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de tableaux nutritionnels [https://drive.google.com/open?id=1nPxOGFgjKaKuUcnVC0nbNAijt1al9rvo pdf]
 
de tableaux nutritionnels [https://drive.google.com/open?id=1nPxOGFgjKaKuUcnVC0nbNAijt1al9rvo pdf]
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* Table extraction (V1): https://github.com/openfoodfacts/off-nutrition-table-extractor
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* Table extraction (V1): https://github.com/openfoodfacts/off-nutrition-table-extractor. Mainly focus on the nutritional table detection part, the extraction process remains quite simple.
 
* Table extraction (v2): https://github.com/cgandon/openfoodfacts-nutriments
 
* Table extraction (v2): https://github.com/cgandon/openfoodfacts-nutriments
    
= Other approaches =
 
= Other approaches =
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The previous attempts are not state of the art for the table extraction task. Other approaches we may consider:
    
* Detection of the table structure: identifying rows, columns and cells of the table. Most of the time rows corresponds to nutrition labels (protein, carbohydrate, energy,...) and columns to the quantity (100g, per portion, % of daily intake). By using pattern matching, we can label the column and rows and extract the values of the nutritional table for 100g or per portion.
 
* Detection of the table structure: identifying rows, columns and cells of the table. Most of the time rows corresponds to nutrition labels (protein, carbohydrate, energy,...) and columns to the quantity (100g, per portion, % of daily intake). By using pattern matching, we can label the column and rows and extract the values of the nutritional table for 100g or per portion.

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