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Assistive solutions for a better shopping experience can improve the qualityof life of people, in particular also of visually impaired shoppers. We presenta system that visually recognizes the fine-grained product classes of items ona shopping list, in shelves images taken with a smartphone in a grocery store.Our system consists of three components: (a) We automatically recognize usefultext on product packaging, e.g., product name and brand, and build a mapping ofwords to product classes based on the large-scale GroceryProducts dataset. Whenthe user populates the shopping list, we automatically infer the product classof each entered word. (b) We perform fine-grained product class recognitionwhen the user is facing a shelf. We discover discriminative patches on productpackaging to differentiate between visually similar product classes and toincrease the robustness against continuous changes in product design. (c) Wecontinuously improve the recognition accuracy through active learning. Ourexperiments show the robustness of the proposed method against cross-domainchallenges, and the scalability to an increasing number of products withminimal re-training.

Fine-Grained Product Class Recognition for Assisted Shopping