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Advisor(s)
Abstract(s)
When compared with traditional local shops where the customer has a personalised service,
in large retail departments, the client has to make his purchase decisions independently, mostly
supported by the information available in the package. Additionally, people are becoming more
aware of the importance of the food ingredients and demanding about the type of products they buy
and the information provided in the package, despite it often being hard to interpret. Big shops such
as supermarkets have also introduced important challenges for the retailer due to the large number
of different products in the store, heterogeneous affluence and the daily needs of item repositioning.
In this scenario, the automatic detection and recognition of products on the shelves or off the shelves
has gained increased interest as the application of these technologies may improve the shopping
experience through self-assisted shopping apps and autonomous shopping, or even benefit stock
management with real-time inventory, automatic shelf monitoring and product tracking. These
solutions can also have an important impact on customers with visual impairments. Despite recent
developments in computer vision, automatic grocery product recognition is still very challenging,
with most works focusing on the detection or recognition of a small number of products, often under
controlled conditions. This paper discusses the challenges related to this problem and presents a
review of proposed methods for retail product label processing, with a special focus on assisted
analysis for customer support, including for the visually impaired. Moreover, it details the public
datasets used in this topic and identifies their limitations, and discusses future research directions of
related fields.
Description
Keywords
retail; grocery products; computer vision; object detection; object recognition; text detection; text recognition; product label analysis