Using semantics in matching cursive Chinese handwritten annotations

Abstract

We propose a semantic matching network for the matching of cursive Chinese handwritten annotations. This architecture combines the semantics of Chinese language with the traditional elastic ink matching. Using semantics can make the matching algorithm more intelligent by pre-selecting the most likely candidates before elastic ink matching is applied thus speed up the whole matching process. The semantic matching network can also establish a link between Chinese handwritten annotations and typed text, which can be used to match between these two. Ou, experiments show that 75 - 85% recall can be achieved with a speed improvement of 85% over traditional elastic ink matching.