Abstract
Paleography studies the writing styles of manuscripts and recognizes different styles and modes of scripts. We explore the applicability of hard and soft-labeling for training deep-learning models to classify Hebrew scripts. In contrast to the hard-labeling scheme, where each document image has one label representing its class, the soft-labeling approach labels an image by a label vector. Each element of the vector is the similarity of the document image to a certain regional writing style or graphical mode. In addition, we introduce a dataset of medieval Hebrew manuscripts that provides complete coverage of major Hebrew writing styles and modes. A Hebrew paleography expert manually annotated the ground truth for soft-labeling. We compare the applicability of soft and hard-labeling approaches on the presented dataset, analyze, and discuss the findings.
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Acknowledgment
This research was partially supported by The Frankel Center for Computer Science at Ben-Gurion University.
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Droby, A., Shapira, D.V., Rabaev, I., Barakat, B.K., El-Sana, J. (2022). Hard and Soft Labeling for Hebrew Paleography: A Case Study. In: Uchida, S., Barney, E., Eglin, V. (eds) Document Analysis Systems. DAS 2022. Lecture Notes in Computer Science, vol 13237. Springer, Cham. https://doi.org/10.1007/978-3-031-06555-2_33
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