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The document presents a performance evaluation of various pattern classifiers for handwritten digit recognition, including a statistical classifier (modified quadratic discriminant function, MQDF) and several neural classifiers (MLP, RBF, PC, LVQ). The study assesses their classification accuracy, sensitivity to training sample size, ambiguity rejection, and outlier resistance using a large dataset from NIST SD19. Results indicate that while neural classifiers achieve higher accuracy with larger sample sizes, MQDF excels in outlier rejection. The findings suggest complementary advantages among classifiers and recommend combining them for improved performance.

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