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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 2017-12-06, 00:00 authored by M Ghosh, R Ghosh, Brijesh VermaIn 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
18Issue
7Start Page
1267End Page
1284Number of Pages
18ISSN
0218-0014Location
SingaporePublisher
World ScientificLanguage
en-ausPeer Reviewed
- Yes
Open Access
- No
External Author Affiliations
Faculty of Informatics and Communication; University of Ballarat;Era Eligible
- Yes