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A fully automated offline handwriting recognition system incorporating rule based neural network validated segmentation and hybrid neural network classifier
journal contributionposted on 2017-12-06, 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.
Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)
Number of Pages18
External Author AffiliationsFaculty of Informatics and Communication; University of Ballarat;