Iterative sub-image binarization for document images

Existing binarization methods are categorized as either global or local. In this paper we present a new category, where the image is considered as a collection of sub-images. Each sub-image provides a statistical model for the handwritten characters that will be used to optimize the binarization of other sub-images. This method can be applied to different types of documents and doesn't require any prior knowledge about the noisiness of the sub-images.