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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. |
| | | |
− | Tools:
| + | == Product Opener improvements == |
− | * Google Drive OCR or Google Goggles | + | * Process all uploaded images using Tesseract and/or the New Cloud based engine |
− | * Ocropus | + | * Return JSON to mobile client and/or web client for suggestions to the user |
− | * OpenCV | + | * Add support to search into OCR results |
− | * Moodstocks
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| | | |
− | Targets: | + | == TODO == |
− | * Logos (standardized) | + | * Process Open Beauty Facts images |
− | * Text | + | * Process the Belgian Food Photographs |
| + | |
| + | == 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) |
| + | |
| + | |
| + | === 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) | | * Standardized layouts (US Nutrition labels) |
| + | ** Store in separate image for further reference |
| * Standardized text (quantities, EU Packaging codes) | | * Standardized text (quantities, EU Packaging codes) |
| + | ** Store in separate image for further reference |
| * 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.
| + | [[Category:Roadmap]] |
| + | [[Category:Project]] |
| + | [[Category:ProductOpener]] |
| + | [[Category:OCR]] |
| + | [[Category:Artificial Intelligence]] |