Adaptive normalization of handwritten characters using global/local affine transformation

Conventional normalization methods for handwritten characters have limitations, such as preprocessing operations because they are category-independent. The 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 3171 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.