When patterns occur in large groups generated by a single source (\emph{style consistent test data}), the statistics of the test data differ from those of the training data which consists of patterns from all sources. We present a Gaussian model for continuously distributed sources under which we develop adaptive classifiers that specialize to the statistics of style-consistent test data. On NIST handwritten digit data, the adaptive classifiers reduce the error rate by more than 50\% operating on one writer ($\thickapprox$10 samples/class) at a time
Adaptive Classifiers for Multisource OCR
Veeramachaneni, Sriharsha;Nagy, George
2004-01-01
Abstract
When patterns occur in large groups generated by a single source (\emph{style consistent test data}), the statistics of the test data differ from those of the training data which consists of patterns from all sources. We present a Gaussian model for continuously distributed sources under which we develop adaptive classifiers that specialize to the statistics of style-consistent test data. On NIST handwritten digit data, the adaptive classifiers reduce the error rate by more than 50\% operating on one writer ($\thickapprox$10 samples/class) at a timeFile in questo prodotto:
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