Abstract
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.
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Acknowledgment
This research was partially supported by The Frankel Center for Computer Science at Ben-Gurion University. The participation of Dr. Vasyutinsky Shapira in this project is funded by Israeli Ministery of Science, Technology and Space, Yuval Ne’eman scholarship n. 3-16784.
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Droby, A., Kurar Barakat, B., Vasyutinsky Shapira, D., Rabaev, I., El-Sana, J. (2021). VML-HP: Hebrew Paleography Dataset. In: Lladós, J., Lopresti, D., Uchida, S. (eds) Document Analysis and Recognition – ICDAR 2021. ICDAR 2021. Lecture Notes in Computer Science(), vol 12824. Springer, Cham. https://doi.org/10.1007/978-3-030-86337-1_14
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