<?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-2176</article-id>
      <title-group>
        <article-title>Development of novel signature architecture for signature recognition</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, Poornima University, Jaipur, Rajasthan, 303905, India</aff>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Saxena</surname>
            <given-names>Rakesh Kumar</given-names>
          </name>
          <aff>Department of Computer Engineering, Poornima University, Jaipur, Rajasthan, 303905, India</aff>
        </contrib>
      </contrib-group>
      <volume>28</volume>
      <issue>5-A</issue>
      <fpage>1763</fpage>
      <lpage>1769</lpage>
      <pub-date date-type="pub">
        <day>30</day>
        <month>08</month>
        <year>2025</year>
      </pub-date>
      <abstract>
        <p>The aim of this research paper is to propose a novel signature recognition system that utilizes deep learning techniques to accurately identify the name of a person based on their handwritten signature. The proposed architecture involves the use of a convolutional neural network (CNN) for feature extraction and a neural network for classification. Experiments were conducted on the GPDS synthetic Signature dataset [1] to evaluate the performance of the model. The results of the experiments showed that the proposed system achieved an impressive training accuracy of 99.98% and a validation accuracy of 84.53% </p>
      </abstract>
      <kwd-group>
        <kwd>Signature recognition</kwd>
        <kwd>Convolutional neural network</kwd>
        <kwd>VGG16</kwd>
        <kwd>GDPS</kwd>
        <kwd>Machine learning</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>
