This paper proposes a new, promising character recognition system with a category-dependent normalization technique that normalizes an input pattern against each reference pattern adaptively using global affine transformation (GAT) as follows. (1) An input character pattern is fed to "the basic OCR," the most powerful of the conventional OCRs. (2) The basic OCR plays the role of rough classification and outputs a small set of candidate categories for the input pattern. (3) GAT normalizes the input pattern against each candidate's reference pattern adaptively. (4) Each adaptively normalized input pattern is fed again to the basic OCR. (5) The final recognition result is obtained using the updated "distance" values within candidate categories. In experiments, our basic OCR linked to GAT adaptive normalization is successfully, applied to 28.694 patterns of totally unconstrained handwritten characters, including Kanji, Kana, and alphanumerics, written by 300 people, with substantial improvements in recognition accuracy.