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Nutrition facts table data extraction (view source)
Revision as of 14:03, 19 June 2020
, 14:03, 19 June 2020no edit summary
de tableaux nutritionnels [https://drive.google.com/open?id=1nPxOGFgjKaKuUcnVC0nbNAijt1al9rvo pdf]
de tableaux nutritionnels [https://drive.google.com/open?id=1nPxOGFgjKaKuUcnVC0nbNAijt1al9rvo pdf]
* Table extraction (V1): https://github.com/openfoodfacts/off-nutrition-table-extractor
* 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 =
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.