An automatic circuit diagram reader with loop-structure-based symbol recognition

Okazaki, Akio; Kondo, Takashi; Mori, Kenichi; Tsunekawa, Shou; Kawamoto, Eiji

doi:10.1109/34.3898

Cited by 79 publications

(30 citation statements)

References 9 publications

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“…The system combines domain-independent algorithms, including segmentation and symbol classification algorithms, and domain-specific knowledge, for example a symbol library, in the processing of graphics. Okazaki et al [30] proposed a loop-structure-based two-phase symbol recognition method for reading logic circuit diagrams. Blostein et al [2] summarized various approaches to diagram recognition.…”

Section: Image Analysismentioning

confidence: 99%

Automated analysis of images in documents for intelligent document search

Lü

Kataria

Brouwer

et al. 2009

IJDAR

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Authors use images to present a wide variety of important information in documents. For example, two-dimensional (2-D) plots display important data in scientific publications. Often, end-users seek to extract this data and convert it into a machine-processible form so that the data can be analyzed automatically or compared with other existing data. Existing document data extraction tools are semi-automatic and require users to provide metadata and interactively extract the data. In this paper, we describe a system that extracts data from documents fully automatically, completely eliminating the need for human intervention. The system uses a supervised learning-based algorithm to classify figures in digital documents into five classes: photographs, 2-D plots, 3-D plots, diagrams, and others. Then, an integrated algorithm is used to extract numerical data from data points and lines in the 2-D plot images along with the axes and their labels, the data symbols in the figure's legend and their associated labels. We demonstrate that the proposed system and its component algorithms are effective via an empirical evaluation. Our data extraction system has the potential to be a vital component in high volume digital libraries.

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Section: Image Analysismentioning

confidence: 99%

Automated analysis of images in documents for intelligent document search

Lü

Kataria

Brouwer

et al. 2009

IJDAR

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“…The many advantage s of converting hard-copy line drawings into e le ctronic format have be e n e nume rated by se veral authors and many differe nt me thods proposed in orde r to do so e .g., Ble y, 1984; . Filipski & Flandrena, 1992;Kasturi e t al., 1990;O kazaki e t al., 1988 . A ne w ge ne ral-purpose m e thod for the recognition of graphic symbols is pre se nte d he re .…”

mentioning

confidence: 96%

A General-Purpose Procedure for Complex Graphic Symbol Recognition

Schettini¹

1996

Cybernetics and Systems

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“…Most of the earlier drawing analysis systems targeted a single domain of drawings [1], [4], [6], [16]. 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].…”

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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An automatic circuit diagram reader with loop-structure-based symbol recognition

Cited by 79 publications

References 9 publications

Automated analysis of images in documents for intelligent document search

Automated analysis of images in documents for intelligent document search

A General-Purpose Procedure for Complex Graphic Symbol Recognition

A system for recognizing a large class of engineering drawings

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