# Writer Retrieval and Writer Identification in Greek Papyri

arXiv: https://arxiv.org/abs/2212.07664 · HTML: https://mlsanity.com/paper/2212.07664/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 34 references that are in the map.
- First posted on arXiv 15 Dec 2022.

## Builds on (most cited first, 6 of 6)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Unsupervised Feature Learning for Writer Identification and Writer Retrieval](https://mlsanity.com/paper/1705.09369/) (2017)
- [Encoding CNN Activations for Writer Recognition](https://mlsanity.com/paper/1712.07923/) (2017)
- [TorMentor: Deterministic dynamic-path, data augmentations with fractals](https://mlsanity.com/paper/2204.03776/) (2022)
- [Artificial intelligence based writer identification generates new evidence for the unknown scribes of the Dead Sea Scrolls exemplified by the Great Isaiah Scroll (1QIsaa)](https://mlsanity.com/paper/2010.14476/) (2020)
- [Deep Generalized Max Pooling](https://mlsanity.com/paper/1908.05040/) (2019)

## Built on by (most cited first, 6 of 6)

- [SAGHOG: Self-Supervised Autoencoder for Generating HOG Features for Writer Retrieval](https://mlsanity.com/paper/2404.17221/) (2024)
- [Feature Mixing for Writer Retrieval and Identification on Papyri Fragments](https://mlsanity.com/paper/2306.12939/) (2023)
- [KaiRacters: Character-level-based Writer Retrieval for Greek Papyri](https://mlsanity.com/paper/2407.07536/) (2024)
- [NeuroPapyri: A Deep Attention Embedding Network for Handwritten Papyri Retrieval](https://mlsanity.com/paper/2408.07785/) (2024)
- [Assessing the impact of Binarization for Writer Identification in Greek Papyrus](https://mlsanity.com/paper/2506.15852/) (2025)
- [Beyond the Pipeline: Analyzing Key Factors in End-to-End Deep Learning for Historical Writer Identification](https://mlsanity.com/paper/2510.18671/) (2025)

## Abstract (arXiv)

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 While automatic writer identification/retrieval methods already provide promising results for many historical document types, papyri data is very challenging due to the fiber structures and severe artifacts.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.