# Offline Writer Identification based on the Path Signature Feature

arXiv: https://arxiv.org/abs/1905.01207 · HTML: https://mlsanity.com/paper/1905.01207/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 May 2019.

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

- [A Survey of Historical Document Image Datasets](https://mlsanity.com/paper/2203.08504/) (2022)
- [GR-RNN: Global-Context Residual Recurrent Neural Networks for Writer Identification](https://mlsanity.com/paper/2104.05036/) (2021)
- [Letter-level Online Writer Identification](https://mlsanity.com/paper/2112.02824/) (2021)

## Abstract (arXiv)

In this paper, we propose a novel set of features for offline writer identification based on the path signature approach, which provides a principled way to express information contained in a path. By extracting local pathlets from handwriting contours, the path signature can also characterize the offline handwriting style. A codebook method based on the log path signature---a more compact way to express the path signature---is used in this work and shows competitive results on several benchmark offline writer identification datasets, namely the IAM, Firemaker, CVL and ICDAR2013 writer identification contest dataset.