# Feature Mixing for Writer Retrieval and Identification on Papyri Fragments

arXiv: https://arxiv.org/abs/2306.12939 · HTML: https://mlsanity.com/paper/2306.12939/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 17 references that are in the map.
- First posted on arXiv 22 Jun 2023.

## Builds on (most cited first, 7 of 7)

- [MetaFormer Is Actually What You Need for Vision](https://mlsanity.com/paper/2111.11418/) (2021)
- [MetaFormer Baselines for Vision](https://mlsanity.com/paper/2210.13452/) (2022)
- [Unsupervised Feature Learning for Writer Identification and Writer Retrieval](https://mlsanity.com/paper/1705.09369/) (2017)
- [ICDAR 2019 Competition on Image Retrieval for Historical Handwritten Documents](https://mlsanity.com/paper/1912.03713/) (2019)
- [TorMentor: Deterministic dynamic-path, data augmentations with fractals](https://mlsanity.com/paper/2204.03776/) (2022)
- [ICFHR 2020 Competition on Image Retrieval for Historical Handwritten Fragments](https://mlsanity.com/paper/2010.10197/) (2020)
- [Writer Retrieval and Writer Identification in Greek Papyri](https://mlsanity.com/paper/2212.07664/) (2022)

## Built on by (most cited first, 3 of 3)

- [SAGHOG: Self-Supervised Autoencoder for Generating HOG Features for Writer Retrieval](https://mlsanity.com/paper/2404.17221/) (2024)
- [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)

## Abstract (arXiv)

This paper proposes a deep-learning-based approach to writer retrieval and identification for papyri, with a focus on identifying fragments associated with a specific writer and those corresponding to the same image. We present a novel neural network architecture that combines a residual backbone with a feature mixing stage to improve retrieval performance, and the final descriptor is derived from a projection layer. The methodology is evaluated on two benchmarks: PapyRow, where we achieve a mAP of 26.6 % and 24.9 % on writer and page retrieval, and HisFragIR20, showing state-of-the-art performance (44.0 % and 29.3 % mAP). Furthermore, our network has an accuracy of 28.7 % for writer identification. Additionally, we conduct experiments on the influence of two binarization techniques on fragments and show that binarizing does not enhance performance. Our code and models are available to the community.