Abstract
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 While automatic writer identification/retrieval methods already provide promising results for many historical document types, papyri data is very challenging due to the fiber structures and severe artifacts.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.
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Notes
- 1.
“Writer” and “scribe” is used interchangeably throughout the paper.
- 2.
Meta-data on the images are available (reference, date, collection...) at https://d-scribes.philhist.unibas.ch/en/gkr-papyri/.
- 3.
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Acknowledgement
This work was partially supported by the Swiss National Science Foundation as part of the project no. PZ00P1-174149 “Reuniting fragments, identifying scribes and characterizing scripts: the Digital paleography of Greek and Coptic papyri (d-scribes)”. This research was supported by grants from NVIDIA and utilized NVIDIA Quadro RTX 6000.
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Christlein, V., Marthot-Santaniello, I., Mayr, M., Nicolaou, A., Seuret, M. (2022). Writer Retrieval and Writer Identification in Greek Papyri. In: Carmona-Duarte, C., Diaz, M., Ferrer, M.A., Morales, A. (eds) Intertwining Graphonomics with Human Movements. IGS 2022. Lecture Notes in Computer Science, vol 13424. Springer, Cham. https://doi.org/10.1007/978-3-031-19745-1_6
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