A parsing methodology for the implementation of visual systems

Schürr

2005 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC'05)

Section: Model Transformations In Model Driven Developmentmentioning

confidence: 99%

Benchmarking for Graph Transformation

Schürr

2005 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC'05)

IEEE Trans. Pattern Anal. Machine Intell.

“…This strategy allows us to exploit the left-to-right reading order of mathematical notation, thereby analyzing layout efficiently without backtracking. Similar layout analysis techniques have been used in applications including parsing of visual languages [13] and recognition of mathematical notation [14], [15]. One of our contributions is to generalize the technique to make it robust enough to handle the irregular symbol layouts present in handwritten expressions (Fig.…”

Section: Mathematics Recognition Via Tree Transformationmentioning

confidence: 99%

“…Extensive research went into defining the search functions Start and Hor, discussed below. The inspiration for this directed search came from the linear positional grammar work of Costagliola et al [13], where syntax-driven linear scanning of the input is used to parse visual languages. The directionality present in mathematical notation made it possible for us to adapt these ideas for use in the Layout pass.…”

Section: Layout Passmentioning

confidence: 99%

Recognizing mathematical expressions using tree transformation

Zanibbi

Blostein

Cordy

2002

204

111

Abstract-We describe a robust and efficient system for recognizing typeset and handwritten mathematical notation. From a list of symbols with bounding boxes the system analyzes an expression in three successive passes. The Layout Pass constructs a Baseline Structure Tree (BST) describing the two-dimensional arrangement of input symbols. Reading order and operator dominance are used to allow efficient recognition of symbol layout even when symbols deviate greatly from their ideal positions. Next, the Lexical Pass produces a Lexed BST from the initial BST by grouping tokens comprised of multiple input symbols; these include decimal numbers, function names, and symbols comprised of nonoverlapping primitives such as "=". The Lexical Pass also labels vertical structures such as fractions and accents. The Lexed BST is translated into L A T E X. Additional processing, necessary for producing output for symbolic algebra systems, is carried out in the Expression Analysis Pass. The Lexed BST is translated into an Operator Tree, which describes the order and scope of operations in the input expression. The tree manipulations used in each pass are represented compactly using tree transformations. The compiler-like architecture of the system allows robust handling of unexpected input, increases the scalability of the system, and provides the groundwork for handling dialects of mathematical notation.

Diagrammatic Representation and Inference

“…Vlcc [7] and Penguins [5] are examples of the approaches of the approaches which do not depend on an internal graph-like model, but parse the diagram directly. Whereas Vlcc makes use of Positional Grammars, Penguins uses Constraint Multiset Grammars for specifying diagram syntax.…”

Section: Related Workmentioning

confidence: 99%

Structure, Abstraction, and Direct Manipulation in Diagram Editors

Köth

Minas

2002

Abstract. Editors for visual languages should be as simple and convenient to use as possible; at the same time, programmers should be able to create such editors without prohibitive effort. We discuss the benefits that can be gained from combining the following aspects in an editorgenerator approach:-direct-manipulation editing (as in drawing programs) -structure-based editing (as in common diagram tools) -structural analysis and a common formal model As a major practical example, we present an editor for UML class diagrams. We show that direct-manipulation editing capabilities can enhance the usability of such an editor in comparison to standard tools. A further improvement is obtained by including selective abstraction features similar to the well-known "fisheye-viewing" and "semantic zooming" paradigms. We show that the proposed generator architecture provides an excellent base for implementing such features. The resulting technique can be applied to a wide range of different diagram languages; in contrast to other general solutions, it takes into account the abstract structure and specific abstraction features of the individual languages.