Abstract
Recently, more and more studies have applied state-of-the-art algorithms for extracting information from handwritten historical documents.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.
This work was supported by the 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 program Grant Agreement No. 679097, acronym UrbanOccupationsOETR. M. Erdem Kabadayı is the principal investigator of UrbanOccupationsOETR.
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Can, Y.S., Kabadayı, M.E. (2021). Line Segmentation of Individual Demographic Data from Arabic Handwritten Population Registers of Ottoman Empire. In: Barney Smith, E.H., Pal, U. (eds) Document Analysis and Recognition – ICDAR 2021 Workshops. ICDAR 2021. Lecture Notes in Computer Science(), vol 12916. Springer, Cham. https://doi.org/10.1007/978-3-030-86198-8_22
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