N-grams: a well-structured knowledge representation for recognition of graphical documents

N-grams are a well-structured knowledge representation that has proven useful in the recognition of textual documents. In this paper, we propose that n-grams can be extended to the domain of graphical documents as well. This has the advantage of providing a regular, easily-maintainable knowledge representation for local constraints, specifically symbol-interaction knowledge, in the graphical domain. To extend the definition of an n-gram from the textual to the graphical domain, we must resolve how to handle directional considerations, how to treat the variety of relations that can occur between image primitives, and how to treat multiple neighbours. We have implemented a prototype system for the application of n-gram knowledge to the recognition of sketch maps. Early results are encouraging. We look forward to further refining this system. N-grams appear to be a promising tool for representing spatial constraints in a form that is useful to the recognition of graphical documents.