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  <title>DSpace Collection:</title>
  <link rel="alternate" href="http://localhost:8080/xmlui/handle/123456789/2883" />
  <subtitle />
  <id>http://localhost:8080/xmlui/handle/123456789/2883</id>
  <updated>2026-06-23T06:32:51Z</updated>
  <dc:date>2026-06-23T06:32:51Z</dc:date>
  <entry>
    <title>Handwritten Devnagri Character Recognition</title>
    <link rel="alternate" href="http://localhost:8080/xmlui/handle/123456789/2884" />
    <author>
      <name>Shelke, S. V.</name>
    </author>
    <author>
      <name>Chandwadkar, D. M.</name>
    </author>
    <id>http://localhost:8080/xmlui/handle/123456789/2884</id>
    <updated>2020-12-17T07:34:09Z</updated>
    <published>2016-04-01T00:00:00Z</published>
    <summary type="text">Title: Handwritten Devnagri Character Recognition
Authors: Shelke, S. V.; Chandwadkar, D. M.
Abstract: Recognition of handwritten characters has been a popular research area for many years. Devnagari script is a major script of India&#xD;
and is widely used for various languages. In this paper we propose a system to recognize devnagri handwritten characters. Total&#xD;
60 devnagri characters (50 letters and 10 digits) are taken in to consideration. 60 samples of each character i.e. total 3600 samples&#xD;
are used for features extraction. Classification is done by four different classifiers which are Multilayer perceptron, K-Nearest&#xD;
Neighbour, Naive Bayes classifier and Classification tree. Performance of different classifiers is compared.98.9 % accuracy is&#xD;
obtained by Multilayer perceptron</summary>
    <dc:date>2016-04-01T00:00:00Z</dc:date>
  </entry>
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