Drawing image understanding framework using state transition models

Abstract

A flexible drawing understanding system with state transition models is proposed. The drawing processor AI-Mudams (written in C) is used as the token extractor in the embodiment discussed. Given drawing images are converted efficiently to suitable geometrical primitives, such as contour vectors, core vectors, dots loops, or, in some cases, primitives with semantics (road line, or house etc.). The understanding system kernel is implemented in Prolog, and the geometrical evaluator is also prepared in C for checking basic geometrical situations, including shape, geometrical relations, and allocations. This understanding kernel accepts the individual state transition rules corresponding to individual drawing images and recognition targets and realizes understanding in the form of bottom-up and top-down state transition. Experiments on different types of drawings reveal that the framework is flexible and effective for various kinds of drawing image.<>