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CNN-Based Page Segmentation and Object Classification for Counting Population in Ottoman Archival Documentation.

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  • 1College of Social Sciences and Humanities, Koc University, Rumelifeneri Yolu, 34450 Sarıyer, Istanbul, Turkey.

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|August 30, 2021
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Summary

This study introduces an automated system for analyzing historical Ottoman Empire registers. The system uses a CNN-based architecture to accurately count individuals and assign them to locations, improving historical document analysis.

Keywords:
Arabic script layout analysisconvolutional neural networkshistorical document analysispage segmentation

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Area of Science:

  • Computer Science
  • Digital Humanities
  • Archival Science

Background:

  • Digitalization of historical archives necessitates robust analysis systems.
  • Page segmentation and layout analysis are critical for Optical Character Recognition (OCR) and handwritten text recognition.
  • Challenges include document degradation, digitization errors, varied layouts, and unique Arabic script properties.

Purpose of the Study:

  • To develop an automatic system for counting registered individuals in historical documents.
  • To assign counted individuals to specific populated places.
  • To address the complexities of analyzing historical Arabic script documents.

Main Methods:

  • A Convolutional Neural Network (CNN)-based architecture was developed.
  • A labeled dataset was created from Ottoman Empire population registers (1840s-1860s).
  • The system was trained to classify objects and count individuals.

Main Results:

  • The system achieved promising results in object classification.
  • Accurate counting of individuals was demonstrated.
  • Successful assignment of individuals to populated places was achieved.

Conclusions:

  • The developed CNN-based system effectively analyzes historical Ottoman registers.
  • This approach offers a significant advancement for historical document analysis, particularly for Arabic scripts.
  • The system demonstrates potential for improving demographic reconstruction from archival data.