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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. p. 1-5.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Harvard

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

APA

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

Vancouver

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