  The analysis of digitized historical manuscripts is typically addressed by
paleographic experts. Writer identification refers to the classification of
known writers while writer retrieval seeks to find the writer by means of image
similarity in a dataset of images. While automatic writer
identification/retrieval methods already provide promising results for many
historical document types, papyri data is very challenging due to the fiber
structures and severe artifacts. Thus, an important step for an improved writer
identification is the preprocessing and feature sampling process. We
investigate several methods and show that a good binarization is key to an
improved writer identification in papyri writings. We focus mainly on writer
retrieval using unsupervised feature methods based on traditional or
self-supervised-based methods. It is, however, also comparable to the state of
the art supervised deep learning-based method in the case of writer
classification/re-identification.
