Offline Text-Independent Arabic and Chinese Writer Identification Using a Multi-Segmentation Codebook-Based Strategy

Mohamed Abdi, Mohamed Abdi, Maher Khemakhem

2024

Abstract

Many approaches rely on segmentation for offline text-independent writer identification. Segmentation schemes based on contours, junctions and projections are widely used and are very effective with Latin alphabet handwriting. However, these schemes seem to be less consistent in capturing writer individuality with Arabic and Chinese. As writing systems, the latter languages are morphologically different and are considered more complex than Latin alphabet languages. In this paper, four different segmentation techniques are tested for the identification of Arabic and Chinese writers. Then, these techniques are combined to increase the accuracy of identification. Experiments were realized on handwriting samples by 300 writers from Arabic IFN/ENIT dataset and 300 writers from Chinese HIT-MW dataset. An additional 300 writers from English/German CVL dataset were used as a control group. Taken separately, these segmentation techniques that gave good results with CVL (Top1% = 99.00%) were not as conclusive with IFN/ENIT and HIT-MW. Nevertheless, the use of different types of segmentation in combination proved to be highly efficient for Arabic and Chinese with Top1% = 96.33% and Top1% = 91.33%, respectively.

Paper Citation

in Harvard Style

Abdi M. and Khemakhem M. (2024). Offline Text-Independent Arabic and Chinese Writer Identification Using a Multi-Segmentation Codebook-Based Strategy. In Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM; ISBN 978-989-758-684-2, SciTePress, pages 613-619. DOI: 10.5220/0012297600003654

in Bibtex Style

@conference{icpram24,
author={Mohamed Abdi and Maher Khemakhem},
title={Offline Text-Independent Arabic and Chinese Writer Identification Using a Multi-Segmentation Codebook-Based Strategy},
booktitle={Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM},
year={2024},
pages={613-619},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012297600003654},
isbn={978-989-758-684-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM
TI - Offline Text-Independent Arabic and Chinese Writer Identification Using a Multi-Segmentation Codebook-Based Strategy
SN - 978-989-758-684-2
AU - Abdi M.
AU - Khemakhem M.
PY - 2024
SP - 613
EP - 619
DO - 10.5220/0012297600003654
PB - SciTePress