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A modified direction feature for cursive character recognition

conference contribution
posted on 06.12.2017, 00:00 by M Blumenstein, X Liu, Brijesh Verma
This paper describes a neural network-based technique for cursive character recognition applicable to segmentation-based word recognition systems. The proposed research builds on a novel feature extraction technique that extracts direction information from the structure of character contours. This principal is extended so that the direction information is integrated with a technique for detecting transitions between background and foreground pixels in the character image. The proposed technique is compared with the standard direction feature extraction technique, providing promising results using segmented characters from the CEDAR benchmark database.

Funding

Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)

History

Start Page

2983

End Page

2988

Number of Pages

6

Start Date

01/01/2004

Location

Budapest, Hungary

Publisher

Institute of Electrical and Electronics Engineers, Inc.

Place of Publication

Piscataway, NJ, USA

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

Faculty of Informatics and Communication; Griffith University;

Era Eligible

Yes

Name of Conference

IEEE International Conference on Neural Networks;IEEE International Conference on Fuzzy Systems

Exports

CQUniversity

Exports