Showstoppers for Ingredients Extraction and Analysis
Jump to navigation
Jump to search
Step 1 - Remove pet food & Cosmetics
- AI: identify them, especially with ingredients, and move them to OBF, OPFF, OPF
Step 2 - Fix ingredients stored in the wrong language
- AI: automatic image deselection & reselection
- AI: drastic cleanup of bad lists
- AI: Data quality facets about ingredients become errors
Step 3 - Fix errors of ingredients cutting
- Crop/Recrop more tightly: Autocrop ingredient lists
- Add more cutters in more languages
- Add more cutters in French
- Move it to AI/LLM: Extract automatically ingredient list from OCR
Step 4 - Fix errors with Bad OCRs
- Reprocess OCRs
- Perform spellcheck
Step 5 - Bad splitting into individual ingredients
- Add a multilingual no cut list of words like U.H.T Cat 1…
Step 6 - Bad ingredient properties recognition
- Processing, Origins, Other properties
Step 7 - Missing translations or synonyms
- Translate the taxonomies using LLM recipes
- Add synonyms
Step 8 - Truly untaxonomized ingredients
- Add those to the taxonomy
Step 9 - Missing or Bad CIQUAL associations
- Add CIQUAL matches
- Add matches from other Nutrition databases
Step 10 - Bad or insufficient categorization
- Identify problematic categories
- Play Hunger Games on problematic categories
Step 11 - Bad nutrition
- Clean nutrition
Step 12 - Additional data not the ingredient list
- Origins, -25% de sel, …