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A fully automated offline handwriting recognition system incorporating rule based neural network validated segmentation and hybrid neural network classifier

journal contribution
posted on 06.12.2017, 00:00 authored by M Ghosh, R Ghosh, Brijesh Verma
In this paper we propose a fully automated offline handwriting recognition system that incorporates rule based segmentation, contour based feature extraction, neural network validation, a hybrid neural network classifier and a hamming neural network lexicon. The work is based on our earlier promising results in this area using heuristic segmentation and contour based feature extraction. The segmentation is done using many heuristic based set of rules in an iterative manner and finally followed by a neural network validation system. The extraction of feature is performed using both contour and structure based feature extraction algorithm. The classification is performed by a hybrid neural network that incorporates a hybrid combination of evolutionary algorithm and matrix based solution method. Finally a hamming neural network is used as a lexicon. A benchmark dataset from CEDAR has been used for training and testing.

Funding

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

History

Volume

18

Issue

7

Start Page

1267

End Page

1284

Number of Pages

18

ISSN

0218-0014

Location

Singapore

Publisher

World Scientific

Language

en-aus

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

Faculty of Informatics and Communication; University of Ballarat;

Era Eligible

Yes

Journal

International journal of pattern recognition and artificial intelligence.