Abstract
In this paper, we discuss the notion of treating electronic ink as first class data without attempting to recognize it by presenting two different variations of approximate ink matching (AIM) for searching ink data. We also illustrate a pen-based electronic document annotating and browsing system and methods for searching handdrawn personal notes employing the described matching schemes. Adapting from the Learning by Knowledge paradigm, we propose a semantic matching network that applies 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 approximate ink matching algorithms perform well, despite the informal and highly variable nature of Chinese handwriting. Our experiments also show some promising results on semantic matching and the feasibility of our semantic matching architecture.
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