Recognition of logic diagrams by identifying loops and rectilinear polylines

Kim, S.H.; Suh, Jung-Yul; Kim, J.H.

doi:10.1109/icdar.1993.395717

Cited by 19 publications

(16 citation statements)

References 5 publications

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“…A lot of contributions can be found in the literature [12,36,13,51,26,14,15]. The understanding and validation of electrical schematics and its conversion to an electronic format has become through the years a prototypical graphics recognition application.…”

Section: Logic Circuit Diagramsmentioning

confidence: 99%

“…Feature extraction must be robust enough to reduce feature variability due to noise and distortion. Moment invariants are another kind of features which have also been applied to symbol recognition, Both the regular moments [51,43] and the moments of Zernike [53] have been used. Moment invariants are easy to compute, they have relation with geometric properties, such as the center of gravity, the axes of inertia, etc, and they can be made invariant to affine transformations.…”

Section: Statistical Symbol Recognitionmentioning

confidence: 99%

“…Features are taken from the representation of the image in the transformation space. Image transforms which have been used in symbol recognition include Fourier transform [51,67], Fourier-Mellin transform [53] or special transforms to get signatures from the image [60].…”

Section: Statistical Symbol Recognitionmentioning

confidence: 99%

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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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Section: Logic Circuit Diagramsmentioning

confidence: 99%

Section: Statistical Symbol Recognitionmentioning

confidence: 99%

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Symbol Recognition: Current Advances and Perspectives

Lladós¹,

Valveny²,

Sánchez³

et al. 2002

Lecture Notes in Computer Science

102

View full text Add to dashboard Cite

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“…Recent work has emphasized the need for systems that can be adapted to different types of drawings, even though experimental results are presented for only one domain [7], [9]. It is not clear how these systems would adapt to more complicated drawings in other domains.…”

Section: Comparison With Prior Workmentioning

confidence: 99%

“…Most drawing analysis systems do not segment symbols from CLs before the understanding phase. Recent systems have used the approach of expanding from identified symbol loops to predetermined surrounding region to search for symbols [5], [7], [9], [16]. Loop-free symbols have been identified with the help of surrounding text [7] or by feature points and line tracing [16].…”

Section: Symbol Segmentationmentioning

confidence: 99%

A system for recognizing a large class of engineering drawings

Yu¹,

Samal

Seth

1997

IEEE Trans. Pattern Anal. Machine Intell.

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Abstract-We present a system for recognizing a large class of engineering drawings characterized by alternating instances of symbols and connection lines. The class includes domains such as flowcharts, logic and electrical circuits, and chemical plant diagrams. The output of the system, a netlist identifying the symbol types and interconnections, may be used for design simulation or as a compact portable representation of the drawing. The automatic recognition task is divided into two stages: 1) Domainindependent rules are used to segment symbols from connection lines in the drawing image that has been thinned, vectorized, and preprocessed in routine ways. 2) A drawing understanding subsystem works in concert with a set of domain-specific matchers to classify symbols and correct errors automatically. A graphical user interface is provided to correct residual errors interactively and to log data for reporting errors objectively. The system has been tested on a database of 64 printed images drawn from text books and handbooks in different domains and scanned at 150 and 300 dpi resolution.Index Terms-Symbolic drawings, flow diagrams, segmentation and labeling, domain independence, automatic and interactive error correction.

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