DocumentCode
1378072
Title
A system for recognizing a large class of engineering drawings
Author
Yu, Yuhong ; Samal, Ashok ; Seth, Sharad C.
Author_Institution
Lucent Technol. Inc., Naperville, IL, USA
Volume
19
Issue
8
fYear
1997
fDate
8/1/1997 12:00:00 AM
Firstpage
868
Lastpage
890
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) domain-independent 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
Keywords
engineering computing; graphical user interfaces; image classification; image segmentation; automatic recognition; chemical plant diagrams; connection lines; design simulation; domain-independent rules; domain-specific matchers; drawing understanding subsystem; electrical circuits; engineering drawings; flowcharts; graphical user interface; logic circuits; netlist; portable representation; symbols; Character recognition; Chemicals; Circuit simulation; Engineering drawings; Error correction; Flowcharts; Image recognition; Image segmentation; Integrated circuit interconnections; Logic circuits;
fLanguage
English
Journal_Title
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher
ieee
ISSN
0162-8828
Type
jour
DOI
10.1109/34.608290
Filename
608290
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