TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Ladwig, Daniel
A1  - Lamm, Bianca
A1  - Keuper, Janis
T1  - Fine-Grained Product Classification on Leaflet Advertisements
N2  - 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.
KW  - Deep Leaning
Y1  - 2023
AX  - 2305.03706
SP  - 1
EP  - 5
ER  -