- Title
- A fully automated offline handwriting recognition system incorporating rule based neural network validated segmentation and hybrid neural network classifier
- Creator
- Ghosh, Moumita; Ghosh, Ranadhir; Verma, Brijesh
- Date
- 2004
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/36932
- Identifier
- vital:320
- Identifier
-
https://doi.org/10.1142/S0218001404003654
- Identifier
- ISSN:0218-0014
- Abstract
- 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- Author; C1
- Publisher
- World Scientific Publishing Company
- Relation
- International Journal of Pattern Recognition and Artificial Intelligence Vol. 18, no. 7 (Nov 2004), p. 1267-1283
- Rights
- Copyright World Scientic Publishing
- Rights
- This metadata is freely available under a CCO license
- Subject
- 0801 Artificial Intelligence and Image Processing; Handwriting recognition; Hybrid learning; Artificial intelligence; Pattern recognition systems; Writing
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