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Abstract

In a clustering system for an optical character reader, feature vectors of character image data to be classified are stored in a first memory. The number of classes, and representative vectors for the respective classes which are previously provided are stored in a second memory. Values of the cosine of each of the feature vectors stored in the first memory and each of the representative vectors stored in the second memory are calculated by a cosine calculating unit. Then, each of the feature vectors is assigned to one of the classes from which the largest cosine value is calculated, and the largest cosine value is stored into a third memory. For each of the thus processed classes, a total sum vector calculating unit calculates a total sum vector from the feature vectors which belong to the respective class, using the largest cosine values for the respective feature vectors stored in the third memory as weights. The resulting weighted total sum vector is stored into the second memory as a new representative vector for the class so as to update the contents of the second memory. The cosine calculating unit, and total sum vector calculating unit are controlled to operate repeatedly, until a convergence judging unit judges that any feature vectors are not exchanged between the classes during the classification.

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