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Abstract

A pattern recognition system includes a hierarchical network of parametric pattern recognition components or algorithms of different types. During a 'training' phase, distinctions among character types are gathered from a set of correctly labelled training samples. The structure of the component hierarchy is established by recursive training of various subsets of the original training set and, for each component, generation of a 'decision function' that either (1) indicates a final classification by the present component the characters of the training, or (2) points to a component lower in the hierarchy, thereby establishing connectivity between components of the hierarchy. The training process continues, generating successively lower components in the hierarchy, until perfect classification is obtained on the training set. The hierarchy of components then is utilized to recognize characters or patterns from a set of unknown patterns or characters, by making successive 'passes', if necessary, on features extracted from each unknown character until the unknown character is classified.

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First Claim