Topic
String grammar
About: String grammar is a research topic. Over the lifetime, 42 publications have been published within this topic receiving 678 citations.
Papers
Journal Article•
TL;DR: In this paper, the authors introduce instance-generating graph grammars for creating instances of meta models, thereby overcoming the main limitation of the meta modeling approach for defining languages.
Abstract: Meta modeling is a wide-spread technique to define visual languages, with the UML being the most prominent one. Despite several advantages of meta modeling such as ease of use, the meta modeling approach has one disadvantage: It is not constructive i. e. it does not offer a direct means of generating instances of the language. This disadvantage poses a severe limitation for certain applications. For example, when developing model transformations, it is desirable to have enough valid instance models available for large-scale testing. Producing such a large set by hand is tedious. In the related problem of compiler testing, a string grammar together with a simple generation algorithm is typically used to produce words of the language automatically. In this paper, we introduce instance-generating graph grammars for creating instances of meta models, thereby overcoming the main deficit of the meta modeling approach for defining languages.
104 citations
14 Jun 2006
TL;DR: This paper introduces instance-generating graph grammars for creating instances of meta models, thereby overcoming the main deficit of the meta modeling approach for defining languages.
Abstract: Meta modeling is a wide-spread technique to define visual languages, with the UML being the most prominent one. Despite several advantages of meta modeling such as ease of use, the meta modeling approach has one disadvantage: It is not constructive i. e. it does not offer a direct means of generating instances of the language. This disadvantage poses a severe limitation for certain applications. For example, when developing model transformations, it is desirable to have enough valid instance models available for large-scale testing. Producing such a large set by hand is tedious. In the related problem of compiler testing, a string grammar together with a simple generation algorithm is typically used to produce words of the language automatically. In this paper, we introduce instance-generating graph grammars for creating instances of meta models, thereby overcoming the main deficit of the meta modeling approach for defining languages.
104 citations
Patent•
10 Dec 1992TL;DR: In this article, an image recognition system using an imaging model employing a 2-dimensional finite state automaton corresponding to a regular string grammar is presented. But the model is not suitable for document image recognition, and it cannot handle a wider variety of image types.
Abstract: An image recognition system, in particular for document image recognition, using an imaging model employing a 2-dimensional finite state automaton corresponding to a regular string grammar. This approach is not only less computationally intensive than previous grammar-based approaches to document image recognition, but also can handle a wider variety of image types. Features of the imaging model include a sidebearing model of glyph positioning, an image decoder based on linear scheduling theory for regular interative algorithms, the combining of overlapping image sub-regions, and a least-squares estimation procedure for measuring character parameters from character samples in the image.
54 citations
TL;DR: The notions introduced in the paper are useful for researches in less restricted edNLC-graph Grammars, for example grammars analogical to context-free string grammARS.
39 citations
TL;DR: A new syntactic model, called pure two-dimensional (2D) context-free grammar (P2DCFG) is introduced based on the notion of pure context- free string grammar, and the rectangular picture generative power of this 2D grammar model is investigated.
39 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2020 | 1 |
| 2019 | 4 |
| 2017 | 2 |
| 2016 | 2 |
| 2015 | 3 |
| 2014 | 2 |