Machine Vision and Applications

, Volume 6, Issue 2–3, pp 69–82 | Cite as

Sparse-pixel recognition of primitives in engineering drawings

  • Dov Dori
  • Yubin Liang
  • Joseph Dowell
  • Ian Chai


Recognition of primitives in technical drawings is the first stage in their higher level interpretation. It calls for processing of voluminous scanned raster files. This is a difficult task if each pixel must be addressed at least once, as required by Hough transform or thinning-based methods. This work presents a set of algorithms that recognize drawing primitives by examining the raster file sparsely. Bars (straight line segments), arcs, and arrowheads are identified by the orthogonal zig-zag, perpendicular Bisector tracing, and self-supervised arrowhead recognition algorithms, respectively. The common feature of these algorithms is that rather than applying massive pixel addressing, they recognize the sought primitives by screening a carefully selected sample of the image and focusing attention on identified key areas. The sparse-pixel-based algorithms yield high quality recognition, as demonstrated on a sample of engineering drawings.

Key words

Engineering drawing understanding Document recognition Computer-Aided Design (CAD) CAD conversion Raster-to-vector Vectorization 


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  1. Antoine D, Collin S, Tombre C (1990) Analysis of technical documents. The REDRAW system. Pre-Proceedings of the IAPR Workshop on Syntactic and Structural Pattern Recognition, Murray Hill, NJ, pp 1–20Google Scholar
  2. Chai I, Dori D (1992) Extraction of text boxes from engineering drawings. Proceedings of the SPIE/IS&T Symposium on Electronic Imaging Science and Technology, Conference on Character Recognition and Digitizer Technologies. San Jose, Calif, 9–14 February 1992Google Scholar
  3. Chai I, Dori D (1992) Orthogonal zig-zag: an effcient method for extracting bars in engineering drawings. In: Arcelli C, Cordella LP, Sanniti diBaja (eds) Visual Form. Plenum, New York pp. 127–136Google Scholar
  4. Collin S, Colnet D (1991) Analysis of dimensions in mechanical engineering drawings. Proc. Machine Vision Applications. 105–108.Google Scholar
  5. Colin S, Vaxiviere P (1991) Recognition and use of dimensioning in digitized industrial drawings. Proceedings of the First International Conference on Document Analysis and Recognition. IEEE Computer Society, Saint Malo, FranceGoogle Scholar
  6. Conker RS (1988) Dual plane variation of the Hough transform for detecting non-concentric circles of different radii. Computer Vision, Graphics and Image Processing 43:115–132CrossRefGoogle Scholar
  7. Csink L (1989) On the recognition of elements appearing in a circuit diagram, Proceedings of the 2nd Hungarian AI Conference, BudapestGoogle Scholar
  8. Dori D (1989) A syntactic geometric approach to recognition of dimensions in engineering machine drawings. Computer Vision, Graphics Image Processing 47:1–21CrossRefGoogle Scholar
  9. Dori D (1991) Self-structural syntax-directed pattern recognition of dimensioning components in engineering drawings. In Baird HS, Bunke H, Yamamoto K (eds) Springer, Berlin Heidelberg New YorkGoogle Scholar
  10. Dori D (1992) Dimensioning analysis: a step towards automatic high level understanding of engineering drawings. Commun ACM October, pp 92–103CrossRefGoogle Scholar
  11. Fahn CS, Wang JF, Lee YL (1988) A topology-based component extractor for understanding electronic circuit diagrams. Computer Vision, Graphics Image Processing 44:119–138CrossRefGoogle Scholar
  12. Fukuda Y (1982) Primary algorithm for the understanding of logic circuit diagrams. Proc 6th ICPR, Munich, pp 706–709Google Scholar
  13. Furuta M, Kase N, Emori S (1984) Segmentation and recognition of symbols for handwritten piping and instrument diagram. Proc 7th ICPR, Montreal, pp 612–614Google Scholar
  14. Haralick RM, Shapiro L (1992) Computer and robot vision. Addison Wesley ReadingGoogle Scholar
  15. Harris JF, Kittler J, Llewellyn B, Preston G (1982) A modular system for interpreting binary pixel representation of linestructured data. In: Pattern recognition: theory and applications. D. Reidel, Dordrecht, pp 311–351Google Scholar
  16. Hunt DJ, Nolte LW (1988) Performance of the Hough transform and its realtionship to statistical signal detection theory. Computer Vision, Graphics Image Processing 43:221–238CrossRefGoogle Scholar
  17. Illingworth J, Kittler J (1987) The adaptive Hough transform. IEEE Trans Pattern Analysis Machine Intelligence 9:690–697Google Scholar
  18. Josep SH, Pridmore TP (1992) Knowledge directed interpretation of mechanical engineering drawings. IEEE Trans Pattern Analysis Machine Intelligence 14:928–940CrossRefGoogle Scholar
  19. Kasturi R, Bow ST, El-Masri W, Shah J, Gattiker JR, Mokate UB (1990) A system for interpretation of line drawings. IEEE Trans Pattern Analysis Machine Intelligence 12:987–991Google Scholar
  20. Kimme C, Ballard DH, Slansky J (1975) Finding circles by an array of accumulators. CACM 18:120–122zbMATHGoogle Scholar
  21. King AK (1988) An Expert system facilitates understanding the paper engineering drawings, Proc IASTED International Symposium Expert Systems Theory and Their Applications, Los Angeles. ACTA Press, Anaheim, pp 169–172Google Scholar
  22. Lin X, Shimotsuji S, Minoh M, Sakai T (1985) Efficient diagram understanding with characteristic pattern detection. Computer Vision, Graphics Image processing 30:84–106CrossRefGoogle Scholar
  23. Nagasami V, Langrana NA (1990) Engineering drawing processing and vectorization system. Computer Vision, Graphics Image Processing 49:379–397CrossRefGoogle Scholar
  24. O'Gorman L, Sanderson AC (1984) The converging squares algorithm: an efficient method for locating peaks in multidimensions. IEEE Trans Pattern Analysis Machine Intelligence 7:280–288CrossRefGoogle Scholar
  25. Preiss K (1984) Constructing the solid representation from engineering projections. Computers Graphics 8:381–389CrossRefGoogle Scholar
  26. Sato T, Tojo A (1982) Recognition and understanding of handdrawn diagams. Proc 6th ICPR, Munich, pp 674–677Google Scholar
  27. Smith B, Wellington J (1986) Initial graphics exchange specification (IGES), version 3.0. National Institute os Standards NSBIR 86-3359Google Scholar
  28. Takaji M, Konishi T, Yamada M (1982) Automatic digitizing and processing method for the printed circuit pattern drawings, Proc 6th ICPR, MunichGoogle Scholar
  29. Therrien C (1989) Decision estimation and classification. Wiley, New YorkzbMATHGoogle Scholar
  30. Tombre K, Vaxiviere P (1991) Structure, syntax and semantics in technical document recognition. Proc First International Conference on Document Analysis and Recognition, IEEE Computer Society, Saint Malo, FranceGoogle Scholar
  31. Wesley MA, Markowski G (1981) Fleshing out projections, IBM J Res Dev 26:934–953CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 1993

Authors and Affiliations

  • Dov Dori
    • 1
  • Yubin Liang
    • 2
  • Joseph Dowell
    • 2
  • Ian Chai
    • 2
  1. 1.Faculty of Industrial Engineering and ManagementTechnion, Israel Institute of TechnologyHaifaIsrael
  2. 2.Department of Computer ScienceUniversity of KansasLawrenceUSA

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