TY - GEN
T1 - VML-HP
T2 - 16th International Conference on Document Analysis and Recognition, ICDAR 2021
AU - Droby, Ahmad
AU - Kurar Barakat, Berat
AU - Vasyutinsky Shapira, Daria
AU - Rabaev, Irina
AU - El-Sana, Jihad
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021/1/1
Y1 - 2021/1/1
N2 - This paper presents a public dataset, VML-HP, for Hebrew paleography analysis. The VML-HP dataset consists of 537 document page images with labels of 15 script sub-types. Ground truth is manually created by a Hebrew paleographer at a page level. In addition, we propose a patch generation tool for extracting patches that contain an approximately equal number of text lines no matter the variety of font sizes. The VML-HP dataset contains a train set and two test sets. The first is a typical test set, and the second is a blind test set for evaluating algorithms in a more challenging setting. We have evaluated several deep learning classifiers on both of the test sets. The results show that convolutional networks can classify Hebrew script sub-types on a typical test set with accuracy much higher than the accuracy on the blind test.
AB - This paper presents a public dataset, VML-HP, for Hebrew paleography analysis. The VML-HP dataset consists of 537 document page images with labels of 15 script sub-types. Ground truth is manually created by a Hebrew paleographer at a page level. In addition, we propose a patch generation tool for extracting patches that contain an approximately equal number of text lines no matter the variety of font sizes. The VML-HP dataset contains a train set and two test sets. The first is a typical test set, and the second is a blind test set for evaluating algorithms in a more challenging setting. We have evaluated several deep learning classifiers on both of the test sets. The results show that convolutional networks can classify Hebrew script sub-types on a typical test set with accuracy much higher than the accuracy on the blind test.
KW - Convolutional neural network
KW - Handwritten style analysis
KW - Hebrew medieval manuscripts
KW - Learning-based classification
KW - Paleography
KW - Script type classification
UR - https://www.scopus.com/pages/publications/85115333935
U2 - 10.1007/978-3-030-86337-1_14
DO - 10.1007/978-3-030-86337-1_14
M3 - Conference contribution
AN - SCOPUS:85115333935
SN - 9783030863364
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 205
EP - 220
BT - Document Analysis and Recognition - ICDAR 2021 - 16th International Conference, Proceedings
A2 - Lladós, Josep
A2 - Lopresti, Daniel
A2 - Uchida, Seiichi
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 5 September 2021 through 10 September 2021
ER -