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A neural-evolutionary approach for feature and architecture selection in online handwriting recognition

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conference contribution
posted on 2017-12-06, 00:00 authored by Brijesh Verma, M Ghosh
An automatic recognition of online handwritten text has been an on-going research problem for nearly four decades. It has been gaining more interest due to the increasing popularity of hand-held computers, digital notebooks and advanced cellular phones. However for these input modalities to be economical and user friendly the recognition rate should be very high for real time use. Also, the large number of writing styles and the variability between them makes the handwriting recognition problem a very challenging area for researchers. Many researchers have proposed a number of novel techniques for online handwriting recognition. However, an acceptable classification rate has not been achieved yet and there is a lack of techniques, which can find appropriate features, architecture and network parameters for online handwriting recognition. In this paper we propose a novel neurogenetic technique to improve classification accuracy through the selection of appropriate features and network parameters for online handwriting recognition. The technique incorporates an evolutionary approach for finding the most significant features, network architecture and its parameters.


Parent Title

Proceedings of Seventh International Conference on Document Analysis and Recognition (ICDAR'03), United Kingdom, 3-6 August, 2003.

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Edinburgh, Scotland


IEEE Computer Society

Place of Publication

United States

Peer Reviewed

  • Yes

Open Access

  • No

External Author Affiliations

Griffith University;

Era Eligible

  • Yes

Name of Conference

International Conference on Document Analysis and Recognition