CNN models with attention reach 99.05% top-1 accuracy on line-level splits and 78.61% on page-disjoint splits for writer identification after expanding the labeled portion of the Muharaf historical Arabic manuscript dataset.
In: 2019 IEEE/CVF Interna- tional Conference on Computer Vision (ICCV) (2019), pp
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Different Strokes for Different Folks: Writer Identification for Historical Arabic Manuscripts
CNN models with attention reach 99.05% top-1 accuracy on line-level splits and 78.61% on page-disjoint splits for writer identification after expanding the labeled portion of the Muharaf historical Arabic manuscript dataset.