Conventional normalization methods for handwritten characters have limitations as preprocessing operations because they are category-independent. This paper introduces an adaptive or category-dependent normalization method that normalizes an input pattern against each reference pattern using global/local affine transformation (GAT/LAT) in a hierarchical manner as a general deformation model. Experiments using input patterns of 3,171 character categories, including Kanji, Kana, and alphanumerics, written by 36 people in the cursive style against square-style reference patterns show not only that the proposed method can absorb a fair large amount of handwriting fluctuation within the same category but also that discrimination ability is greatly improved by the suppression of excessive normalization against similarly shaped but different categories.