Advertisement

Symbol and Shape Recognition

  • L. P. Cordella
  • M. Vento
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1941)

Abstract

The different aspects of a process for recognizing symbols in documents are considered and the techniques that have been most commonly used during the last ten years, in the different application fields, are reviewed. Methods used in the representation, description and classification phases are shortly discussed and the main recognition strategies are mentioned. Some of the problems that appear still open are proposed to the attention of the reader.

Keywords

Graph Match Musical Score Technical Drawing Polygonal Approximation Pattern Recognition Letter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    K. Chhabra: Graphic symbol recognition: an overview. Proc. GREC’97, Nancy, France (1997) 244–252Google Scholar
  2. 2.
    D Blostein: General diagram recognition methodologies. Proc. GREC’95, University Park, PA (1995) 200–212Google Scholar
  3. 3.
    K. Tombre: Analysis of engineering drawings: state of the art and challenges. Proc. GREC’97 (1997) 54–61Google Scholar
  4. 4.
    J. F. Arias and R. Kasturi: Recognition of Graphical Objects for Intelligent Interpretation of Line Drawings, in Aspects of visual form processing. C. Arcelli, L. P. Cordella and G. Sanniti di Baja Eds, World Scientific (1994) 11–31Google Scholar
  5. 5.
    L. O. Gorman: Basic Techniques and Symbol-Level Recognition-An overview. in Graphics recognition-Methods and applications, (Proc. GREC’95) Vol. LNCS 1072, Springer (1996) 1–12Google Scholar
  6. 6.
    K. Tombre, C. Ah-Soon, P. Dosch, A. Habed and G. Masini: Stable. Robust and Off-the-Shelf Methods for Graphics Recognition, Proc. ICPR’98 (1998) 406–408Google Scholar
  7. 7.
    Y. Y. Tang, S. W. Lee and C. Y. Suen, Automatic Document Processing: a Survey. Pattern Recognition Vol. 29 N. 12 (1996) 1931–1952CrossRefGoogle Scholar
  8. 8.
    T. Kanungo, R. Haralick, and D. Dori: Understanding engineering drawings: A survey. Proc. First Int. Workshop on Graphics Recognition, The Pennsylvania State University, PA, USA (1995) 119–130Google Scholar
  9. 9.
    D. Blostein and H. Baird: A critical survey of music image analysis. in Structured document image analysis, H. Baird, H. Bunke, K. Yamamoto Eds, Springer (1992) 405–434Google Scholar
  10. 10.
    C. S. Fahn, J. F. Wang, J. Y. Lee: A topology based component extractor for understanding electronic circuit diagrams. CVGIP, 44 (1988) 119–138Google Scholar
  11. 11.
    B. Messmer and H. Bunke: Automatic learning and recognition of graphical symbols in engineering drawings. In R. Kasturi and K. Tombre, editors, Graphics Recognition: Methods and Applications, Selected Papers from First International Workshop on Graphics Recognition (1995) Vol. LNCS 1072 123-134. Springer, Berlin (1996)Google Scholar
  12. 12.
    A. D. Ventura and R. Schettini: Graphic symbol recognition using a signature technique. Proc. 12th ICPR, Jerusalem Vol. 2 (1994) 533–535Google Scholar
  13. 13.
    B. Yu: Automatic understanding of symbol-connected diagrams. Proc. of Third IAPR International Conference on Document Analysis and Recognition, ICDAR’95, pages 803–806, Montreal, Canada, August 1995Google Scholar
  14. 14.
    Okazaki, T. Kondo, K. Mori, S. Tsunekawa, and E. Kawamoto: An automatic circuit diagram reader with loop-structure-based symbol recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol.10 N. 3 (1988) 331–341CrossRefGoogle Scholar
  15. 15.
    R. Kasturi et al.: A System for Interpretation of Line Drawings. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 12 N. 10 (1990) 978–992CrossRefGoogle Scholar
  16. 16.
    A. Hamada: A new system for the analysis of schematic diagrams. In Proc. of the Second International Conference on Document Analysis and Recognition-ICDAR’93, pages 369–372, Tsukuba Science City, Japan, October 1993Google Scholar
  17. 17.
    T. Cheng, J. Khan, H. Liu and D. Y. Y. Yun: A Symbol Recognition System, Proc. ICDAR’93 (1993) 918–921Google Scholar
  18. 18.
    S. Kim, J. Suh, and J. Kim: Recognition of logic diagrams by identifying loops and rectilinear polylines. In Proc. of Second International Conference on Document Analysis and Recognition-ICDAR’ 93, pages 349–352, Tsukuba Science City, Japan, October 1993Google Scholar
  19. 19.
    Y. H. Yu, A. Samal, and S. Seth: Isolating symbols from connection lines in a class of engineering drawings. Pattern Recognition Vol. 27 N. 3 (1994) 391–404CrossRefGoogle Scholar
  20. 20.
    Y. H. Yu, A. Samal, and S. Seth, A system for recognizing a large class of engineering drawings, Proc. ICDAR’95 (1995) 791–794Google Scholar
  21. 21.
    K. Abe, Y. Azumatani, M. Kukouda, and S. Suzuki: Discrimination of symbols, lines, and characters in flow chart recognition. Proc. 8th ICPR, Paris, France (1986) 1071–1074Google Scholar
  22. 22.
    F. C. A. Groen, A. C. Sanderson, and J. F. Schlag: Symbol recognition in electrical diagrams using probabilistic graph matching. Pattern Recognition Letters Vol. 3 N. 5 (1985) 343–350CrossRefGoogle Scholar
  23. 23.
    B. Pasternak: The role of taxonomy in drawing interpretation. Proc. ICDAR’95 (1995) 799–802Google Scholar
  24. 24.
    P. Vaxiviere and K. Tombre, Celesstin: CAD conversion of mechanical drawings. Computer Vol. 25,7 (1992) 46–53CrossRefGoogle Scholar
  25. 25.
    Y. Aoki, A. Shio, H Arai, K. Odaka: A prototype system for interpreting hand-sketched floor plans. Proc. ICPR’96 (1996) 747–751Google Scholar
  26. 26.
    J. Llados, J. Lopez-Krahe, E. Marti.: Hand-drawn document understanding using the straight line Hough transform and graph matching. Proc. ICPR’96 (1996) 497–501Google Scholar
  27. 27.
    J. Llados, G. Sanchez, E. Marti.: A string based method to recognize symbols and structural textures in architectural plans. Proc. GREC’97 (1997) 287–294Google Scholar
  28. 28.
    Ah-Soon, Symbol detection in architectural drawings. Proc. GREC’97 (1997) 280–286Google Scholar
  29. 29.
    J. F. Arias, C. Lai, S. Surya: R. Kasturi and A. Chhabra, Interpretation of telephone system manhole drawings. PRL, 16 (1995) 355–369Google Scholar
  30. 30.
    J. F. Arias, R. Kasturi and A. Chhabra: Efficient techniques for telephone company line drawing interpretation. Proc. ICDAR’95, Montreal (1995) 795–798Google Scholar
  31. 31.
    M. Furuta, N. Kase, and S. Emori: Segmentation and recognition of symbols for handwritten piping and instrument diagram. Proc. 7th ICPR, Montreal, Canada (1984) IEEE Publ. 84CH2046-1, 626–629Google Scholar
  32. 32.
    O. D. Trier, T. Taxt, and A. K. Jain: Recognition of digits in hydrographic maps-binary versus topographic analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 19 N. 4 (1997) 399–404CrossRefGoogle Scholar
  33. 33.
    R. Mullot, J.-M. Ogier, F. Brisepierre, Y. Lecourtier and M. F. Collinas: An Original Approach for Extracting Circular Shapes from Technical Charts. Proc. ICPR’96 (1996) 813–817Google Scholar
  34. 34.
    G. Myers, P. Mulgaonkar, C. Chen, J. DeCurtins, and E. Chen: Verification-based approach for automated text and feature extraction. in Graphics Recognition: Methods and Applications, R. Kasturi and K. Tombre Eds, Selected Papers from First Int. Workshop on Graphics Recognition Vol. LNCS 1072 190–203, Springer, Berlin (1996)Google Scholar
  35. 35.
    J. den Hartog, T. ten Kate and J. Gebrands: Knowledge based segmentation for automatic map interpretation. in Graphics Recognition: Methods and Applications, R. Kasturi and K. Tombre Eds, Selected Papers from First Int. Workshop on Graphics Recognition (1995) Vol. LNCS 1072 159–178, Springer, Berlin (1996)Google Scholar
  36. 36.
    De Stefano, F. Tortorella, and M. Vento: Morphological functions for symbol recognition on geographic maps. Proc. ICRCV’92,Singapore (1992) CV-21.3.1-5Google Scholar
  37. 37.
    C. De Stefano, F. Tortorella, and M. Vento: An entropy based method for extracting robust binary templates, Machine Vision and Applications Vol. 8 N. 3, 1995, 173–178CrossRefGoogle Scholar
  38. 38.
    H. Samet and A. Soffer: Legend-driven geographic symbol recognition system. Proc. 12th ICPR, Jerusalem Vol. 2 (1994) 350–355Google Scholar
  39. 39.
    Soffer and H. Samet: Negative Shape Features for Images Databases Consisting of Geographical Symbols. Proc. 3rd International Workshop on Visual Forms, World Scientific (1997) 569–581Google Scholar
  40. 40.
    L. Boatto, V. Consorti, M. Del Buono, V. Eramo, A. Esposito, F. Melcarne, and M. Meucci: Detection and Separation of Symbols Connected to Graphics in Line Drawings. Proc. 11th ICPR Vol. II, The Hague, The Netherlands (1992) IEEE Comp. Soc. Press (1992) 545–548Google Scholar
  41. 41.
    L. Boatto et al.: An interpretation system for land register maps. Computer, July 1992, 25–33Google Scholar
  42. 42.
    H. Yamada, K. Yamamoto, and K. Hosokawa: Directional mathematical morphology and reformalized Hough transformation for the analysis of topographic maps. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 15 N. 4 (1993) 380–387CrossRefGoogle Scholar
  43. 43.
    K. Yamamoto, H. Yamada, and S. Muraki: Symbol recognition and surface reconstruction from topographic map by parallel method. Proc. 2nd Int. Conf. on Document Analysis and Recognition (ICDAR’ 93) Tsukuba Science City, Japan (1993) 914–917Google Scholar
  44. 44.
    O. A. Morean and R. Kasturi: Symbol identification in geographical maps. Proc. 7th ICPR, Montreal, Canada (1984) IEEE Publ. 84CH2046-1, 966–967Google Scholar
  45. 45.
    E. Reiher et al.: A System for Efficient and Robust Map Symbol Recognition. Proc. ICPR’96 (1996) 783–787Google Scholar
  46. 46.
    J. M. Ogier, R. Mullot, J. Labiche and Y. Lecoutier: An Interpretation Device Can Not Be Reliable Without Any Semantic Coherency Analysis of the Interpreted Objects-Application to French Cadastral Maps. Proc. ICDAR’97, Ulm (1997) 532-Google Scholar
  47. 47.
    R. Randriamahefa, J. Cocquerez, C. Fluhr, F. Pepin, and S. Philipp: Printed music recognition. In Proc. Second IAPR International Conference on Document Analysis and Recognition, ICDAR’ 93, pages 898–901, Tsukuba Science City, Japan, October 1993Google Scholar
  48. 48.
    K. Todd Reed and J. R. Parker: Automatic computer recognition of printed music. Proc. ICPR’96 (1996) 803, 807Google Scholar
  49. 49.
    J. Armand: Musical score recognition: A hierarchical and recursive approach. In Proc. Second IAPR International Conference on Document Analysis and Recognition, ICDAR’ 93, pages 906–909, Tsukuba Science City, Japan, October 1993Google Scholar
  50. 50.
    H. Miyao and Y. Nakano: Note symbol extraction for printed piano score using neural networks. IEICE Transactions on Inf.& Syst., E79-D(5):548-554, May 1996Google Scholar
  51. 51.
    I. Leplumey, J. Camillerapp, G. Lorette: A robust detector for music staves. Proc. ICDAR’93 (1993) 902–905Google Scholar
  52. 52.
    D. S. Doermann, E. Rivlin and I Weiss: Logo recognition using geometric invariants. Proc. ICDAR’93 (1993) 894–897Google Scholar
  53. 53.
    M. Corvi, E. Ottaviani: Multiresolution logo recognition. in Aspects of visual form processing, World Scientific, (Proc. 3rd Int. Workshop on Visual Form, Capri, Italy) (1997) 110–118Google Scholar
  54. 54.
    F. Cesarini, M. Gori, S Marinai, G. Soda: A Hybrid system for locating and recognizing low level graphic items. in Graphics Recognition: methods and applications, (papers from GREC’95) Vol. LCNS1072, Springer (1996) 135–147Google Scholar
  55. 55.
    P. Suda, C. Bridoux, B. Kammerer, G. Maderlechner: Logo and word matching using a general approach to signal registration. Proc. ICDAR’97 (1997) 61–65Google Scholar
  56. 56.
    N. A. Murshed, F. Bortolozzi: Recognition of electronic components in circuit layouts using the fuzzy ARTMAP neural network. Proc. GREC’97 (1997) 267–272Google Scholar
  57. 57.
    L. Wenyin, D. Dori: Generic graphics recognition of engineering drawing objects. Proc. GREC’97 (1997) 70–80Google Scholar
  58. 58.
    M. Minoh, T. Munetsugu, and K. Ikeda: Extraction and Classification of Graphical Symbol Candidates Based on Perceptual Organization. Proc. 11th ICPR Vol. II, The Hague, The Netherlands (1992) IEEE Comp. Soc. Press (1992) 234–237Google Scholar
  59. 59.
    Z. Xeujun, L. Xinyu, Z. Shengling, P. Baochang and Y. Y. Tang: On-Line Recognition of Handwritten Mathematical Symbols. Proc. ICDAR’97, Ulm (1997) 645–648Google Scholar
  60. 60.
    Y. J. Zhang: A survey on evaluation methods for image segmentation. Pattern Recognition Vol. 29 N. 8 (1996) 1335–1346CrossRefGoogle Scholar
  61. 61.
    P. K. Sahoo, S. Soltani, A. K. C. Wong and Y. C. Chen: A survey of thresholding techniques. CVGIP Vol.41 (1988) 233–260Google Scholar
  62. 62.
    L. Lam, S. W. Lee and C. Y. Suen: Thinning methodologies-A comprehensive survey. IEEE Trans. PAMI Vol.14, n.9 (1992) 869–887Google Scholar
  63. 63.
    A. Rosenfeld: Axial Representations Of Shape. Computer Vision Graphics and Image ProcessingVol. 33 156–173 (1986)CrossRefGoogle Scholar
  64. 64.
    R. M. Brown, T. H. Fay, and C. L. Walker: Handprinted symbol recognition system, Pattern Recognition. Vol. 21 (1988) 91–118CrossRefGoogle Scholar
  65. 65.
    R. Plamondon et al.: Validation of preprocessing algorithms: a methodology and its application to the design of a thinning algorithm for handwritten characters. Proc. ICDAR’93 Tsukuba, Japan (1993) 287–290Google Scholar
  66. 66.
    V. K. Govindan and A. P. Shivaprasad: A pattern adaptive thinning algorithm. Pattern Recognition Vol.20 N. 6 623–637, (1987)Google Scholar
  67. 67.
    X. Li and A. Basu: Variable-resolution character thinning. Pattern Recognition Letters Vol.12 241–248 (1991)CrossRefGoogle Scholar
  68. 68.
    M. Frucci and A. Marcelli: Line Representation of Elongated Shapes. Lecture Notes in Computer Science, Berlin: Springer-Verlag Vol. 970 643–648 (1995)Google Scholar
  69. 69.
    F. Leymaria and M. D. Levine: Simulating the grassfire transform using an active contour model. IEEE Trans. on PAMI Vol.14. no.1 56–75 (1992)Google Scholar
  70. 70.
    G. Dimauro, S. Impedovo and G. Pirlo: A new thinning algorithm based on controlled deletion of edge regions. IJPRAI 969–985Google Scholar
  71. 71.
    C. Chouinard and R. Plamondon: Thinning and Segmenting handwritten characters by line following. Machine Vision and Applications Vol. 5 185–197 (1992)CrossRefGoogle Scholar
  72. 72.
    D. Kalles and D. T. Morris: A novel and reliable thinning algorithm. IVC Vol.11 N. 9 588–603 (1993)Google Scholar
  73. 73.
    F. L. Bookstein: The line skeleton. CVIP Vol.11 123–137 (1979)Google Scholar
  74. 74.
    J. W. Brandt and V. R. Algazi: Continuous skeleton computation by Voronoi diagram. CVGIP:IU Vol. 55 N. 3 329–338 (1992)zbMATHCrossRefGoogle Scholar
  75. 75.
    A. Sirijani and G. R. Cross: On representation of a shape’s skeleton. Pattern Recognition Letters Vol.12 149–154 (1991)CrossRefGoogle Scholar
  76. 76.
    S. Lee and J. C. Pan: Off-line tracing and representation of signatures. IEEE Trans. on Systems, Man and Cybernetics Vol. SMC-22 N. 4 (1992) 755–771CrossRefGoogle Scholar
  77. 77.
    S. W. Lu and H. Xu: False stroke detection and elimination for character recognition. Pattern Recognition Letters Vol. 13, 745–755 (1992)CrossRefGoogle Scholar
  78. 78.
    G. Boccignone, A. Chianese, L. P. Cordella, A. Marcelli: Recovering dynamic information from static handwriting, Pattern Recognition. 26, 3 (1993) 409–418CrossRefGoogle Scholar
  79. 79.
    G. Boccignone, A. Chianese, L. P. Cordella, A. Marcelli: Using skeletons for OCR. in Progress in image analysis and processing, V. Cantoni et al. Eds., World Scientific (1990). 275–282Google Scholar
  80. 80.
    L. P. Cordella, A. Marcelli: An alternative approach to the performance evaluation of thinning algorithms for document processing applications. in Graphics Recognition: Methods and Applications, R. Kasturi and K. Tombre Eds Vol. LNCS 1072 190–203, Springer, Berlin (1996)Google Scholar
  81. 81.
    O. Hori and S. Tanigawa: Raster-To-Vector Conversion by Line Fitting Based on Contours and Skeletons. Proc. ICDAR’93 (1993) 353Google Scholar
  82. 82.
    L. A. Fletcher and R. Kasturi: A robust algorithm for text string separation from mixed text/graphics images. IEEE Trans. on PAMI Vol.10 N. 6 (1988) 910–918Google Scholar
  83. 83.
    D. Trier, A. K. Jain, and T. Taxt: Feature extraction methods for character recognition — a survey. Pattern Recognition Vol. 29 N. 4 641–662, Apr. 1996CrossRefGoogle Scholar
  84. 84.
    S. Loncaric: A Survey of Shape Analysis Techniques. Pattern Recognition Vol. 31 N. 8 (1998) 983–1001CrossRefGoogle Scholar
  85. 85.
    L. P. Cordella, P. Foggia, C. Sansone and M. Vento: Subgraph transformations for the inexact matching of attributed relational graphs. Computing, 12 (1998) 43–52MathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • L. P. Cordella
    • 1
  • M. Vento
    • 1
  1. 1.Dipartimento di Informatica e SistemisticaUniversita’ di Napoli “Federico II”NapoliItaly

Personalised recommendations