Comments (3)
devingfx
commented on May 26, 2024
I developped a draft of bills data extractor from PDF > detect header footer infos (like store, address, date, SIRET, ect) > parse table rows to CSV.
PDF file are generated right now by an external app TextFairy that uses Tesseract to extract text and positioning.
I found OpenFoodFacts searching a way to get products infos from "partial general name" + store
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teolemon
commented on May 26, 2024
@devingfx We had made this prototype during a hackathon: https://github.com/openreceipts/openreceipts-server
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raphael0202
commented on May 26, 2024
@devingfx I'm not sure whether you're still interested in the subject, but you've launche Open Prices (https://prices.openfoodfacts.org), a crowdsourced database of prices of food products in the world.
Having ML to extract automatically data from receipts/price tags would help tremendously.
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Related Issues (20)
- Use LLMs to find subcategories for taxonomies
- Use LLMs to create data models for Open Products Facts categories
- Use LLMs to spellcheck/lowercase ingredient lists HOT 1
- Use LLMs to retrieve properties on ingredients/additives
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- Use LLMs in Odoo Connect
- Use LLMs in Odoo Connect
- Use LLMs to summarise long texts about Quality Schemes
- Use LLMs to parse a natural language complex meal into foods with nutritional values for nutrition/environment tracking
- Use LLMs to retrieve food wine pairings
- Recognize Nutrition tables using multimodal models (GPT4) HOT 4
- Turbocharge the mobile app using LLMs and Multimodal models
- Use LLMs to extract ingredient lists from raw text HOT 3
- Improve image resolution
- Predict an image crop of nutrition tables HOT 1
- Use LLMs to detect origin of ingredients
- Extract nutrients data from US products nutrients table photos
- Use large language models (LLM) to perform ingredient list spellcheck
- Use a multimodal LLM to extract the likely Product name and brand as JSON
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