Machine Learning Model for Optical Character Recognition-Based Food Allergen Detection with Recommendation System for Alternative Food

Authors

  • Rugved Borade, Arishi Gupta, Anupriya Kathpalia, Tanish Jain, Priti Chakurkar

Keywords:

OCR, Regular Expressions, Recommendation, Database, Food packages, Allergies, Cosine Similarity

Abstract

In today’s diverse and fast-paced food industry, ensuring consumer safety and meeting specific dietary needs is of paramount importance. Food allergen detection and recommendation systems have emerged as crucial tools to address these concerns. This project aims to create an innovative OCR based solution for automating the identification of allergenic ingredients on food packaging labels. By combining Optical Character Recognition (OCR) technology with a comprehensive allergen database, the system will provide real-time allergen information to consumers. Moreover, it will recommend suitable food alternatives for individuals with specific dietary restrictions, enhancing their shopping experience and reducing the risk of allergen-related incidents. The allergen knowledge base is implemented using several machine learning algorithms and will be updated constantly. This holistic approach not only promotes food safety but also empowers consumers to make informed choices, fostering a healthier and more inclusive food environment.

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References

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Published

26.03.2024

How to Cite

Rugved Borade,. (2024). Machine Learning Model for Optical Character Recognition-Based Food Allergen Detection with Recommendation System for Alternative Food. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 1869–1875. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5757

Issue

Vol. 12 No. 21s (2024)

Section

Research Article

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