From hyperconnections to hypercomponent tree: Application to document image binarization

International audienceIn this paper, we propose an extension of the component tree based on at zones to hyperconnections (h-connections). The tree is dened by a special order on the h-connection and allows non at nodes. We apply this method to a particular fuzzy h-connection and we give an ecient algorithm to transform the component tree into the new fuzzy h-component tree. Finally, we propose a method to binarize document images based on the h-component tree and we evaluate it on the DIBCO 2009 benchmarking dataset: our novel method places rst or second according to the dierent evaluation measures. Hierarchical and tree based representations have become very topical in image processing. In particular, the component tree (or Max-Tree) has been the subject of many studies and practical works. Nevertheless, the component tree inherits the weaknesses of the at zone approach, namely its high sensitivity to noise, gradients and diculty to manage disconnected objects. Even if some solutions have been proposed to preserve the component tree [5, 4], it seems that a more general framework for grayscale component tree [1] based on non at zones becomes necessary. In this article, we propose a method to design grayscale component tree based on h-connections. The h-connection theory has been proposed in [7] and developed in [1, 3, 4, 8, 9]. It provides a general denition of the notion of connected component in arbitrary lattices. In Sec. 2, we present the h-connection theory and a method to generate a related hierarchical representation. This method is applied to a fuzzy h-connection in Sec. 3 where an algorithm is given to transform a Max-Tree into the new grayscale component tree. In Sec. 4, we illustrate the interest of this tree with an application on document image binarization. 2 H-component Tree This section presents the basis of the h-connection theory [7, 1] and gives a denition of the h-component tree. The construction of the tree is based on the z-zones [1] of the h-connection, together with a special partial ordering. Z-zones are particular regions where all points generate the same set of hyperconnected (h-connected) components and the entire image can be divided into such zones. Under a given condition, the Hasse diagram obtained in this way is acyclic and provides a tree representation. Let L be a complete lattice furnished with the partial ordering ≤, the inmum , the supremum. The least element of L is denoted by ⊥ = L. We assume the existence of a sup-generatin

Adaptive Methods for Robust Document Image Understanding

A vast amount of digital document material is continuously being produced as part of major digitization efforts around the world. In this context, generic and efficient automatic solutions for document image understanding represent a stringent necessity. We propose a generic framework for document image understanding systems, usable for practically any document types available in digital form. Following the introduced workflow, we shift our attention to each of the following processing stages in turn: quality assurance, image enhancement, color reduction and binarization, skew and orientation detection, page segmentation and logical layout analysis. We review the state of the art in each area, identify current defficiencies, point out promising directions and give specific guidelines for future investigation. We address some of the identified issues by means of novel algorithmic solutions putting special focus on generality, computational efficiency and the exploitation of all available sources of information. More specifically, we introduce the following original methods: a fully automatic detection of color reference targets in digitized material, accurate foreground extraction from color historical documents, font enhancement for hot metal typesetted prints, a theoretically optimal solution for the document binarization problem from both computational complexity- and threshold selection point of view, a layout-independent skew and orientation detection, a robust and versatile page segmentation method, a semi-automatic front page detection algorithm and a complete framework for article segmentation in periodical publications. The proposed methods are experimentally evaluated on large datasets consisting of real-life heterogeneous document scans. The obtained results show that a document understanding system combining these modules is able to robustly process a wide variety of documents with good overall accuracy