OCR/Roadmap

Currently, all products are edited manually. This project is about automatic or semi-automatic detection of a number of things using OCR and Computer vision.

Product Opener improvements

• Process all uploaded images using Tesseract and/or the New Cloud based engine
• Return JSON to mobile client and/or web client for suggestions to the user
• Add support to search into OCR results

Short term goals

• Use the right standard dict for each language
• Integrate custom lists from Global Ingredients Taxonomy
• USDA UNII list of ingredients (will also work for Open Beauty Facts)
• Integrate custom lists from the live instances; language per language.
  • http://de.openfoodfacts.org/zutaten
  • http://uk.openfoodfacts.org/ingredients + http://us.openfoodfacts.org/ingredients
  • http:// fr.openfoodfacts.org/ingredients

Dictionaries

• https://openfoodfacts.slack.com/files/teolemon/F08FC3T6V/deu.user-words
• https://openfoodfacts.slack.com/files/teolemon/F08FBT3BM/eng.user-words
• https://openfoodfacts.slack.com/files/teolemon/F08FBNBLG/fra.user-words
• https://openfoodfacts.slack.com/files/teolemon/F08FBQ45D/nld.user-words
• https://openfoodfacts.slack.com/files/teolemon/F08FBQ45V/spa.user-words

Testing

Create a golden set of products that are complete

• Product
  • Category: "Ingredients complete" "Ingredient images selected"
  • Get the ingredients image
  • Get the canonical (typed by contributors) ingredient list
  • Get the ingredients list generated with the current OCR system
  • Generate the ingredient list on your laptop based on the image, and the custom dictionary above
  • Compare the result with the canonical/golden test and report some accuracy measures
• Draft Script: https://lite6.framapad.org/p/OFF_OCR_Script

Easy wins

• Process all images and make products searchable, even if not filled yet

Long-term goals

• Get dictionaries translations from Wikidata
• Investigate Ocropus for complex layout extractions
• Investigate Open CV for detection of patterns, logos…

Targets

• Logos of brands (Getting them from POD ?)
• Logos of Labels (standardized)
• Text (distorted - bottle case, diagonally - with low light, bright light)
• Standardized layouts (US Nutrition labels)
  • Store in separate image for further reference
• Standardized text (quantities, EU Packaging codes)
  • Store in separate image for further reference
• Barcodes (extraction in uploaded images)
  • Store in separate image for further reference
• Image orientation: check that the text is properly oriented to guess if the image is properly oriented.
• Deep Learning
  • Product photo on packaging - guess category based on product picture
  • Container: guess whether it's a bottle, cardboard…

Extracting areas is already great work: if we can extract logos or patterns, it will be faster for humans to double check and turn that into text.