Standard

Arabic manuscript author verification using deep convolutional networks. / Boiarov, Andrei; Senov, Alexander; Knysh, Alexander.

1st IEEE International Workshop on Arabic Script Analysis and Recognition, ASAR 2017. Institute of Electrical and Electronics Engineers Inc., 2017. стр. 1-5.

Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование

Harvard

Boiarov, A, Senov, A & Knysh, A 2017, Arabic manuscript author verification using deep convolutional networks. в 1st IEEE International Workshop on Arabic Script Analysis and Recognition, ASAR 2017. Institute of Electrical and Electronics Engineers Inc., стр. 1-5, 1st IEEE International Workshop on Arabic Script Analysis and Recognition, Франция, 3/04/17.

APA

Boiarov, A., Senov, A., & Knysh, A. (2017). Arabic manuscript author verification using deep convolutional networks. в 1st IEEE International Workshop on Arabic Script Analysis and Recognition, ASAR 2017 (стр. 1-5). Institute of Electrical and Electronics Engineers Inc..

Vancouver

Boiarov A, Senov A, Knysh A. Arabic manuscript author verification using deep convolutional networks. в 1st IEEE International Workshop on Arabic Script Analysis and Recognition, ASAR 2017. Institute of Electrical and Electronics Engineers Inc. 2017. стр. 1-5

Author

Boiarov, Andrei ; Senov, Alexander ; Knysh, Alexander. / Arabic manuscript author verification using deep convolutional networks. 1st IEEE International Workshop on Arabic Script Analysis and Recognition, ASAR 2017. Institute of Electrical and Electronics Engineers Inc., 2017. стр. 1-5

BibTeX

@inproceedings{5acd6e409a57439db99969138ddbb691,
title = "Arabic manuscript author verification using deep convolutional networks",
abstract = "In this paper, we propose an automatic method for manuscript author verification based on an analysis of consecutive patches extracted from an image. The classification algorithm uses a deep convolutional network with two types of patch extraction: one based on connected components and the other based on usage of a fixed-size sliding window. We apply this method to verify the authorship of the Arabic manuscript entitled al-Khitat attributed to the hand of the renowned medieval Arab historian al-Maqrizi. Using appropriately collected ground-truth labeled data for convolutional network training purpose, our method has demonstrated promising results when applied to previously unseen manuscripts.",
author = "Andrei Boiarov and Alexander Senov and Alexander Knysh",
year = "2017",
month = oct,
day = "13",
language = "English",
pages = "1--5",
booktitle = "1st IEEE International Workshop on Arabic Script Analysis and Recognition, ASAR 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",
note = "1st IEEE International Workshop on Arabic Script Analysis and Recognition ; Conference date: 03-04-2017 Through 05-04-2017",

}

RIS

TY - GEN

T1 - Arabic manuscript author verification using deep convolutional networks

AU - Boiarov, Andrei

AU - Senov, Alexander

AU - Knysh, Alexander

PY - 2017/10/13

Y1 - 2017/10/13

N2 - In this paper, we propose an automatic method for manuscript author verification based on an analysis of consecutive patches extracted from an image. The classification algorithm uses a deep convolutional network with two types of patch extraction: one based on connected components and the other based on usage of a fixed-size sliding window. We apply this method to verify the authorship of the Arabic manuscript entitled al-Khitat attributed to the hand of the renowned medieval Arab historian al-Maqrizi. Using appropriately collected ground-truth labeled data for convolutional network training purpose, our method has demonstrated promising results when applied to previously unseen manuscripts.

AB - In this paper, we propose an automatic method for manuscript author verification based on an analysis of consecutive patches extracted from an image. The classification algorithm uses a deep convolutional network with two types of patch extraction: one based on connected components and the other based on usage of a fixed-size sliding window. We apply this method to verify the authorship of the Arabic manuscript entitled al-Khitat attributed to the hand of the renowned medieval Arab historian al-Maqrizi. Using appropriately collected ground-truth labeled data for convolutional network training purpose, our method has demonstrated promising results when applied to previously unseen manuscripts.

UR - http://www.scopus.com/inward/record.url?scp=85059381743&partnerID=8YFLogxK

UR - https://www.semanticscholar.org/paper/Arabic-manuscript-author-verification-using-deep-Boiarov-Senov/dc92975f676b0ea8f7d0e477d50de5701d4a13dd

M3 - Conference contribution

AN - SCOPUS:85059381743

SP - 1

EP - 5

BT - 1st IEEE International Workshop on Arabic Script Analysis and Recognition, ASAR 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 1st IEEE International Workshop on Arabic Script Analysis and Recognition

Y2 - 3 April 2017 through 5 April 2017

ER -

ID: 103988774