Supervised Discrimination of Characters on Images



The discrimination of characters is an important domain of optical characters recognition. The goal is to determine if a delimited surface of an image is a character or not, with rotation invariance. We are able to reduce the redundant information by doing a principal component analysis (PCA) on the training data set. Then, we use the probabilistic linear discriminant analysis (PLDA) algorithm to models both intra-class and inter-class variance as mutli-dimensional Gaussians. The performance of the new model will be compared with the one currently used in the optical characters recognition application of Olena.