  This competition succeeds upon a line of competitions for writer and style
analysis of historical document images. In particular, we investigate the
performance of large-scale retrieval of historical document fragments in terms
of style and writer identification. The analysis of historic fragments is a
difficult challenge commonly solved by trained humanists. In comparison to
previous competitions, we make the results more meaningful by addressing the
issue of sample granularity and moving from writer to page fragment retrieval.
The two approaches, style and author identification, provide information on
what kind of information each method makes better use of and indirectly
contribute to the interpretability of the participating method. Therefore, we
created a large dataset consisting of more than 120 000 fragments. Although the
most teams submitted methods based on convolutional neural networks, the
winning entry achieves an mAP below 40%.
