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= 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.

* [https://docs.google.com/presentation/d/1GQ5wBRtu48GJCEg6Ir4BddNnPqS7Ui3kmeCZ_TaRBi8/edit#slide=id.g70e2e194c1_2_97 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 =

= Approaches =

Two approaches have been tested so far:

* Regexes used on the OCR output. A new [https://github.com/openfoodfacts/robotoff/blob/master/doc/api.md#nutrient-prediction-predictnutrient /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.

* [https://drive.google.com/a/openfoodfacts.org/open?id=1XKyMCaq77LThhhmLmqZ-w_Oy_gV98IthajN4HjO7zxA Nutrition Table extraction architecture schema from @teolemon]

* [https://docs.google.com/spreadsheets/d/1fRLuzFGsoRgnHI59MgJcUcoj4hbT80I5ZUaZ4-9oV8U Table recognition litterature review]

= Previous attempts =

* Scoring des méthodes de détection de tableaux nutritionnels [https://drive.google.com/open?id=1-bo0v7eoRMtemZjcmqVzW0-0YMZaSOkB PDF] [https://drive.google.com/open?id=1KJ6H1HqHSHVBk3mWjEZZLLpcCt5whe6N docx]
* Analyse approfondie sur la détection
de tableaux nutritionnels [https://drive.google.com/open?id=1nPxOGFgjKaKuUcnVC0nbNAijt1al9rvo pdf]

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

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