"Document Image Binarization using Local Features and Gaussian Mixture Modelling"
Image and Vision Computing, Vol. 38, pp. 33-51, 2015.
In this paper, we address the document image binarization problem with a
three-stage procedure. First, possible stains and general document background
information are removed from the image through a background removal stage.
The remaining misclassified background and character pixels are then separated
using a Local Co-occurrence Mapping, local contrast and a two-state Gaussian
Mixture Model. Finally, some isolated misclassified components are removed
by a morphology operator. The proposed scheme offers robust and fast performance,
especially for both handwritten and printed documents, which compares
favourably with other binarization methods.