Cursive Character Recognition by Learning Vector Quantization
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 an LVQ. The selected feature set was compared to Zernike polynomials using the same classifier. Experiments are reported on a database of about 49000 isolated characters.
Accepted for publication by Pattern Recognition Letters
Record created on 2006-03-10, modified on 2016-08-08