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    <identifier>10.11922/sciencedb.j00104.00102</identifier>
    <datestamp>2022-04-06T13:48:52Z</datestamp>
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  <dc:date>2022-04-06</dc:date>
  <dc:title>Datasets for NLPCC2022.SharedTask5.Track3</dc:title>
  <dc:identifier>doi:10.11922/sciencedb.j00104.00102</dc:identifier>
  <dc:language>en</dc:language>
  <dc:description>Detecting and extracting inline and isolated mathematical expressions (including mathematical characters, symbols, and equations) are among the most important and challenging tasks in text mining. Although OCR techniques are effective on digitizing ordinary text, their performance on mathematical expression are quite limited. Mathematical expressions are essential information containers, especially for the fields of academic research, regulation&amp;nbsp;and education. The accurate recognition of inline and isolated mathematical expressions has a wide variety of applications and is favored by readers and practitioners in related fields.</dc:description>
  <dc:subject>Detection and Extraction of Inline and Isolated Mathematical Expressions ;  PDF Information Extraction; English PDF Articles</dc:subject>
  <dc:creator>CNPIEC KEXIN LTD</dc:creator>
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  <dc:publisher>Science Data Bank</dc:publisher>
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