Unconstrained handwritten character recognition using metaclasses of characters
Unconstrained handwritten character recognition using metaclasses of characters
- New search for: Koerich, A.L.
- New search for: Kalva, P.R.
- New search for: Koerich, A.L.
- New search for: Kalva, P.R.
In this paper we tackle the problem of unconstrained handwritten character recognition using different classification strategies. For such an aim, four multilayer perceptron classifiers (MLP) were built and used into three different classification strategies: combination of two 26-class classifiers; 26-metaclass classifier; 52-class classifier. Experimental results on the NIST SD19 database have shown that the recognition rate achieved by the metaclass classifier (87.8%) outperforms the other approaches (82.9% and 86.3%).
Document information
Unconstrained handwritten character recognition using metaclasses of characters
2005-01-01
140823 byte
Conference paper
Electronic Resource
English
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