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<article article-type="research-article">
  <front>
    <journal-meta>
      <journal-id journal-id-type="aggregator">72010604</journal-id>
      <journal-title>Electronic Imaging</journal-title>
      <issn pub-type="ppub">2470-1173</issn><issn pub-type="epub"></issn>
      <publisher>
        <publisher-name>Society for Imaging Science and Technology</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.2352/ISSN.2470-1173.2017.18.COLOR-046</article-id>
      <article-id pub-id-type="sici">2470-1173(20170129)2017:18L.137;1-</article-id>
      <article-id pub-id-type="publisher-id">s19.phd</article-id>
      <article-id pub-id-type="other">/ist/ei/2017/00002017/00000018/art00019</article-id>
      <article-categories>
        <subj-group>
          <subject>Articles</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>Page Classification for Print Imaging Pipeline</article-title>
      </title-group>
      <contrib-group>
        <contrib>
          <name>
            <surname>Xu</surname>
            <given-names>Shaoyuan</given-names>
          </name>
        </contrib>
        <contrib>
          <name>
            <surname>Lu</surname>
            <given-names>Cheng</given-names>
          </name>
        </contrib>
        <contrib>
          <name>
            <surname>Shaw</surname>
            <given-names>Mark</given-names>
          </name>
        </contrib>
        <contrib>
          <name>
            <surname>Bauer</surname>
            <given-names>Peter</given-names>
          </name>
        </contrib>
        <contrib>
          <name>
            <surname>Allebach</surname>
            <given-names>Jan</given-names>
          </name>
        </contrib>
      </contrib-group>
      <pub-date>
        <day>29</day>
        <month>01</month>
        <year>2017</year>
      </pub-date>
      <volume>2017</volume>
      <issue>18</issue>
      <fpage>137</fpage>
      <lpage>142</lpage>
      <permissions>
        <copyright-year>2017</copyright-year>
      </permissions>
      <abstract>
        <p>Digital copiers and printers are widely used nowadays. One of the most important things people care about is copying or printing quality. In order to improve it, we previously came up with an SVM-based classification method to classify images with only text, only pictures or a mixture
 of both based on the fact that modern copiers and printers are equipped with processing pipelines designed specifically for different kinds of images. However, in some other applications, we need to distinguish more than three classes. In this paper, we develop a more advanced SVM-based classification
 method using four more new features to classify 5 types of images which are text, picture, mixed, receipt and highlight.</p>
      </abstract>
      <kwd-group>
        <kwd>PAGE CLASSIFICATION</kwd>
        <kwd>MACHINE LEARNING</kwd>
        <kwd>PRINT IMAGING PIPELINE</kwd>
      </kwd-group>
    </article-meta>
  </front>
</article>
