A new character recognition method using multiple subspace for each category is proposed. The method is essentially the CLAFIC method. Five subspaces for each category are spanned by vector series expansions constructed from five feature vectors extracted from character patterns separately. The features used are proposed for the pre-classification. The decision procedure of this method is as follows. The feature vectors are extracted from an input character pattern. The projections of those feature vectors on corresponding feature subspaces are computed and five projection lengths are obtained for each category. Five-dimensional vectors whose elements are the above projection lengths are defined for each category. The decision rule is to classify the input pattern into the category on whose five-dimensional vector it has the largest Euclidean norm. The discrimination ability for similar Kanji characters of this method is examined through a discrimination experiment using handprinted Kanji character database ETL-9(B).
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