This paper presents a cursive character recognizer embedded in an off-line cursive script recognition system. The recognizer is composed of two modules: The first one is a feature extractor, the second one a learning vector quantizer. The selected feature set was compared to Zernike polynomials using the same classifier. Experiments are reported on a database of about 49,000 isolated characters. © 2001 Elsevier Science B.V. All rights reserved.

Cursive Character Recognition by Learning Vector Quantization

CAMASTRA, Francesco;
2001

Abstract

This paper presents a cursive character recognizer embedded in an off-line cursive script recognition system. The recognizer is composed of two modules: The first one is a feature extractor, the second one a learning vector quantizer. The selected feature set was compared to Zernike polynomials using the same classifier. Experiments are reported on a database of about 49,000 isolated characters. © 2001 Elsevier Science B.V. All rights reserved.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11367/20373
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