@inproceedings{9fa4836fbc184d509800a225f31e261a,
title = "Writer identification for historical arabic documents",
abstract = "Identification of writers of handwritten historical documents is an important and challenging task. In this paper we present several feature extraction and classification approaches for the identification of writers in historical Arabic manuscripts. The approaches are able to successfully identify writers of multipage documents. The feature extraction methods rely on different principles, such as contour-, textural- and key point-based and the classification schemes are based on averaging and voting. For all experiments a dedicated data set based on a publicly available database is used. The experiments show promising results and the best performance was achieved using a novel feature extraction based on key point descriptors.",
author = "Daniel Fecker and Abedelkadir Asit and Volker M{\"a}rgner and Jihad El-Sana and Tim Fingscheidt",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 22nd International Conference on Pattern Recognition, ICPR 2014 ; Conference date: 24-08-2014 Through 28-08-2014",
year = "2014",
month = dec,
day = "4",
doi = "10.1109/ICPR.2014.526",
language = "English",
series = "Proceedings - International Conference on Pattern Recognition",
publisher = "Institute of Electrical and Electronics Engineers",
pages = "3050--3055",
booktitle = "2014 22nd International Conference on Pattern Recognition",
address = "United States",
}