VML-HP: Hebrew Paleography Dataset

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

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

1 Scopus citations


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.

Original languageEnglish
Title of host publicationDocument Analysis and Recognition - ICDAR 2021 - 16th International Conference, Proceedings
EditorsJosep Lladós, Daniel Lopresti, Seiichi Uchida
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages16
ISBN (Print)9783030863364
StatePublished - 1 Jan 2021
Event16th International Conference on Document Analysis and Recognition, ICDAR 2021 - Lausanne, Switzerland
Duration: 5 Sep 202110 Sep 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12824 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference16th International Conference on Document Analysis and Recognition, ICDAR 2021


  • Convolutional neural network
  • Handwritten style analysis
  • Hebrew medieval manuscripts
  • Learning-based classification
  • Paleography
  • Script type classification

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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