Offline Writer Identification based on the Path Signature Feature
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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.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 3 May 2019.
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Citing papers per year: 2021: 2; 2022: 1.
3 papers.
- 2022A Survey of Historical Document Image Datasetscited by 20
- 2021GR-RNN: Global-Context Residual Recurrent Neural Networks for Writer Identificationcited by 11
- 2021Letter-level Online Writer Identificationcited by 3
- 2022A Survey of Historical Document Image Datasets16 Mar 2022
- 2021Letter-level Online Writer Identification6 Dec 2021
- 2021GR-RNN: Global-Context Residual Recurrent Neural Networks for Writer Identification11 Apr 2021