Authors:
Mohamed Abdi
1
;
2
and
Maher Khemakhem
3
Affiliations:
1
Mir@cl Laboratory, FSEG, University of Sfax, Tunisia
;
2
Institut Supérieur des Mathématiques Appliquées et de l’Informatique, ISMAI, University of Kairouan, Tunisia
;
3
Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
Keyword(s):
Writer Identification, Grapheme Codebook, Segmentation, K-Medoid Clustering, Feature Combination.
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.
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