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    <identifier>10.57760/sciencedb.j00001.01083</identifier>
    <datestamp>2024-04-29T10:01:03Z</datestamp>
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<oai_dc:dc xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:date>2024-04-29</dc:date>
  <dc:title>An Image Dataset of Dunhuang Manuscripts for Fragments Conjunction</dc:title>
  <dc:identifier>doi:10.57760/sciencedb.j00001.01083</dc:identifier>
  <dc:language>en</dc:language>
  <dc:description>Dunhuang manuscripts are precious heritage created by various ethnic groups in China during the history. Because of the ancient times they have been through, most of the manuscripts are damaged, which makes the conjunction a key step in Dunhuang studies. Manual stitching is difficult and time-consuming. With the improvement of computer technology in recent years, automatic conjunction of fragments with computer assistance has emerged. However, there is a severe lack of high-quality image datasets due to the complex reservation conditions of Dunhuang manuscripts, while the method with computer needs a big image dataset. This dataset collects a batch of high-quality fragment image data based on published manuscripts conjunction papers, and supplements it with manually segmented images. The scale of the dataset is 95 groups with 366 images. Each group of data includes 1 completed image as the reference of conjunction, as well as 2-7 fragments. The content of images mainly involved Chinese, with some ancient Tibetan. While the collection institutions include the National Library of China, the British Library, the National Library of France, and the Dunhuang Academy. The data is representative and can support the training and validation of conjunction algorithms or models.</dc:description>
  <dc:subject>Dunhuang Manuscripts; Automatic conjunction; Fragments image; Ancient manuscript fragments</dc:subject>
  <dc:creator>Gao Ge</dc:creator>
  <dc:creator>Yin Zixuan</dc:creator>
  <dc:creator>Li Xuelong</dc:creator>
  <dc:creator>Zhao Xiaobing</dc:creator>
  <dc:creator>Weng Yu</dc:creator>
  <dc:rights>PUBLIC</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by-nc-sa/4.0/</dc:rights>
  <dc:type>dataset</dc:type>
  <dc:relation>http://www.doi.org/10.11922/11-6035.csd.2024.0039.zh</dc:relation>
  <dc:publisher>Science Data Bank</dc:publisher>
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