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Automatic Transcription of Ottoman Documents Using Deep Learning

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Document Analysis Systems (DAS 2024)

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Abstract

With the accelerated pace of digitization, a vast collection of Ottoman documents has become accessible to researchers and the general public. However, most users interested in these documents are unable to read them, as the text is Turkish written in the Arabic-Persian script. Manual transcription of such a massive amount of documents is also beyond the capacity of human experts. With the advancements in deep learning, we have been able to provide a solution to the long-standing problem of automatic transcription of printed Ottoman documents. We evaluated three decoding strategies including Word Beam Search that allows to use a recognition lexicon and n-gram statistics during the decoding phase. Furthermore, the effect of lexicon size and coverage and language modelling via character or word n-grams are also evaluated. Using a general purpose large lexicon of the Ottoman era (260K words and 86% test coverage), the performance is measured as \(6.59\%\) character error rate and \(28.46\%\) word error rate on a test set of 6, 828 text lines.

Berrin Yanikoglu—Part of this work was done when Z. Tandoğan, S. D. Akansu and F. Kızılırmak were students at Sabancı University.

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Notes

  1. 1.

    A demo of the current system is available at https://demos.sabanciuniv.edu.

  2. 2.

    Test subset is publicly available at https://github.com/verimsu/Akis-Dataset.

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Acknowledgement

This study was supported by Scientific and Technological Research Council of Turkey (TUBITAK) under the Grant Number 122E399. The authors thank TUBITAK for their support.

Author information

Authors and Affiliations

  1. Department of Information and Document Management, Istanbul Medeniyet University, 34704, Istanbul, Turkey

    Esma F. Bilgin Tasdemir, Zeynep Tandoğan, S. Doğan Akansu & Fırat Kızılırmak

  2. Faculty of Eng. and Natural Sciences, Sabanci University, 34956, Istanbul, Turkey

    M. Umut Sen & Berrin Yanikoglu

  3. VERIM, Sabanci University, 34956, Istanbul, Turkey

    M. Umut Sen & Berrin Yanikoglu

  4. Department of Near Eastern Studies, University of Vienna, 1090, Vienna, Austria

    Aysu Akcan

  5. Faculty of Arts and Sciences, Sabanci University, 34956, Istanbul, Turkey

    Mehmet Kuru

Authors
  1. Esma F. Bilgin Tasdemir
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  2. Zeynep Tandoğan
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  3. S. Doğan Akansu
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  4. Fırat Kızılırmak
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  8. Berrin Yanikoglu
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Correspondence to Berrin Yanikoglu .

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Editors and Affiliations

  1. University of West Attica, Egaleo, Greece

    Giorgos Sfikas

  2. National Technical University of Athens, Zografou, Greece

    George Retsinas

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Tasdemir, E.F.B. et al. (2024). Automatic Transcription of Ottoman Documents Using Deep Learning. In: Sfikas, G., Retsinas, G. (eds) Document Analysis Systems. DAS 2024. Lecture Notes in Computer Science, vol 14994. Springer, Cham. https://doi.org/10.1007/978-3-031-70442-0_26

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