OCR/Roadmap: Difference between revisions
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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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Revision as of 16:54, 2 May 2015
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:
- Google Drive OCR or Google Goggles
- Ocropus
- OpenCV
- Moodstocks
Targets:
- Logos (standardized)
- Text
- Standardized layouts (US Nutrition labels)
- Standardized text (quantities, EU Packaging codes)
- Barcodes (extraction in uploaded images)
- Image orientation: check that the text is properly oriented to guess if the image is properly oriented.
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.