A flexible framework for online document segmentation by pairwise stroke distance learning

•A flexible framework for handling segmentation of online documents is introduced.•The system is based on single linkage clustering with globally trained distance.•Absence of external parameters permits flexibility for various segmentation tasks.•Results on a variety of segmentation tasks demonstrate the method validity.

A flexible framework for handling a variety of segmentation problems in online handwritten documents is introduced. The strategy relies on single linkage clustering and a pairwise stroke distance that is globally trained for direct optimization of the segmentation. We define a variety of features that can contribute to the pairwise distance definition and show how to select a good combination of features for dealing with a new online handwritten document segmentation task. Our experiments demonstrate the validity of the method on a large range of segmentation tasks over several types of documents from various publicly available databases.