Management of graphical symbols in a CAD environment: A neural network approach

Yang, Der-Shung; Webster, Julie L.; Renmdell, L.A.; Garrett, James H.; Shaw, D. G.

doi:10.1109/tai.1993.633967

Cited by 4 publications

(1 citation statement)

References 5 publications

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“…Similarly, Wenyin et al [118] made use of attributed graphs to represent graphics, where vertices represent the lines that compose the symbol and edges denote the kind of interaction between vectors. Furthermore, an advantage obtained from graphs as feature representations is the Statistical-based Bitmap [21,123] Pixel intensity [24] Others [49] Structural based Line vectors [15,52,53,83,99,118,120] Geometrical primitives [31,32,34,41,48,56,93,129,132] Contour [7,126] Moments [67] Others [2] Hybrid [75] capability of refining the features for a class of symbols. Such is the case presented by Jiang et al [63], where the prototype symbol of a class was calculated from a set of distorted symbols by extracting the features of all symbols, representing them as graphs, and applying a genetic algorithm to find the median graph.…”

Section: Feature Representationmentioning

confidence: 99%

New trends on digitisation of complex engineering drawings

Moreno‐García

Elyan

Jayne

2018

Neural Comput & Applic

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Engineering drawings are commonly used across different industries such as oil and gas, mechanical engineering and others. Digitising these drawings is becoming increasingly important. This is mainly due to the legacy of drawings and documents that may provide rich source of information for industries. Analysing these drawings often requires applying a set of digital image processing methods to detect and classify symbols and other components. Despite the recent significant advances in image processing, and in particular in deep neural networks, automatic analysis and processing of these engineering drawings is still far from being complete. This paper presents a general framework for complex engineering drawing digitisation. A thorough and critical review of relevant literature, methods and algorithms in machine learning and machine vision is presented. Real-life industrial scenario on how to contextualise the digitised information from specific type of these drawings, namely piping and instrumentation diagrams, is discussed in details. A discussion of how new trends on machine vision such as deep learning could be applied to this domain is presented with conclusions and suggestions for future research directions.

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Section: Feature Representationmentioning

confidence: 99%

New trends on digitisation of complex engineering drawings

Moreno‐García

Elyan

Jayne

2018

Neural Comput & Applic

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Symbol Recognition: Current Advances and Perspectives

Lladós¹,

Valveny²,

Sánchez³

et al. 2002

Lecture Notes in Computer Science

102

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Abstract. The recognition of symbols in graphic documents is an intensive research activity in the community of pattern recognition and document analysis. A key issue in the interpretation of maps, engineering drawings, diagrams, etc. is the recognition of domain dependent symbols according to a symbol database. In this work we first review the most outstanding symbol recognition methods from two different points of view: application domains and pattern recognition methods. In the second part of the paper, open and unaddressed problems involved in symbol recognition are described, analyzing their current state of art and discussing future research challenges. Thus, issues such as symbol representation, matching, segmentation, learning, scalability of recognition methods and performance evaluation are addressed in this work. Finally, we discuss the perspectives of symbol recognition concerning to new paradigms such as user interfaces in handheld computers or document database and WWW indexing by graphical content.

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Graphic symbol recognition: An overview

Chhabra

1998

Graphics Recognition Algorithms and Systems

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Management of graphical symbols in a CAD environment: A neural network approach

Cited by 4 publications

References 5 publications

New trends on digitisation of complex engineering drawings

New trends on digitisation of complex engineering drawings

Symbol Recognition: Current Advances and Perspectives

Graphic symbol recognition: An overview

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