Character string extraction from color documents using supervised learning
In this study, we deal with a pattern classification problem for extracting character string regions from digital color documents that contain both character images and other types of images. We applied supervised classification methods to data set represented in the feature space. By utilizing the characteristics that especially the negative data distribute as to have a cluster structure, we devised a method using a mixture of classifiers. Additionally, we propose a parametric feature that allows to improve the discriminative performance by optimizing its own parameters. As a consequence, we have achieved a high classification accuracy, which is as high as 96.5%, when applied to various color documents even with complicated layouts.