A learning method for definite canonicalization based on minimum classification error

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.