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.