Jaap Oosterbroek - Using Max-Trees with alternative connectivity classes in historical document processing

In mathematical morphology, connectivity classes are a formal way of describing the grouping of sets of elements in a graph. When applied to images, connectivity classes can easily be used to describe relations in binary images and generalizes to gray-scale by means of threshold super-positioning. Connectivity classes have long been of interest in image processing research, because they provided a basis for the invention and verification of connectivity based algorithms. Much work has been invested in finding structured ways of modifying connectivity classes with predictable results. Here we present a method of using a combination of two known types of connectivity: mask and edge-based connectivity, in historical document processing. Max-Trees are data structures that can be used to express various connectivity classes. A system was build that uses a Max-Tree for all steps in the document processing chain, from preprocessing to feature generation. The system aims to show that a combination of these two types of connectivity counteracts some of their mutual weaknesses. Two types of filters: the k-subtractive and k-absorption filters were used to remove noise and help with segmentation. Finally a class of features that can efficiently be computed in a Max-Tree, Normalized Central Moments, were used to classify the character zones resulting from this segmentation.