  This competition investigates the performance of large-scale retrieval of
historical document images based on writing style. Based on large image data
sets provided by cultural heritage institutions and digital libraries,
providing a total of 20 000 document images representing about 10 000 writers,
divided in three types: writers of (i) manuscript books, (ii) letters, (iii)
charters and legal documents. We focus on the task of automatic image retrieval
to simulate common scenarios of humanities research, such as writer retrieval.
The most teams submitted traditional methods not using deep learning
techniques. The competition results show that a combination of methods is
outperforming single methods. Furthermore, letters are much more difficult to
retrieve than manuscripts.
