Hard and Soft Labeling for Hebrew Paleography: A Case Study

Ahmad Droby, Daria Vasyutinsky Shapira, Irina Rabaev, Berat Kurar Barakat, Jihad El-Sana

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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.

Original languageEnglish
Title of host publicationDocument Analysis Systems - 15th IAPR International Workshop, DAS 2022, Proceedings
EditorsSeiichi Uchida, Elisa Barney, Véronique Eglin
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages15
ISBN (Print)9783031065545
StatePublished - 1 Jan 2022
Event15th IAPR International Workshop on Document Analysis Systems, DAS 2022 - La Rochelle, France
Duration: 22 May 202225 May 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13237 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference15th IAPR International Workshop on Document Analysis Systems, DAS 2022
CityLa Rochelle


  • Convolutional neural network
  • Digital paleography
  • Medieval Hebrew manuscripts
  • Script type classification
  • Soft-labeling

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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