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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.
 
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.
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Tools:
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== Current state ==
* Google Drive OCR or Google Goggles
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* OCR extraction of Ingredients using Tesseract 2 (production) and 3 (.net)
* Ocropus
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* Uses the French dictionary for all languages
* OpenCV
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<pre>
* Moodstocks
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-- /home/off-fr/cgi# grep get_ocr *
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Ingredients.pm:use Image::OCR::Tesseract 'get_ocr';
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Ingredients.pm: $text =  decode utf8=>get_ocr($image,undef,'fra');
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</pre>
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* Has a small custom dictionary for French ( /usr/share/tesseract-ocr/tessdata/fra.user-words)
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**https://code.google.com/p/tesseract-ocr/wiki/FAQ#How_do_I_provide_my_own_dictionary
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== Short term goals ==
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* Use the right standard dict for each language
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* Integrate custom lists from Global Ingredients Taxonomy
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**Create a golden set
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*** e.g.  someproduct.jpg -> ingredients image
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*** someproduct.golden -> ingredients text
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*** then we create a script that runs the OCR through the images, compare with the golden text, and report some accuracy measures
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*** Draft Script: https://lite6.framapad.org/p/OFF_OCR_Script
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* Integrate custom lists from the live instances; language per language.
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** http://de.openfoodfacts.org/zutaten
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** http://uk.openfoodfacts.org/ingredients + http://us.openfoodfacts.org/ingredients
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**http:// fr.openfoodfacts.org/ingredients
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** USDA UNII list of ingredients (will also work for Open Beauty Facts)
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* Process all images and make products searchable, even if not filled yet
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==  Long-term goals ==
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* Get dictionaries translations from Wikidata
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* Investigate Ocropus for complex layout extractions
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* Investigate Open CV for detection of patterns, logos…
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Targets:
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== Targets ==
* Logos (standardized)
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* Logos of brands (Getting them from POD ?)
* Text
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* Logos of Labels (standardized)
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* Text (distorted - bottle case, diagonally - with low light, bright light)
 
* Standardized layouts (US Nutrition labels)
 
* Standardized layouts (US Nutrition labels)
 
* Standardized text (quantities, EU Packaging codes)
 
* Standardized text (quantities, EU Packaging codes)
 
* Barcodes (extraction in uploaded images)
 
* Barcodes (extraction in uploaded images)
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** Store in separate image for further reference
 
* Image orientation: check that the text is properly oriented to guess if the image is properly oriented.
 
* Image orientation: check that the text is properly oriented to guess if the image is properly oriented.
 
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* Deep Learning
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** Product photo on packaging - guess category based on product picture
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** 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.
 
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.
    
[[Category:Project]]
 
[[Category:Project]]
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[[Category:Product Opener]]

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