TY - GEN
T1 - Alignment of Historical Handwritten Manuscripts Using Siamese Neural Network
AU - Kassis, Majeed
AU - Nassour, Jumana
AU - El-Sana, Jihad
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - Historical manuscript alignment is a widely known problem in historical document analysis, and the attempt of finding the differences between manuscript editions is mainly done by hand. Today, most of the computational tools coming to assist the historians are based on word recognition or spotting. These solutions are partial at best. In this paper, we present a Siamese neural network based system, which automatically identifies whether a pair of images contain the same text without the need of recognizing the text. The user is required to annotate several pages of two manuscripts, and with the assistance of synthetically generated data and affine distortions we can align two manuscripts written by different writers, achieving strong results.
AB - Historical manuscript alignment is a widely known problem in historical document analysis, and the attempt of finding the differences between manuscript editions is mainly done by hand. Today, most of the computational tools coming to assist the historians are based on word recognition or spotting. These solutions are partial at best. In this paper, we present a Siamese neural network based system, which automatically identifies whether a pair of images contain the same text without the need of recognizing the text. The user is required to annotate several pages of two manuscripts, and with the assistance of synthetically generated data and affine distortions we can align two manuscripts written by different writers, achieving strong results.
KW - Deep learning
KW - Manuscript alignment
KW - Siamese network
UR - https://www.scopus.com/pages/publications/85045218023
U2 - 10.1109/ICDAR.2017.56
DO - 10.1109/ICDAR.2017.56
M3 - Conference contribution
AN - SCOPUS:85045218023
T3 - Proceedings of the International Conference on Document Analysis and Recognition, ICDAR
SP - 293
EP - 298
BT - Proceedings - 14th IAPR International Conference on Document Analysis and Recognition, ICDAR 2017
PB - Institute of Electrical and Electronics Engineers
T2 - 14th IAPR International Conference on Document Analysis and Recognition, ICDAR 2017
Y2 - 9 November 2017 through 15 November 2017
ER -