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<p>We present algebraic approach to the inductive learning of structural models and automatic construction of shape prototypes for character recognition on the basis of the algebraic description of curve structure proposed by Nishida and Mori (1991, 1992). A class in the structural models is a set of shapes that can be transformed continuously to each other. We consider an algebraic representation of continuous transformation of components of the shape, and give specific properties satisfied by each component in the class. The generalization rules in the inductive learning are specified from the viewpoints of continuous transformation of components and relational structure among the components. The learning procedure generalizes a pair of classes into one class incrementally and hierarchically in terms of the generalization rules. We show experimental results on handwritten numerals.</p>
algebraic approach; structural model automatic construction; inductive learning; shape prototypes; character recognition; curve structure; algebraic representation; continuous transformation; handwritten numerals; algebra; learning (artificial intelligence); optical character recognition

S. Mori and H. Nishida, "An Algebraic Approach to Automatic Construction of Structural Models," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 15, no. , pp. 1298-1311, 1993.
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