Technology Category
- Analytics & Modeling - Machine Learning
- Application Infrastructure & Middleware - Event-Driven Application
Applicable Industries
- Food & Beverage
- Healthcare & Hospitals
Use Cases
- Onsite Human Safety Management
- Tamper Detection
Services
- System Integration
About The Customer
Yuka is a mobile application that allows users to scan foods and cosmetics to get the product’s associated health impact. The application uses OCR and machine learning to extract data from uploaded images of the ingredients list and, in the case of food products, the nutritional table. This information is then used to calculate a health score for a given product. Yuka's database is extensive, containing over 4 million products, and is growing rapidly with approximately 1,200 new products added daily. The company aims to provide the health impact of new products in real-time, or within 2-3 hours of a product being added to the database.
The Challenge
Yuka, a mobile application that provides health impact information for food products and cosmetics, faced a significant challenge in managing its rapidly growing database. The database, which already contained over 4 million products, was expanding at a rate of approximately 1,200 new products daily. Yuka's small team was unable to manually review each new product added to the platform, a process that often required multiple transcription tasks. The application initially used OCR to scan product images for nutritional information and ingredients, but this process was not always accurate. OCR struggled with images featuring inconsistent lighting, obstructions, or irregular text surfaces. As a result, about 60% of the images submitted to Yuka needed to be outsourced to a human annotator. This was a daunting task for Yuka's small team, especially considering their goal to provide a product's health score within 2-3 hours of its addition to the database.
The Solution
Yuka turned to Scale Rapid to overcome the challenge of quickly and accurately transcribing product information. When OCR failed to achieve a sufficient detection rate on an image, Yuka sent the image to Scale Rapid for manual transcription by a human annotator. Yuka typically sent hundreds or thousands of these images to Scale Rapid each day. Once the transcribed data was returned, Yuka compared the text against their existing dictionary of known ingredients and nutritional information. If the error rate was low enough, the product was integrated into their database and a health rating was calculated. Scale Rapid's ability to handle massive amounts of data within a short period of time was crucial for Yuka. The service consistently provided accurate transcriptions within 2-3 hours of each request, in multiple languages including English, French, German, Italian, and Spanish.
Operational Impact
Quantitative Benefit
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