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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/3

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Department: search.filters.department.Department of HistorySponsors: search.filters.sponsors.EU

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    Automatic detection of road types from the third military mapping survey of Austria-Hungary historical map series with deep convolutional neural networks
    (IEEE-inst Electrical Electronics Engineers inc, 2021) N/A; N/A; Department of History; Can, Yekta Said; Gerrits, Petrus Johannes; Kabadayı, Mustafa Erdem; Resercher; Master Student; Faculty Member; Department of History; College of Social Sciences and Humanities; Graduate School of Social Sciences and Humanities; College of Social Sciences and Humanities; N/A; N/A; 33267
    With the increased amount of digitized historical documents, information extraction from them gains pace. Historical maps contain valuable information about historical, geographical and economic aspects of an era. Retrieving information from historical maps is more challenging than processing modern maps due to lower image quality, degradation of documents and the massive amount of non-annotated digital map archives. Convolutional Neural Networks (CNN) solved many image processing challenges with great success, but they require a vast amount of annotated data. for historical maps, this means an unprecedented scale of manual data entry and annotation. in this study, we first manually annotated the Third Military Mapping Survey of austria-Hungary historical map series conducted between 1884 and 1918 and made them publicly accessible. We recognized different road types and their pixel-wise positions automatically by using a CNN architecture and achieved promising results.
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    Text detection and recognition by using CNNs in the Austro-Hungarian historical military mapping survey
    (Association for Computing Machinery, 2021) Department of History; N/A; Kabadayı, Mustafa Erdem; Can, Yekta Said; Faculty Member; Researcher; Department of History; College of Social Sciences and Humanities; College of Social Sciences and Humanities; 33267; N/A
    Historical maps include precious data about historical, geographical and economic perspectives of a period. However, several unique challenges and opportunities accompany historical maps compared to modern maps, such as low-quality images, degraded manuscripts and the huge quantity of non-annotated digital map collections. In the recent decade, Convolutional Neural Networks (CNNs) are applied to solve various image processing problems, but they need enormous annotated data to have accurate results. In this work, we annotated text regions of the Third Military Mapping Survey of Austria-Hungary historical map series conducted between 1884 and 1918 manually and made them accessible for researchers. Then, we detected the pixel-wise positions of text regions by employing the deep neural network architecture and recognized them with encouraging error rates.
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    Line segmentation of individual demographic data from Arabic handwritten population registers of Ottoman Empire
    (Springer International Publishing Ag, 2021) N/A; Department of History; Can, Yekta Said; Kabadayı, Mustafa Erdem; Researcher; Faculty Member; Department of History; College of Social Sciences and Humanities; College of Social Sciences and Humanities; N/A; 33267
    Recently, more and more studies have applied state-of-the-art algorithms for extracting information from handwritten historical documents. Line segmentation is a vital stage in the HTR systems; it directly affects the character segmentation stage, which affects the recognition success. In this study, we first applied deep learning-based layout analysis techniques to detect individuals in the first Ottoman population register series collected between the 1840s and 1860s. Then, we used a star path planning algorithm-based line segmentation to the demographic information of these detected individuals in these registers. We achieved encouraging results from the selected regions, which could be used to recognize the text in these registers.