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Computerized Counting of Individuals in Ottoman Population Registers with Deep Learning

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Document Analysis Systems (DAS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12116))

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Abstract

The digitalization of historical documents continues to gain pace for further processing and extract meanings from these documents. Page segmentation and layout analysis are crucial for historical document analysis systems. Errors in these steps will create difficulties in the information retrieval processes. Degradation of documents, digitization errors and varying layout styles complicate the segmentation of historical documents. The properties of Arabic scripts such as connected letters, ligatures, diacritics and different writing styles make it even more challenging to process Arabic historical documents. In this study, we developed an automatic system for counting registered individuals and assigning them to populated places by using a CNN-based architecture. To evaluate the performance of our system, we created a labeled dataset of registers obtained from the first wave of population registers of the Ottoman Empire held between the 1840s–1860s. We achieved promising results for classifying different types of objects and counting the individuals and assigning them to populated places.

This work has been supported by European Research Council (ERC) Project: “Industrialisation and Urban Growth from the mid-nineteenth century Ottoman Empire to Contemporary Turkey in a Comparative Perspective, 1850–2000” under the European Union’s Horizon 2020 research and innovation programme grant agreement No. 679097.

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Authors and Affiliations

  1. Koc University, Rumelifeneri Yolu, 34450, Sarıyer, Istanbul, Turkey

    Yekta Said Can & Mustafa Erdem Kabadayı

Authors
  1. Yekta Said Can
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  2. Mustafa Erdem Kabadayı
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Correspondence to Yekta Said Can .

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Editors and Affiliations

  1. Huazhong University of Science and Technology, Wuhan, China

    Xiang Bai

  2. Autonomous University of Barcelona, Barcelona, Spain

    Dimosthenis Karatzas

  3. Lehigh University, Bethlehem, PA, USA

    Daniel Lopresti

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Can, Y.S., Kabadayı, M.E. (2020). Computerized Counting of Individuals in Ottoman Population Registers with Deep Learning. In: Bai, X., Karatzas, D., Lopresti, D. (eds) Document Analysis Systems. DAS 2020. Lecture Notes in Computer Science(), vol 12116. Springer, Cham. https://doi.org/10.1007/978-3-030-57058-3_20

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  • DOI: https://doi.org/10.1007/978-3-030-57058-3_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-57057-6

  • Online ISBN: 978-3-030-57058-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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