Self Learning Classification for Degraded Document Images by Sparse Representation

Document Image Binarization is a technique to segment text out from the background region of a document image, which is a challenging task due to high intensity variations of the document foreground and background. Recently, a series of document image binarization contests (DIBCOs) had been held that have drawn great research interest in this area. Several document binarization techniques have been proposed and achieve great performance on the contest datasets. However, those proposed techniques may not perform well on all kinds of degraded document images because it is difficult to design a classification method that correctly models the non-uniform degraded document background and text foreground simultaneously. In this paper, we propose a self learning classification framework that combines binary outputs of different binarization methods. The proposed framework makes used of the sparse representation to re-classify the document pixels and produces a better binary results. The experimental results on the recent DIBCO contests show the great performance and robustness of our proposed framework on different kinds of degraded document images.