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.
@article{arxiv.1905.01207,
title = {Offline Writer Identification based on the Path Signature Feature},
author = {Songxuan Lai and Lianwen Jin},
journal= {arXiv preprint arXiv:1905.01207},
year = {2019}
}