Shape quantization and recognition with randomized trees
Neural Computation
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Shift-invariant, custom designed n-tuple features are combined with a probabilistic decision tree to classify isolated printed characters. The feature probabilities are estimated using a novel compound Bayesian procedure in order to delay the fall-off in classification accuracy with tree size due to a small sample set. On a ten-class confusion set of eight-point characters, the method yields error rates under 1% with only 3 training samples per class.