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

College of Social Sciences and Humanities, Koc University, Rumelifeneri Yolu, 34450 Sarıyer, Istanbul, Turkey
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Received: 31 March 2020 / Revised: 10 May 2020 / Accepted: 11 May 2020 / Published: 14 May 2020
(This article belongs to the Special Issue Recent Advances in Historical Document Processing)
Historical document analysis systems gain importance with the increasing efforts in the digitalization of archives. Page segmentation and layout analysis are crucial steps for such systems. Errors in these steps will affect the outcome of handwritten text recognition and Optical Character Recognition (OCR) methods, which increase the importance of the page segmentation and layout analysis. Degradation of documents, digitization errors, and varying layout styles are the issues that complicate the segmentation of historical documents.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letters, ligatures, diacritics, and different writing styles make it even more challenging to process Arabic script historical documents.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 and 1860s. We achieved promising results for classifying different types of objects and counting the individuals and assigning them to populated places. View Full-Text
Keywords: page segmentation; historical document analysis; convolutional neural networks; Arabic script layout analysis page segmentation; historical document analysis; convolutional neural networks; Arabic script layout analysis
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MDPI and ACS Style

Can, Y.S.; Kabadayı, M.E. CNN-Based Page Segmentation and Object Classification for Counting Population in Ottoman Archival Documentation. J. Imaging 2020, 6, 32. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging6050032

AMA Style

Can YS, Kabadayı ME. CNN-Based Page Segmentation and Object Classification for Counting Population in Ottoman Archival Documentation. Journal of Imaging. 2020; 6(5):32. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging6050032

Chicago/Turabian Style

Can, Yekta S.; Kabadayı, M. E. 2020. "CNN-Based Page Segmentation and Object Classification for Counting Population in Ottoman Archival Documentation" J. Imaging 6, no. 5: 32. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging6050032

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