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Download fileA neural-evolutionary approach for feature and architecture selection in online handwriting recognition
conference contribution
posted on 2017-12-06, 00:00 authored by Brijesh Verma, M GhoshAn 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.
History
Parent Title
Proceedings of Seventh International Conference on Document Analysis and Recognition (ICDAR'03), United Kingdom, 3-6 August, 2003.Start Page
1203End Page
1207Number of Pages
5Start Date
2003-01-01Finish Date
2003-01-01ISBN-10
0769519601Location
Edinburgh, ScotlandPublisher
IEEE Computer SocietyPlace of Publication
United StatesPeer Reviewed
- Yes
Open Access
- No
External Author Affiliations
Griffith University;Era Eligible
- Yes