@inproceedings{ff0b4832d255418d9df258c2a0e79575,
title = "Binarization Free Layout Analysis for Arabic Historical Documents Using Fully Convolutional Networks",
abstract = "We present a Fully Convolutional Network based method for layout analysis of non-binarized historical Arabic manuscripts. The document image is segmented into main text and side text regions by dense pixel prediction. Convolutional part of the network can learn useful features from the non-binarized document images and is robust to degradation and uncontrained layouts. We have evaluated the proposed method on a private dataset containing challenging historical Arabic manuscripts to demonstrate it effectiveness.",
author = "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 = "https://doi.org/10.1109/ASAR.2018.8480333",
language = "الإنجليزيّة",
series = "2nd IEEE International Workshop on Arabic and Derived Script Analysis and Recognition, ASAR 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "151--155",
booktitle = "2nd IEEE International Workshop on Arabic and Derived Script Analysis and Recognition, ASAR 2018",
address = "الولايات المتّحدة",
}