INK MATCHING OF CURSIVE CHINESE HANDWRITTEN ANNOTATIONS

In this paper, we discuss the notion of treating electronic ink as fir st class data without attempting to recognize it by presenting two dif ferent variations of approximate ink matching (AIM) for searching ink data. We also illustrate a pen-based electronic document annotating an d browsing system and methods for searching handdrawn personal notes e mploying the described matching schemes. Adapting from the Learning by Knowledge paradigm, we propose a semantic matching network that appli es semantics of Chinese language early in the process of ink matching. Finally we evaluate several key components in our entire ink matching network via experiments. Preliminary experimental results show the ap proximate ink matching algorithms perform well, despite the informal a nd highly variable nature of Chinese handwriting. Our experiments also show some promising results on semantic matching and the feasibility of our semantic matching architecture.