@inproceedings{8a4e9d5031c249caae733848ed0580b7,
title = "Synthesizing versus Augmentation for Arabic Word Recognition with Convolutional Neural Networks",
abstract = "In this paper, we present a sub-word recognition method for historical Arabic manuscripts, using convolutional neural networks. We investigate the benefit of extending training set with synthetically created samples in comparison to augmentation. We show that annotating around ten pages of a manuscript and extending it, is sufficient for successful sub-word recognition in the whole manuscript. In addition, we show the contribution of using different combinations of training sets and compare their sub-word recognition performance in the whole manuscript.",
keywords = "Arabic, Database, handwritten, text recognition",
author = "Reem Alaasam and Barakat, {Berat Kurar} and Jihad El-Sana",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2nd IEEE International Workshop on Arabic and Derived Script Analysis and Recognition, ASAR 2018 ; Conference date: 12-03-2018 Through 14-03-2018",
year = "2018",
month = oct,
day = "2",
doi = "10.1109/ASAR.2018.8480189",
language = "English",
series = "2nd IEEE International Workshop on Arabic and Derived Script Analysis and Recognition, ASAR 2018",
publisher = "Institute of Electrical and Electronics Engineers",
pages = "114--118",
booktitle = "2nd IEEE International Workshop on Arabic and Derived Script Analysis and Recognition, ASAR 2018",
address = "United States",
}