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Nutrition facts table data extraction (view source)
Revision as of 14:01, 19 June 2020
, 14:01, 19 June 2020no edit summary
* Table extraction (V1): https://github.com/openfoodfacts/off-nutrition-table-extractor
* Table extraction (V1): https://github.com/openfoodfacts/off-nutrition-table-extractor
* Table extraction (v2): https://github.com/cgandon/openfoodfacts-nutriments
* Table extraction (v2): https://github.com/cgandon/openfoodfacts-nutriments
= Other approaches =
* 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.
* Using an end-to-end model to directly extract nutritional values. This may be done by scoring candidates for each field (protein_100g, protein_serving, carbohydrate_100g,...), and selecting the highest scoring token as the field value. This is similar to [https://azure.microsoft.com/fr-fr/services/cognitive-services/form-recognizer Microsoft Form Recognizer API] or [https://cloud.google.com/document-ai/docs Google Document API].
Each approach requires different kinds of annotation, so chosing the most effective approach at the beginning of the project is crucial.