Adaptive classifiers for multisource OCR

Authors:
Sriharsha Veeramachaneni;George Nagy
Affiliations:
Rensselaer Polytechnic Institute, 110 8th St., NY 12180, USA;Rensselaer Polytechnic Institute, 110 8th St., NY 12180, USA
Venue:
International Journal on Document Analysis and Recognition
Year:
2003

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Abstract

When patterns occur in large groups generated by a single source (style consistent test data), the statistics of the test data differ from those of the training data, which consist of patterns from all sources. We present a Gaussian model for continuously distributed sources under which we develop adaptive classifiers that specialize in 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.