Fine-Grained Product Classification on Leaflet Advertisements
Daniel Ladwig Bianca Lamm Janis Keuper
In this paper, we describe a first publicly available fine-grained product
recognition dataset based on leaflet images. Using advertisement leaflets,
collected over several years from different European retailers, we provide a
total of 41.6k manually annotated product images in 832 classes. Further, we
investigate three different approaches for this fine-grained product
classification task, Classification by Image, by Text, as well as by Image and
Text. The approach "Classification by Text" uses the text extracted directly
from the leaflet product images. We show, that the combination of image and
text as input improves the classification of visual difficult to distinguish
products. The final model leads to an accuracy of 96.4% with a Top-3 score of
99.2%. We release our code at
https://github.com/ladwigd/Leaflet-Product-Classification.