A Statistical Model for Machine Print Recognition

With the aid of statistical detection theory, the continuous optimum Bayes recognition scheme for machine characters is developed using a sufficient statistic approach. This result is then extended to the discrete case and the associated problems such as the sampling effects are examined. Using the discrete optimum recognition model, design parameters are then developed. Among these are the critical sampling matrix which provides a lower bound for the sampling rates and the rate matrix which provides information on the classification error. Algorithms such as the two-dimensional fast Fourier transform are then employed to calculate these discrete optimum model design parameters. Based on this theoretical background a machine print recognition problem is presented. The design parameters are computed and the discrete optimum system is simulated on the digital computer. Finally some actual machine print data is recognized and the results are analyzed.