Abstract: A computing system and database analyses product images to determine product attributes and populate the database. Candidate product information is identified within an image of product packaging of a product. A model created by machine learning is applied to the candidate product information to discern indicators of product attributes from indicators of non-product attributes of the candidate product information. Individual indicators are extracted from the indicators of product attributes. In response to a determination that additional confidence is needed for a given individual indicator, a rule is applied to identify unique product information from the given individual indicator. A taxonomy is then applied to the product attributes based on representations of the individual indicators to generate categorized product attributes representing the product. The database is populated with representations of the categorized product attributes.