Product Label and Text Extraction System (PLATES)
Description
Manual food label data extraction causes slow data accessibility and insights. Manual data processing and standardization causes slow data accessibility and insights. Decentralized food label data limits research and regulatory processing of industry compliance and health impacts.
Detailed example
The system includes a user interface that allows users to upload food product images to receive extracted, standardized (utilizing FoodTrak standards), and metadata attached, structured data for 30+ key food data elements that can be reviewed and saved , exported, or published to downstream databases.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
Expected benefit
Reduced burden to HFP reviewers and data scientists reviewing and analyzing food product label data, including ingredient and nutrition research. The capabilities have significantly accelerated the data extraction and entry process, providing standardized and parsed structured data 35.29x faster than the manual process (reducing the manual burden by 97.08%).
Controls / human review
ATO: Yes; PIA: Not published
Data needed
Internal FDA FoodTrak and OLOAS data.