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<p><b>Abstract</b>—We address the problem of determining what size test set guarantees statistically significant results in a character recognition task, as a function of the expected error rate. We provide a statistical analysis showing that if, for example, the expected character error rate is around 1 percent, then, with a test set of at least 10,000 statistically independent handwritten characters (which could be obtained by taking 100 characters from each of 100 different writers), we guarantee, with 95 percent confidence, that: (1) The expected value of the character error rate is not worse than 1.25 <it>E</it>, where <it>E</it> is the empirical character error rate of the best recognizer, calculated on the test set; and (2) a difference of 0.3 <it>E</it> between the error rates of two recognizers is significant. We developed this framework with character recognition applications in mind, but it applies as well to speech recognition and to other pattern recognition problems.</p>
Pattern recognition, test set, test set size, benchmark, hypothesis testing, designed experiment, statistical significance, estimation, guaranteed estimators, recognition error.

I. Guyon, R. Schwartz, V. Vapnik and J. Makhoul, "What Size Test Set Gives Good Error Rate Estimates?," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 20, no. , pp. 52-64, 1998.
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