Dynamic filters selection for textual document image binarization

For a document class, one challenge in document binarization is to automatically find a set of techniques, which are adapted to the different degradation level of the images. It is important to know the methods to use and where they can be applied advantageously. A multi-classifiers solution is presented for pixel classification. These classifiers act as filters and are used for binarization. The technique starts by clustering close pixels by K-means. A classifier, which corresponds to a supervised neural network, is dedicated to each cluster. They are trained according to a binarized image where its pixels are weighted function to erosion transformation effects. The presented method is compared to classical binarization techniques in the literature. Its effect on the commercial OCR performance reaches a gain from 0.16% for Finereader7 and 1.06% for Omnipage14 for the recognition rate.