Context-Aware and Content-Based Dynamic Voronoi Page Segmentation

This paper presents a dynamic approach to document page segmentation based on inter-component relationships, local patterns and context features. State-of-the art page segmentation algorithms segment zones based on local properties of neighboring connected components such as distance and orientation, and do not typically consider additional properties other than size. Our proposed approach uses a contextually aware and dynamically adaptive page segmentation scheme. The page is first over-segmented using a dynamically adaptive scheme of separation features based on [2] and adapted from [13]. A decision to form zones is then based on the context built from these local separation features and high-level content features. Zone-based evaluation was performed on sets of printed and handwritten documents in English and Arabic scripts with multiple font types, sizes and we achieved an increase of 15% over the accuracy reported in [2].