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.


Year:
2000
Publisher:
IDIAP
Keywords:
Note:
Accepted for publication by Pattern Recognition Letters
Laboratories:




 Record created 2006-03-10, last modified 2018-03-17

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