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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:
| + | == Current state == |
− | * Google Drive OCR or Google Goggles | + | * OCR extraction of Ingredients using Tesseract 2 (production) and 3 (.net) |
− | * Ocropus | + | * Uses the French dictionary for all languages |
− | * OpenCV | + | <pre> |
− | * Moodstocks | + | -- /home/off-fr/cgi# grep get_ocr * |
| + | Ingredients.pm:use Image::OCR::Tesseract 'get_ocr'; |
| + | Ingredients.pm: $text = decode utf8=>get_ocr($image,undef,'fra'); |
| + | </pre> |
| + | * Has a small custom dictionary for French ( /usr/share/tesseract-ocr/tessdata/fra.user-words) |
| + | **https://code.google.com/p/tesseract-ocr/wiki/FAQ#How_do_I_provide_my_own_dictionary |
| + | == Short term goals == |
| + | * Use the right standard dict for each language |
| + | * Integrate custom lists from Global Ingredients Taxonomy |
| + | **Create a golden set |
| + | *** e.g. someproduct.jpg -> ingredients image |
| + | *** someproduct.golden -> ingredients text |
| + | *** then we create a script that runs the OCR through the images, compare with the golden text, and report some accuracy measures |
| + | *** Draft Script: https://lite6.framapad.org/p/OFF_OCR_Script |
| + | * 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 |
| + | ** USDA UNII list of ingredients (will also work for Open Beauty Facts) |
| + | * 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… |
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− | Targets: | + | == Targets == |
− | * Logos (standardized) | + | * Logos of brands (Getting them from POD ?) |
− | * Text | + | * Logos of Labels (standardized) |
| + | * 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) |
| + | ** 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. |
− | | + | * 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. | | 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. |
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| [[Category:Project]] | | [[Category:Project]] |
| + | [[Category:Product Opener]] |