Abstract
Multi-level classifier architectures provide a means of improving error-rate performance in many classification tasks such as machine printed or handwritten character recognition. It is argued that appropriate multi-level structures are well suited to parallel implementation, and results are presented to characterise the performance of such structures in a practical character recognition environment for a range of configurations and, in particular, for hierarchies of increasing order.
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