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From Digitization and Images to Text and Content: Transkribus as a Case Study

Prebor, Gila·2023·Proceedings of the Association for Information Science and Technology
DOI 10.1002/pra2.958
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Abstract

This poster explores the potential of using technological tools, specifically the Transkribus platform, for the transcription of Hebrew manuscripts. The digitization of historical resources has made them accessible, but the textual content of the scanned images remains inaccessible. Transkribus, an AI‐powered platform, offers tools for text recognition, transcription, and search of historical documents. The poster discusses the process of automatic text recognition (ATR) and the challenges it faces, particularly in handling handwritten texts and Hebrew letters. It provides an overview of the Transkribus platform, its functionalities, and the training process for creating transcription models. The author presents a case study of transcribing a 15th‐century Sephardic semi‐cursive Hebrew manuscript using the Transkribus platform and evaluates the performance of different models. The poster concludes by discussing the implications and possibilities of using Transkribus for automatic transcription of historical Hebrew manuscripts. While the results show promising improvements in accuracy, further challenges and solutions are also discussed. Overall, Transkribus offers significant potential for the study and transcription of Hebrew manuscripts, revolutionizing the field of Jewish studies and historical research.

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Figures

Image 1
CC-BY-NC-ND · From the PDF
Figure 1 Example of transcription of the Sefer ha-Iqqarim manuscript, Paris, Bibliothèque nationale de France, MS hébreu 740, fol. 19r, using the DiJeSt model.
Figure 1 Example of transcription of the Sefer ha-Iqqarim manuscript, Paris, Bibliothèque nationale de France, MS hébreu 740, fol. 19r, using the DiJeSt model.CC-BY-NC-ND · From the PDF
Image 3
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Figure 3 Example of a transcription of BnF MS hébreu 740, fol. 15v, using the BnF 740 model.
Figure 3 Example of a transcription of BnF MS hébreu 740, fol. 15v, using the BnF 740 model.CC-BY-NC-ND · From the PDF
Image 5
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Figure 5 The learning curve of Heb_Sefaradic_Semi_Cursive_15 yielded a CER of 0.43 percent on the training set and of 2.96 percent on the test set after fifty epochs (training sessions) of training, meaning that there was fewer than one incorrectly transcribed character for every one hundred characters.
Figure 5 The learning curve of Heb_Sefaradic_Semi_Cursive_15 yielded a CER of 0.43 percent on the training set and of 2.96 percent on the test set after fifty epochs (training sessions) of training, meaning that there was fewer than one incorrectly transcribed character for every one hundred characters.CC-BY-NC-ND · From the PDF
Image 7
CC-BY-NC-ND · From the PDF
Figure 7 Example of transcription of BnF MS hébreu 740, fol. 26v, using Heb_Sefaradic_ Semi_Cursive_15 model.
Figure 7 Example of transcription of BnF MS hébreu 740, fol. 26v, using Heb_Sefaradic_ Semi_Cursive_15 model.CC-BY-NC-ND · From the PDF
Figure 8 Example of transcription of Oxford, Bodleian Library, MS Poc. 393, fol. 11r, using Heb_Sefaradic_Semi_Cursive_15.
Figure 8 Example of transcription of Oxford, Bodleian Library, MS Poc. 393, fol. 11r, using Heb_Sefaradic_Semi_Cursive_15.CC-BY-NC-ND · From the PDF
Image 10
CC-BY-NC-ND · From the PDF
Figure 10 Comparison of the transcription of the Oxford, Bodleian Library, MS Poc. 393, fol. 19v, using the Oxford_393 model and the corrected text.
Figure 10 Comparison of the transcription of the Oxford, Bodleian Library, MS Poc. 393, fol. 19v, using the Oxford_393 model and the corrected text.CC-BY-NC-ND · From the PDF

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Research works printed but not linked: 3

  1. 10.1109/ICCICCT.2014.6993174 · DOI
  2. 10.46298/jdmdh.6249 · DOI
  3. 10.1108/JD-07-2018-0114 · DOI · Paper page

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Metadata

Authors
Prebor, G.
Affiliations
Bar Ilan University Israel
Venue
Proceedings of the Association for Information Science and Technology
Publisher
Wiley
Published
2023-10
Volume
60
Issue
1
Pages
1102-1103
ISSN
2373-9231
DOI
10.1002/pra2.958
Cited by
1
References
0
Language
en
Licence
onlinelibrary.wiley.com/termsAndConditions#vor (vor, 21 d)

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CoChat. (2026). Citation check: From Digitization and Images to Text and Content: Transkribus as a Case Study. https://app.cochat.ai/papers/10.1002/pra2.958
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Prebor, G. (2023). From Digitization and Images to Text and Content: Transkribus as a Case Study. Proceedings of the Association for Information Science and Technology. https://doi.org/10.1002/pra2.958

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.