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<p><b>Abstract</b>—Many obstacles to progress in image pattern recognition result from the fact that per-class distributions are often too irregular to be well-approximated by simple analytical functions. Simulation studies offer one way to circumvent these obstacles. We present three closely related studies of machine-printed character recognition that rely on synthetic data generated pseudorandomly in accordance with an explicit stochastic model of document image degradations. The unusually large scale of experiments—involving several million samples—that this methodology makes possible has allowed us to compute sharp estimates of the intrinsic difficulty (Bayes risk) of concrete image recognition problems, as well as the asymptotic accuracy and domain of competency of classifiers.</p>

H. S. Baird and T. K. Ho, "Large-Scale Simulation Studies in Image Pattern Recognition," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 19, no. , pp. 1067-1079, 1997.
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