Cursive character recognition by learning vector quantization

摘要

This paper presents a cursive character recognizer embedded in an off line cursive script recognition system. The recognizer is composed of two modules f 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 arc reported on a database of about 49,000 isolated characters.