Title :
A learning method for definite canonicalization based on minimum classification error
Author_Institution :
Comput. & Commun. Media Res., NEC Corp., Kawasaki, Japan
Abstract :
This paper presents a novel learning method for definite canonicalization (DC) based on minimum classification error (MCE). It is shown that DC is identical to normalized cross-correlation, and that the complementary similarity measure is derived from DC for binary patterns. The proposed learning method is derived from the framework of generalized learning vector quantization (GLVQ), which is one of the discriminative learning methods based on MCE. Experimental results obtained for machine-printed Kanji character recognition reveal that the proposed method achieves high performance recognition of low-quality patterns
Keywords :
correlation methods; learning (artificial intelligence); minimisation; optical character recognition; pattern classification; vector quantisation; DC; GLVQ; LVQ; MCE; VQ; complementary similarity measure; definite canonicalization; discriminative learning methods; generalized learning vector quantization; high performance recognition; low-quality patterns; machine-printed Kanji character recognition; minimum classification error; normalized cross-correlation; Character recognition; Computer errors; Degradation; Electronic mail; Learning systems; Marine vehicles; National electric code; Pattern recognition; Robustness; Vector quantization;
Conference_Titel :
Pattern Recognition, 2000. Proceedings. 15th International Conference on
Conference_Location :
Barcelona
Print_ISBN :
0-7695-0750-6
DOI :
10.1109/ICPR.2000.906047
