Efficient Analysis of Complex Diagrams using Constraint-Based Parsing

TL;DR

This paper presents a constraint grammar-based method for efficiently parsing complex diagrams, including graphs and biological diagrams, by leveraging set types and spatial indexing, enabling retargeting to various domains.

Contribution

It introduces a novel constraint grammar approach with set types and spatial indexing, achieving efficient diagram parsing adaptable to multiple diagram types.

Findings

Parsed a complex data graph in 35 seconds

Successfully analyzed biological and automata diagrams

Demonstrated domain retargetability of the parsing system

Abstract

This paper describes substantial advances in the analysis (parsing) of diagrams using constraint grammars. The addition of set types to the grammar and spatial indexing of the data make it possible to efficiently parse real diagrams of substantial complexity. The system is probably the first to demonstrate efficient diagram parsing using grammars that easily be retargeted to other domains. The work assumes that the diagrams are available as a flat collection of graphics primitives: lines, polygons, circles, Bezier curves and text. This is appropriate for future electronic documents or for vectorized diagrams converted from scanned images. The classes of diagrams that we have analyzed include x,y data graphs and genetic diagrams drawn from the biological literature, as well as finite state automata diagrams (states and arcs). As an example, parsing a four-part data graph composed of 133 primitives required 35 sec using Macintosh Common Lisp on a Macintosh Quadra 700.