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A novel feature extraction technique for the recognition of segmented handwritten characters

conference contribution
posted on 06.12.2017, 00:00 by M Blumenstein, Brijesh Verma, H Basli
High accuracy character recognition techniques can provide useful information for segmentation-based handwritten word recognition systems. This research describes neural network-based techniques for segmented character recognition that may be applied to the segmentation and recognition components of an off-line handwritten word recognition system. Two neural architectures along with two different feature extraction techniques were investigated. A novel technique for character feature extraction is discussed and compared with others in the literature. Recognition results above 80% are reported using characters automatically segmented from the CEDAR benchmark database as well as standard CEDAR alphanumerics.

History

Start Page

137

End Page

141

Number of Pages

5

Start Date

03/08/2003

Finish Date

06/08/2003

ISBN-10

0769519601

Location

Edinburgh, Scotland

Publisher

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

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