Delving Deep into NutriScan: Automated Nutrition Table Extraction and Ingredient Recognition

Abstract

Abstract: Our paper introduces an innovative solution for automating the detection of nutrition tables and recognition of ingredients on packaged food products, addressing the growing demand for tools that facilitate the extraction of essential dietary information from food packaging. The increasing interest in healthy eating and dietary awareness underscores the need for precise and efficient tools to assist consumers and health-conscious individuals in making well-informed food choices. Harnessing state-of-the-art technology, we enhance the accuracy of nutrition table and ingredient detection through fine-tuning the EfficientDet model. We further extract textual content from these tables using PaddleOCR a sophisticated optical character recognition tool. Additionally, we employ regular expressions to meticulously capture crucial nutritional details, including salt or sodium content, carbohydrates, total fat, saturated fat, trans fat, protein values, additives, and allergens found in the product. To empower users in their dietary decision-making, we introduce a dynamic user preference system. Users can tailor their dietary requirements, specifying allergies and preferences for sugar, saturated fat, total fat, or salt content. Our system then evaluates food products and offers personalized recommendations including the nutri-score, indicating their alignment with the user's health goals. Extensive experimentation and evaluation confirm the high accuracy and efficiency of our method in both nutrition table and ingredient recognition. These paper underscores the transformative potential of our approach, which can revolutionize the accessibility of dietary information, encourage healthier eating habits, and promote dietary transparency