution not only solves the issue, but also helps promote greater awareness of food safety to the society. This work also demonstrates how AI can enhance public health, by making food labels more accessible and comprehensive.