Authors : Aditi Patil; Shravani Jamdade; Sia Shah; Viranchi Kamble; Lekha Surana

Volume/Issue : Volume 11 - 2026, Issue 4 - April

Google Scholar : https://tinyurl.com/2z43hkj4

Scribd : https://tinyurl.com/2v9ehmvm

DOI : https://doi.org/10.38124/ijisrt/26apr897

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : People need to learn about nutritional information because health awareness increases together with rising consumption of packaged foods. Yet consumers experience difficulties when they attempt to read nutrition labels because of the complex design , technical words and small font sizes. This paper presents NutriScan, which functions as an AI-powered web application that automatically retrieves and analyzes the nutritional data from packaged food labels through its use of Optical Character Recognition (OCR) and machine learning techniques. The system begins by capturing nutrition label images which it processes through preprocessing methods that enhance text quality before using OCR technology to extract important textual information. The system identifies essential nutritional components which includes categories like calories, sugars, fats and sodium. It compares the categories with established dietary guidelines which include FSSAI and WHO recommended standards. The system employs a supervised machine learning model to determine different levels of health risks which people face. The results are shown through colorcoded indicators that match different levels of health status levels which consumers can easily understand. The system serves educational purposes together with awareness purposes since it does not offer medical recommendations.

Keywords : Optical Character Recognition, Nutrition Label Analysis, Machine Learning, Health Informatics

References :

1. T. M. E. Saputara ,et al. “Multimodal Fine-Grained Grocery Product Recognition Using Image and OCR Text,” Machine Vision and Applications, vol. 35, no. 4, article 79, pp. 1–20, June 2024, doi: 10.1007/s00138-024-01549-9.
2. S. P. Siddique Ibrahim, L. Karthikeyan, P. Bhoomika, and K. Tejaswi, “Automating Nutritional Claim Verification Using OCR and Machine Learning,” Research Report, July 2025.
3. J. Castro and H. Min, “NutritionWatch: Machine Learning for Sugar and Additive Classification,” Proc. IEEE International Conference on Big Data, 2020, pp.
4. M. Sanghani and R. Patel, “Augmenting Diet Tracking with OCR,” International Journal of Engineering Research & Technology (IJERT), vol. 10, no. 3, pp. 155–160, 2021.
5. S. Ghosh, A. Banerjee, and D. Chakraborty, “Food Label OCR Using Deep Learning,” Procedia Computer Science, vol. 167, pp. 231–240, 2020.
6. S. Rahman, M. Hossain, and T. Ahmed, “Deep-Learning Based Food Recognition and Nutrient Estimation,” IEEE Access, vol. 8, pp. 53713–53725, 2020.
7. R. Singh, A. Verma, and S. Gupta, “Intelligent Food Analyzer Using OCR and Machine Learning,” International Journal of Scientific & Technology Research, vol. 9, no. 4, pp. 1289–1295, 2020.
8. W. Li, J. Zhang, and Y. Wang, “Visual Ingredient Extractor for Food Packaging Using Object Recognition,” ICIP 2019 – International Conference on Image Processing, pp. 1895–1899, 2019.
9. C. Roux and T. Steiner, “Open Food Data APIs for Health Analysis: A Survey,” ACM Computing Surveys, vol. 53, no. 4, pp. 1–28, 2021.
10. A. Fleury, J. Martin, and P. Dubois, “Yuka: Mobile Food Quality Scanning Application,” Proc. International Conference on Mobile Computing and Applications, 2020, pp. 210–217.
11. A. Sharma, R. Gupta, and P. Mehta, “AI-Based Nutritional Label Recognition Using OCR and Deep Learning,” IEEE Access, vol. 9, pp. 125432–125441, 2021.
12. S. Das and N. Chatterjee, “Smart Food Analysis Using Machine Learning Techniques,” International Journal of Computer Applications, vol. 183, no. 47, pp. 28–35, 2022.
13. M. Jain, V. Bansal, and T. Kaur, “Health Classification of Packaged Foods Using Supervised Learning,” Proceedings of the 2020 International Conference on Intelligent Computing and Applications (ICICA), pp. 115–123, 2020.
14. S R. Narayan and A. Kumar, “OCR-Based Text Extraction for Food Product Labels,” International Journal of Engineering Research and Technology (IJERT), vol. 11, no. 4,pp. 501–507, 2022.
15. Food Safety and Standards Authority of India (FSSAI), “Manual of Methods for Food Safety,” Ministry of Health and Family Welfare, Government of India, 2023.
16. World Health Organization (WHO), “Nutrient Profiling for the Development of Healthier Food Environments,” WHO Technical Report, 2022.
17. R. Singh, P. Patel, and K. Deshmukh, “AI-Driven Nutrition Estimation from Food Packaging,” International Conference on Data Science and Intelligent Applications (ICDSIA), pp. 141–150, 2021.
18. L. Chen, Y. Zhao, and J. Zhou, “Rule-Based Nutrient Evaluation Model for Dietary Recommendation,” Expert Systems with Applications, vol. 197, pp. 116850, 2022.
19. S. Rao and D. Menon, “Enhancing Food Label Legibility Using Image Preprocessing and OCR,” International Journal of Image Processing (IJIP), vol. 16, no. 2, pp. 63–72, 2022.
20. N. Verma and H. Thomas, “AI and Data Analytics in Food Safety: A Review,” Journal of Food Informatics and Safety, vol. 5, no. 1, pp. 22–34, 2023.
21. A. Sharma, R. Gupta, and P. Mehta, “AI-Based Nutritional Label Recognition Using OCR and Deep Learning,” IEEE Access, vol. 9, pp. 125432–125441, 2021.
22. S. Das and N. Chatterjee, “Smart Food Analysis Using Machine Learning Techniques, International Journal of Computer Applications, vol. 183, no. 47, pp. 28–35,2022.
23. J.E. Nalavade, M. Gavali, N. Gohil, and S. Jamale, “Impelling Heart Attack Prediction System using Data Mining and Artificial Neural Network,” International Journal of Current Engineering and Technology, vol. 4, no. 3, pp. 1-5, Jun. 2014.
24. A. Kumar, J. E. Nalavade, V. Yeola, V. Vivek, and Y. Srivastava, “An adaptive learning system based on ant colony algorithm,” Journal of Soft Computing and Engineering (IJSCE), vol. 3, no. 2, pp. 212- 214, 2013
25. J. E. Nalavade, A. Patil, A. Buchade, and N. Jadhav, “Deep Neural Network and GAN-Based Reversible Data Hiding in Encrypted Images: A Privacy-Preserving Approach,” SN Computer Science, vol. 5, no. 1, p. 45, Nov. 2023.
26. C. Nashte, J. E. Nalavade, A. Darvemula, S. Singh, and M. Bhowmick, “Differentiation of Malignant and Benign Breast Lesions Using Machine Learning Algorithms,” Differentiation, vol. 4, no. 5, May 2017.
27. D. R. Kale , J.E. Nalavade, S. Hirve, S. Tamboli, P. S. Randive, and N. Karlekar, “Quantum Machine Learning Algorithms for Optimization Problems: Theory, Implementation and Applications”, International Journal Of Intelligent Systems and Applications in Engineering, vol. 12, no. 4, Jun 2024.