Researcher:
Kabadayı, Mustafa Erdem

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Mustafa Erdem

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Kabadayı

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Kabadayı, Mustafa Erdem

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2016 - 201912020 - 202233

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Researcher Of Publications: search.filters.isAuthorOfPublication.613542ab-2038-4fec-9dd7-e922d7216d90

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Now showing 1 - 10 of 34
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    Ethno-religious division of labour in urban economie s of the Ottoman Empire in the nineteenth century
    (Berghahn Books, 2020) Güvenç, Murat; Department of History; Kabadayı, Mustafa Erdem; Faculty Member; Department of History; College of Social Sciences and Humanities; 33267
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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    With the increased amount of digitized historical documents, information extraction from them gains pace.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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    Epilogue
    (Berghahn Books, 2020) Papastefanaki, Leda; Department of History; Kabadayı, Mustafa Erdem; Faculty Member; Department of History; College of Social Sciences and Humanities; 33267
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    Reading and mapping mid-nineteenth century Ottoman tax registers: an early attempt toward building a digital research infrastructure for Ottoman economic and social history
    (Peter Lang AG, 2020) Güvenç, Murat; Department of History; Kabadayı, Mustafa Erdem; Faculty Member; Department of History; College of Social Sciences and Humanities; 33267
    This proposed paper would use advanced methods of cluster analysis and multiple correspondence analysis (MCA). It will present some outcomes of a three-year research project An Introduction to the Occupational History of Turkey via New Methods and New Approaches (1840-1940), funded by the Scientific and Technological Research Council of Turkey (TUBITAK) (Nr. 112K271), which will be completed in September 2015. Kabadayi is the principal investigator and Güvenç is the advisor of this project. Especially in recent years, MCA has gained popularity among social scientists, but to the best of our knowledge, it has not yet been used in economic or social history in the international literature in English. We will present the preliminary results of our application of MCA to the 1845 Ottoman tax registers (temettuat) focusing on Bursa. For this city, we have been able to locate 126 of the 145 neighbourhoods listed in the records. 7,914 individuals were recorded. 5,662 identified as household heads and 2,252 not. An important aspect of this work involves identifying patterns connected to ethnoreligious affiliations.
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    Introduction and historiographical essay: Greek and Turkish economic and social history, and labour history
    (Berghahn Books, 2020) Papastefanaki, Leda; Department of History; Kabadayı, Mustafa Erdem; Faculty Member; Department of History; College of Social Sciences and Humanities; 33267
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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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    CNN-based page segmentation and object classification for counting population in ottoman archival documentation
    (Multidisciplinary Digital Publishing Institute (MPDI), 2020) 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 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.
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    Developing an automatic layout analysis system for Ottoman population registers
    (IEEE, 2020) Can, Yekta Said; Kabadayı, Mustafa Erdem; Researcher; Faculty Member; College of Social Sciences and Humanities; N/A; 33267
    For extracting information from the historical documents, digitization efforts have increased dramatically in the recent decades. Accurate layout analysis will help researchers for developing more robust HTR and OCR techniques which will extract meaningful information from these documents. Variable layouts, low quality and distorted images of historical documents create different problems to deal with when compared to modern document processing. Arabic script features have even more problems for these automatic processing systems. In this study, we have developed a tool for automatically analyzing the layouts of the first Ottoman population registers which are written in Arabic script form. We built a dataset for testing the performance of our system which are chosen from the first population records of the Ottoman Empire between the 1840s and 1860s. We successfully classified two different object types in those documents.
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    Curation of historical Arabic handwritten digit datasets from Ottoman population registers: a deep transfer learning case study
    (IEEE, 2020) Can, Yekta Said; Kabadayı, Mustafa Erdem; Researcher; Faculty Member; College of Social Sciences and Humanities; N/A; 33267
    With the increasing number of digitization efforts of historical manuscripts and archives, automatical information retrieval systems need to extract meaning fast and reliably. Historical archives bring more challenges for these systems when compared to modern manuscripts. More advanced algorithms, archive specific methods, preprocessing techniques are needed to retrieve information. Cutting-edge machine learning algorithms should also be applied to retrieve meaning from these documents. One of the most important research issues of historical document analysis is the lack of public datasets. Although there are plenty of public datasets for modern document analysis, the number of public annotated historical archives is limited. Researchers can test novel algorithms on these modern datasets and infer some results, but their performance is unknown without testing them on historical datasets. In this study, we created a historical Arabic handwritten digit dataset by combining manual annotation and automatic document analysis techniques. The dataset is open for researchers and contained more than 6000 digits. We then tested deep transfer learning algorithms and various machine learning techniques to recognize these digits and achieved promising results.
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    Automatic CNN-based Arabic numeral spotting and handwritten digit recognition by using deep transfer learning in Ottoman population registers
    (Mdpi, 2020) 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
    Historical manuscripts and archival documentation are handwritten texts which are the backbone sources for historical inquiry. Recent developments in the digital humanities field and the need for extracting information from the historical documents have fastened the digitization processes. Cutting edge machine learning methods are applied to extract meaning from these documents. Page segmentation (layout analysis), keyword, number and symbol spotting, handwritten text recognition algorithms are tested on historical documents. For most of the languages, these techniques are widely studied and high performance techniques are developed. However, the properties of Arabic scripts (i.e., diacritics, varying script styles, diacritics, and ligatures) create additional problems for these algorithms and, therefore, the number of research is limited. In this research, we first automatically spotted the Arabic numerals from the very first series of population registers of the Ottoman Empire conducted in the mid-nineteenth century and recognized these numbers. They are important because they held information about the number of households, registered individuals and ages of individuals. We applied a red color filter to separate numerals from the document by taking advantage of the structure of the studied registers (numerals are written in red). We first used a CNN-based segmentation method for spotting these numerals. In the second part, we annotated a local Arabic handwritten digit dataset from the spotted numerals by selecting uni-digit ones and tested the Deep Transfer Learning method from large open Arabic handwritten digit datasets for digit recognition. We achieved promising results for recognizing digits in these historical documents.