<?xml version="1.0" encoding="UTF-8"?>
<article article-type="Research Article">
  <front>
    <journal-meta>
      <journal-id journal-id-type="publisher">journal-of-discrete-mathematical-sciences-and-cryptography</journal-id>
      <journal-title-group>
        <journal-title>Journal of Discrete Mathematical Sciences and Cryptography</journal-title>
      </journal-title-group>
      <issn publication-format="electronic">2169-0065</issn>
      <issn publication-format="print">0972-0529</issn>
      <publisher>
        <publisher-name>Taru Publications</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.47974/JDMSC-1756</article-id>
      <title-group>
        <article-title>Development of signature recognition system using VGG16</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author" corresp="yes">
          <name>
            <surname>Moud</surname>
            <given-names>Deepak</given-names>
          </name>
          <aff>Department of Computer Engineering, Jaipur, Rajasthan, Poornima University, India</aff>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Saxena</surname>
            <given-names>Rakesh Kumar</given-names>
          </name>
          <aff>Department of Computer Engineering, Jaipur, Rajasthan, Poornima University, India</aff>
        </contrib>
      </contrib-group>
      <volume>26</volume>
      <issue>3</issue>
      <fpage>807</fpage>
      <lpage>813</lpage>
      <pub-date date-type="pub">
        <day>01</day>
        <month>04</month>
        <year>2023</year>
      </pub-date>
      <abstract>
        <p>The goal of this research is to locate a signature recognition system that can replace individual presence in the process of handwritten signature recognition. It has been discovered that an automatic signature recognition system that does not require a person’s physical presence is necessary throughout corona duration. In this article more accurate signature recognition model employing VGG 16 pre- trained models is proposed. Novel Convolution neural network has exhibited 83% validation accuracy on the GPDS synthetic Signature dataset[1].</p>
      </abstract>
      <kwd-group>
        <kwd>Signature recognition</kwd>
        <kwd>CNN</kwd>
        <kwd>VGG16</kwd>
        <kwd>GDPS</kwd>
        <kwd>ML</kwd>
      </kwd-group>
      <custom-meta-group>
        <custom-meta>
          <meta-name>access</meta-name>
          <meta-value>open</meta-value>
        </custom-meta>
        <custom-meta>
          <meta-name>retracted</meta-name>
          <meta-value>no</meta-value>
        </custom-meta>
      </custom-meta-group>
    </article-meta>
  </front>
</article>
