Offline handwritten character recognition based on discriminative training of orthogonal Gaussian mixture model

The statistical approach to offline handwritten character recognition, in which classifier design is very important, has been used widely. To approximate the class conditional density more precisely, it can be represented by an orthogonal Gaussian mixture model (OGMM). The parameters of the OGMM are commonly estimated by an expectation-maximization algorithm (EM), which converges to the maximum likelihood estimation (MLE). Since the MLE cannot directly minimize the classification errors as the goal of classifier design, a discriminative training method based on the minimum classification error (MCE) criterion is used to adjust the parameters of the OGMM. In order to achieve good generalization, the complexity of the classifier, namely the number of components in the OGMM, is determined by following the structure risk minimization (SRM) principle. Finally, the recognition performance is demonstrated by applying it to the handwritten numerals in the NIST database