Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{209069,
        author = {Ishani HB and Khathija Rafida and Kadija Afra and Lakshmi Chandran and Shruthi},
        title = {NUTRISCAN: SMART FOOD LABEL ANALYZER FOR HEALTH AWARENESS},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {5},
        pages = {389-394},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=209069},
        abstract = {The increasing consumption of packaged food products makes it vital for consumers to understand the ingredients and nutritional information mentioned on the label of the food product. However, food labels often contain complex ingredient name, technical nutritional information, various additives, preservatives, and other components that maybe challenging to understand for an ordinary consumer. This paper presents a Food Label Analyzer for Health Awareness a smart system intended to simplify and analyze food product labels. The proposed system utilizes Optical Character Recognition (OCR) to extract textual information contained in the image of food label and employes Machine learning (ML) and Natural Language Processing (NLP) method for ingredient and nutritional analysis. Moreover, the system extracts relevant ingredient and recognizes harmful components such as excessive sugar, unhealthy fats, preservatives, artificial additives etc., and evaluates their possible impact, providing simplified information about each ingredient and its potential side effects. In addition, the system offers personalized analysis based on user’s requirements such as diabetes, hypertension, or food allergies. A health classification mechanism classifies products into Healthy, Moderate, and Avoid categories’ according to the obtained information. The proposed approach is intended to help bridge gap between complex information provided on the food label and consumers understanding, leading to better food awareness and healthier eating habits},
        keywords = {Food label Analysis, Machine Learning, Natural Language Processing, Nutritional Analysis, Optical Character Recognition (OCR), Personalized Health Analysis.},
        month = {October},
        }