Post-processing inkml for random-access navigation of voluminous handwritten ink documents

Abstract

The goal of this research is the improvement of browsing voluminous InkML data in two areas: ease of rendering continuous inkflow for replay-browsing, and ease of random access navigation in eLearning domains. The notion of real-time random access navigation in ink documents has not yet been fully exploited. Users of existing eLearning browsers are restricted to viewing static annotated slides that are inferior in quality when compared to actively replaying the same slides with sequenced ink-flow of the annotated freehand writings. We are developing a tool to investigate ways of managing massive InkML data for efficient "active visible scrolling" of recorded freehand writings in ink documents. This work will also develop and evaluate new post-processing techniques that take advantage of the relationship between ink volumes and active-rendering times for real-time random access navigation.