# Offline Writer Identification Using Convolutional Neural Network Activation Features

arXiv: https://arxiv.org/abs/2402.17029 · HTML: https://mlsanity.com/paper/2402.17029/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 31 references that are in the map.
- First posted on arXiv 26 Feb 2024.

## Builds on (most cited first, 1 of 1)

- [Multi-scale Orderless Pooling of Deep Convolutional Activation Features](https://mlsanity.com/paper/1403.1840/) (2014)

## Built on by (most cited first, 5 of 5)

- [An Interpretable Deep Learning Approach for Morphological Script Type Analysis](https://mlsanity.com/paper/2408.11150/) (2024)
- [Attention based End to end network for Offline Writer Identification on Word level data](https://mlsanity.com/paper/2404.07602/) (2024)
- [Towards the Influence of Text Quantity on Writer Retrieval](https://mlsanity.com/paper/2506.07566/) (2025)
- [Contrastive Masked Autoencoders for Character-Level Open-Set Writer Identification](https://mlsanity.com/paper/2501.11895/) (2025)
- [TextileNet: Towards Zero-shot Text-style Segmentation of Manuscripts](https://mlsanity.com/paper/2607.09299/) (2026)

## Abstract (arXiv)

Convolutional neural networks (CNNs) have recently become the state-of-the-art tool for large-scale image classification. In this work we propose the use of activation features from CNNs as local descriptors for writer identification. A global descriptor is then formed by means of GMM supervector encoding, which is further improved by normalization with the KL-Kernel. We evaluate our method on two publicly available datasets: the ICDAR 2013 benchmark database and the CVL dataset. While we perform comparably to the state of the art on CVL, our proposed method yields about 0.21 absolute improvement in terms of mAP on the challenging bilingual ICDAR dataset.