This work focuses on the most commonly used binarization method: Sauvola's. It performs relatively well on classical documents, however, three main defects remain: the window parameter of Sauvola's formula does not fit automatically to the contents, it is not robust to low contrasts, and it is not invariant with respect to contrast inversion. Thus on documents such as magazines, the contents may not be retrieved correctly, which is crucial for indexing purpose.

Abstract-Electronic documents are being more and more usable thanks to better and more affordable network, storage and computational facilities. But in order to benefit from computeraided document management, paper documents must be digitized and analyzed. This task may be challenging at several levels. Data may be of multiple types thus requiring different adapted processing chains. The tools to be developed should also take into account the needs and knowledge of users, ranging from a simple graphical application to a complete programming framework. Finally, the data sets to process may be large. In this paper, we expose a set of features that a Document Image Analysis framework should provide to handle the previous issues. In particular, a good strategy to address both flexibility and efficiency issues is the Generic Programming (GP) paradigm. These ideas are implemented as an open source module, SCRIBO, built on top of Olena, a generic and efficient image processing platform. Our solution features services such as preprocessing filters, text detection, page segmentation and document reconstruction (as XML, PDF or HTML documents). This framework, composed of reusable software components, can be used to create full-fledged graphical applications, small utilities, or processing chains to be integrated into third-party projects.

Mathematical morphology, when used in the field of document image analysis and processing, is often limited to some classical yet basic tools. The domain however features a lesser-known class of powerful operators, called connected filters. These operators present an important property: they do not shift nor create contours. Most connected filters are linked to a tree-based representation of an image's contents, where nodes represent connected components while edges express an inclusion relation. By computing attributes for each node of the tree from the corresponding connected component, then selecting nodes according to an attribute-based criterion, one can either filter or recognize objects in an image. This strategy is very intuitive, efficient, easy to implement, and actually well-suited to processing images of magazines. Examples of applications include image simplification, smart binarization, and object identification.