# FragNet: Writer Identification using Deep Fragment Networks

arXiv: https://arxiv.org/abs/2003.07212 · HTML: https://mlsanity.com/paper/2003.07212/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2020.

## Built on by (most cited first, 18 of 18)

- [Global-Local Transformer for Brain Age Estimation](https://mlsanity.com/paper/2109.01663/) (2021)
- [MetaHTR: Towards Writer-Adaptive Handwritten Text Recognition](https://mlsanity.com/paper/2104.01876/) (2021)
- [GR-RNN: Global-Context Residual Recurrent Neural Networks for Writer Identification](https://mlsanity.com/paper/2104.05036/) (2021)
- [Evaluating Synthetic Pre-Training for Handwriting Processing Tasks](https://mlsanity.com/paper/2304.01842/) (2023)
- [Exploiting Multi-Scale Fusion, Spatial Attention and Patch Interaction Techniques for Text-Independent Writer Identification](https://mlsanity.com/paper/2111.10605/) (2021)
- [Image-based Freeform Handwriting Authentication with Energy-oriented Self-Supervised Learning](https://mlsanity.com/paper/2408.09676/) (2024)
- [Siamese based Neural Network for Offline Writer Identification on word level data](https://mlsanity.com/paper/2211.14443/) (2022)
- [CSSL-RHA: Contrastive Self-Supervised Learning for Robust Handwriting Authentication](https://mlsanity.com/paper/2307.11100/) (2023)
- [Writer adaptation for offline text recognition: An exploration of neural network-based methods](https://mlsanity.com/paper/2307.15071/) (2023)
- [Offline Text-Independent Writer Identification based on word level data](https://mlsanity.com/paper/2202.10207/) (2022)
- [Innovative Methods for Non-Destructive Inspection of Handwritten Documents](https://mlsanity.com/paper/2310.11217/) (2023)
- [A Novel Dataset for Non-Destructive Inspection of Handwritten Documents](https://mlsanity.com/paper/2401.04448/) (2024)
- [Attention based End to end network for Offline Writer Identification on Word level data](https://mlsanity.com/paper/2404.07602/) (2024)
- [Decorrelation-based Self-Supervised Visual Representation Learning for Writer Identification](https://mlsanity.com/paper/2410.01441/) (2024)
- [Global Regular Network for Writer Identification](https://mlsanity.com/paper/2201.05951/) (2022)
- [Recognizing Handwriting Styles in a Historical Scanned Document Using Unsupervised Fuzzy Clustering](https://mlsanity.com/paper/2210.16780/) (2022)
- [CTP-Net: Character Texture Perception Network for Document Image Forgery Localization](https://mlsanity.com/paper/2308.02158/) (2023)
- [Contrastive Masked Autoencoders for Character-Level Open-Set Writer Identification](https://mlsanity.com/paper/2501.11895/) (2025)

## Abstract (arXiv)

Writer identification based on a small amount of text is a challenging problem. In this paper, we propose a new benchmark study for writer identification based on word or text block images which approximately contain one word. In order to extract powerful features on these word images, a deep neural network, named FragNet, is proposed. The FragNet has two pathways: feature pyramid which is used to extract feature maps and fragment pathway which is trained to predict the writer identity based on fragments extracted from the input image and the feature maps on the feature pyramid. We conduct experiments on four benchmark datasets, which show that our proposed method can generate efficient and robust deep representations for writer identification based on both word and page images.