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Authors: Elmokhtar Mohamed Moussa 1 ; 2 ; Thibault Lelore 1 and Harold Mouchère 2

Affiliations: 1 MyScript SAS, Nantes, France ; 2 Nantes Université, Ecole Centrale Nantes, CNRS, LS2N, UMR 6004, F-44000 Nantes, France

Keyword(s): Handwriting Matching, DTW Metric, Sampling Invariance.

Abstract: In this paper, we propose DTWseg, a modified DTW algorithm based on a point-to-segment distance instead of the euclidean point-to-point distance. Applying DTWseg to online handwriting matching proves to be advantageous compared to other algorithms as it is less sensitive to differences between signals sampling rates occurring due to acquisition frequencies or handwriting speed. It eliminates the need for a commonly practiced resampling that omits an important dynamic part of the ductus. Experiments on IRONOFF french words dataset and FLOWCHARTS dataset show DTWseg to be least impacted by sampling rate alterations. We also propose a new benchmark of state-of-the-art methods on offline handwriting to online conversion based on our new proposed metric.

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Paper citation in several formats:
Mohamed Moussa, E.; Lelore, T. and Mouchère, H. (2023). Point to Segment Distance DTW for Online Handwriting Signals Matching. In Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-626-2; ISSN 2184-4313, SciTePress, pages 850-855. DOI: 10.5220/0011672600003411

@conference{icpram23,
author={Elmokhtar {Mohamed Moussa}. and Thibault Lelore. and Harold Mouchère.},
title={Point to Segment Distance DTW for Online Handwriting Signals Matching},
booktitle={Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2023},
pages={850-855},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011672600003411},
isbn={978-989-758-626-2},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Point to Segment Distance DTW for Online Handwriting Signals Matching
SN - 978-989-758-626-2
IS - 2184-4313
AU - Mohamed Moussa, E.
AU - Lelore, T.
AU - Mouchère, H.
PY - 2023
SP - 850
EP - 855
DO - 10.5220/0011672600003411
PB - SciTePress