Nutrition facts table data extraction

Introduction

The Nutrition facts table data extraction project aims to automatically extract the nutrition facts values from photos of nutrition facts table.

Extracting nutritional facts from images would allow to fill values when nutritional facts are missing and to check values filled by users.

Why it's very important

• We need nutrition facts to compute nutritional quality with the Nutri-Score
• It takes 2 minutes per product to type in nutrition values, a process that is also tedious and error-prone
  • 1M products x 2 minutes = 10 years of work full time without week-ends or vacations
• Being able to add complete data for a product quickly is key to attain a critical mass of products in new countries

• Presentation of the problem in French (for Share AI project)

Data, evaluation and test sets

Data sets

• https://github.com/openfoodfacts/openfoodfacts-ai/blob/master/data-sets.md

How the model fits in the OFF infrastructure

SVG source on Google Drawing

Approaches

Two approaches have been tested so far:

• Regexes used on the OCR output. A new /predict/nutrient endpoint has been added to Robotoff. This approach works best when nutritional information are not displayed in a table (“text” only).
• A clustering approach that tries to estimate the number of columns and lines of the nutritional tables using K-means or DBSCAN algorithms. Not integrated to Robotoff.

• Nutrition Table extraction architecture schema from @teolemon

• Table recognition litterature review

Previous attempts

• Scoring des méthodes de détection de tableaux nutritionnels PDF docx
• Analyse approfondie sur la détection

de tableaux nutritionnels pdf

• Table extraction (V1): https://github.com/openfoodfacts/off-nutrition-table-extractor
• Table extraction (v2): https://github.com/cgandon/openfoodfacts-nutriments