Advertisement

Symbol Recognition: Current Advances and Perspectives

  • Josep Lladós
  • Ernest Valveny
  • Gemma Sánchez
  • Enric Martí
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2390)

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.

Keywords

Graph Match Graph Grammar Current Advance Musical Score Graphical Content 
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]
    Blostein, D.: General diagram-recognition methodologies. In Kasturi, R., Tombre, K., eds.: Graphics Recognition: Methods and Applications. Springer, Berlin (1996) 106–122 Vol. 1072 of LNCS.Google Scholar
  2. [2]
    Chhabra, A.: Graphic symbol recognition: An overview. In Tombre, K., Chhabra, A., eds.: Graphics Recognition: Algorithms and Systems. Springer, Berlin (1998) 68–79 Vol. 1389 of LNCS.Google Scholar
  3. [3]
    Cordella, L., Vento, M.: Symbol and shape recognition. In Chhabra, A., Dori, D., eds.: Graphics Recognition: Recent Advances. Springer-Verlag, Berlin (2000) 167–182 Vol. 1941 of LNCS.CrossRefGoogle Scholar
  4. [4]
    Kasturi, R., Luo, H.: Research advances in graphics recognition: An update. In Murshed, N., Bortolozzi, F., eds.: Advances in Document Image Analysis, First Brazilian Symposium, BSDIA’97. Springer, Berlin (1997) 99–110 Vol. 1339 of LNCS.Google Scholar
  5. [5]
    Chen, Y., Langrana, N., Das, A.: Perfecting vectorized mechanical drawings. Computer Vision and Image Understanding 63 (1996) 273–286.CrossRefGoogle Scholar
  6. [6]
    Dori, D., Liu, W.: Sparse pixel vectorization: An algorithm and its performance evaluation. IEEE Trans. on PAMI 21 (1999) 202–215.Google Scholar
  7. [7]
    Nagasamy, V., Langrana, N.: Engineering drawing processing and vectorisation system. Computer Vision, Graphics and Image Processing 49 (1990) 379–397.CrossRefGoogle Scholar
  8. [8]
    Tombre, K., Ah-Soon, C., Dosch, P., Masini, G., Tabonne, S.: Stable and robust vectorization: How to make the right choices. In: Proceedings of Third IAPR Work. on Graphics Recognition. (1999) 3–16 Jaipur, India.Google Scholar
  9. [9]
    Phillips, I., Chhabra, A.: Empirical performance evaluation of graphics recognition systems. IEEE Trans. on PAMI 21 (1999) 849–870.Google Scholar
  10. [10]
    Chhabra, A., Phillips, I.: The second international graphics recognition contest-raster to vector conversion: A report. In Tombre, K., Chhabra, A., eds.: Graphics Recognition: Algorithms and Systems. Springer, Berlin (1998) 390–410 Vol. 1389 of LNCS.Google Scholar
  11. [11]
    Chhabra, A., Philips, I.: Performance evaluation of line drawing recognition systems. In: Proceedings of 15th. Int. Conf. on Pattern Recognition. Volume 4. (2000) 864–869 Barcelona, Spain.CrossRefGoogle Scholar
  12. [12]
    Groen, F., Sanderson, A., Schlag, F.: Symbol recognition in electrical diagrams using probabilistic graph matching. PRL 3 (1985) 343–350.Google Scholar
  13. [13]
    Jiang, X., Munger, A., Bunke, H.: Synthesis of representative graphical symbols by computing generalized median graph. In Chhabra, A., Dori, D., eds.: Graphics Recognition: Recent Advances. Springer-Verlag, Berlin (2000) 183–192 Vol. 1941 of LNCS.CrossRefGoogle Scholar
  14. [14]
    Kuner, P., Ueberreiter, B.: Pattern recognition by graph matching. combinatorial versus continuous optimization. Int. Journal of Pattern Recognition and Artificial Intelligence 2 (1988) 527–542.CrossRefGoogle Scholar
  15. [15]
    Lee, S.: Recognizing hand-written electrical circuit symbols with attributed graph matching. In Baird, H., Bunke, H., Yamamoto, K., eds.: Structured Document Analysis. Springer Verlag, Berlin (1992) 340–358.Google Scholar
  16. [16]
    Sato, T., Tojo, A.: Recognition and understanding of hand-drawn diagrams. In: Proceedings of 6th. Int. Conf. on Pattern Recognition. (1982) 674–677.Google Scholar
  17. [17]
    Messmer, B., Bunke, H.: Automatic learning and recognition of graphical symbols in engineering drawings. In Kasturi, R., Tombre, K., eds.: Graphics Recognition: Methods and Applications. Springer, Berlin (1996) 123–134 Vol. 1072 of LNCS.Google Scholar
  18. [18]
    Muller, S., Rigoll, G.: Engineering drawing database retrieval using statistical pattern spotting techniques. In Chhabra, A., Dori, D., eds.: Graphics Recognition: Recent Advances. Springer-Verlag, Berlin (2000) 246–255 Vol. 1941 of LNCS.CrossRefGoogle Scholar
  19. [19]
    Lladós, J., Sánchez, G., Martí, E.: A string-based method to recognize symbols and structural textures in architectural plans. In Tombre, K., Chhabra, A., eds.: Graphics Recognition: Algorithms and Systems. Springer, Berlin (1998) 91–103 Vol. 1389 of LNCS.Google Scholar
  20. [20]
    Valveny, E., Martí, E.: Hand-drawn symbol recognition in graphic documents using deformable template matching and a bayesian framework. In: Proceedings of 15th. Int. Conf. on Pattern Recognition. Volume 2. (2000) 239–242 Barcelona, Spain.CrossRefGoogle Scholar
  21. [21]
    Chang, M., Chen, S.: Deformed trademark retrieval based on 2d pseudo-hidden markov model. PR 34 (2001) 953–967.MATHGoogle Scholar
  22. [22]
    Cortelazzo, G., Mian, G., Vezzi, G., Zamperoni, P.: Trademark shapes descrition by string matching techniques. PR 27 (1994) 1005–1018.Google Scholar
  23. [23]
    Segen, J.: From features to symbols: Learning relational models of shape. In Simon, J., ed.: From Pixels to Features. Elsevier Science Publishers B. V. (North-Holland) (1989) 237–248.Google Scholar
  24. [24]
    Bunke, H.: Attributed programmed graph grammars and their application to schematic diagram interpretation. IEEE Trans. on PAMI 4 (1982) 574–582.MATHGoogle Scholar
  25. [25]
    Fahn, C., Wang, J., Lee, J.: A topology-based component extractor for understanding electronic circuit diagrams. IEEE Trans. on PAMI 2 (1989) 1140–1157.Google Scholar
  26. [26]
    Kiyko, V.: Recognition of objects in images of paper based line drawings. In: Proceedings of Third IAPR Int. Conf. on Document Analysis and Recognition, ICDAR’95. Volume 2., Montreal, Canada (1995) 970–973.CrossRefGoogle Scholar
  27. [27]
    Collin, S., Colnet, D.: Syntactic analysis of technical drawing dimensions. Int. Journal of Pattern Recognition and Artificial Intelligence 8 (1994) 1131–1148.CrossRefGoogle Scholar
  28. [28]
    Dori, D.: A syntactic/geometric approach to recognition of dimensions in engineering machine drawings. Computer Vision, Graphics and Image Processing 47 (1989) 271–291.CrossRefGoogle Scholar
  29. [29]
    Joseph, S., Pridmore, T.: Knowledge-directed interpretation of mechanical engineering drawings. IEEE Trans. on PAMI 14 (1992) 928–940.Google Scholar
  30. [30]
    Min, W., Tang, Z., Tang, L.: Using web grammar to recognize dimensions in engineering drawings. PR 26 (1993) 1407–1916.Google Scholar
  31. [31]
    Fahmy, H., Blostein, D.: A survey of graph grammars: Theory and applications. In: Proceedings of 12th. Int. Conf. on Pattern Recognition (a). (1994) 294–298 Jerusalem, Israel.Google Scholar
  32. [32]
    Sánchez, G., Lladós, J.: A graph grammar to recognize textured symbols. In: Proceedings of 6th Int. Conf. on Document Analysis and Recognition. (2001) Seattle, USA.Google Scholar
  33. [33]
    Kosmala, A., Lavirotte, S., Pottier, L., Rigoll, G.: On-Line Handwritten Formula Recognition using Hidden Markov Models and Context Dependent Graph Grammars. In: Proceedings of 5th Int. Conf. on Document Analysis and Recognition. (1999) 107–110 Bangalore, India.Google Scholar
  34. [34]
    Lavirotte, S., Pottier, L.: Optical formula recognition. In: Proceedings of 4th Int. Conf. on Document Analysis and Recognition. (1997) 357–361 Ulm, Germany.Google Scholar
  35. [35]
    Bley, H.: Segmentation and preprocessing of electrical schematics using picture grafs. Computer Vision, Graphics and Image Processing 28 (1984) 271–288.CrossRefGoogle Scholar
  36. [36]
    Habacha, A.: Structural recognition of disturbed symbols using discrete relaxation. In: Proceedings of 1st. Int. Conf. on Document Analysis and Recognition. (1991) 170–178 Saint Malo, France.Google Scholar
  37. [37]
    Vaxiviere, P., Tombe, K.: Celesstin: CAD conversion of mechanical drawings. Computer 25 (1992) 46–54.CrossRefGoogle Scholar
  38. [38]
    Myers, G., Mulgaonkar, P., Chen, C., DeCurtins, J., Chen, E.: Verification-based approach for automated text and feature extraction from raster-scanned maps. In Kasturi, R., Tombre, K., eds.: Graphics Recognition: Methods and Applications. Springer Verlag, Berlin (1996) 190–203.Google Scholar
  39. [39]
    Hartog, J., Kate, T., Gerbrands, J.: Knowledge-based segmentation for automatic map interpretation. In Kasturi, R., Tombre, K., eds.: Graphics Recognition: Methods and Applications. Springer, Berlin (1996) Vol. 1072 of LNCS.Google Scholar
  40. [40]
    Ah-Soon, C., Tombre, K.: Architectural symbol recognition using a network of constraints. PRL 22 (2001) 231–248.MATHGoogle Scholar
  41. [41]
    Aoki, Y., Shio, A., Arai, H., Odaka, K.: A prototype system for interpreting hand-sketched floor plans. In: Proceedings of 13th. Int. Conf. on Pattern Recognition. (1996) 747–751 Vienna, Austria.Google Scholar
  42. [42]
    Pasternak, B.: Processing imprecise and structural distroted line drawings by an adaptable drawing interpretation system. In Dengel, A., Spitz, L., eds.: Document Analysis Systems. World Scientific (1994) 349–365.Google Scholar
  43. [43]
    Cheng, T., Khan, J., Liu, H., Yun, Y.: A symbol recognition system. In: Proceedings of Second IAPR Int. Conf. on Document Analysis and Recognition, ICDAR’93. (1993) 918–921 Tsukuba, Japan.Google Scholar
  44. [44]
    Reiher, E., Li, Y., Donne, V., Lalonde, M., C. Hayne, Zhu, C.: A system for efficient and robust map symbol recognition. In: Proceedings of the 13th IAPR Int. Conf. on Pattern Recognition. Volume 3., Viena, Austria (1996) 783–787.CrossRefGoogle Scholar
  45. [45]
    Anquetil, E., Coüasnon, B., Dambreville, F.: A symbol classifier able to reject wrong shapes for document recognition systems. In Chhabra, A., Dori, D., eds.: Graphics Recognition-Recent Advances. Springer, Berlin (2000) 209–218 Vol. 1941 of LNCS.CrossRefGoogle Scholar
  46. [46]
    Miyao, H., Nakano, Y.: Note symbol extraction for printed piano scores using neural networks. IEICE Trans. Inf. and Syst. E79-D (1996) 548–553.Google Scholar
  47. [47]
    Yadid-Pecht, O., Gerner, M., Dvir, L., Brutman, E., Shimony, U.: Recognition of handwritten musical notes by a modified neocognitron. Machine Vision and Applications 9 (1996) 65–72.Google Scholar
  48. [48]
    Yang, D., Webster, J., Rendell, L., Garret, J., Shaw, D.: Management of graphical symbols in a cad environment: a neural network approach. In: Proceedings of Int. Conf. on Tools with AI. (1993) 272–279 Boston, Massachussets.Google Scholar
  49. [49]
    Cesarini, F., Francesconi, E., Gori, M., Marinai, S., Sheng, J., Soda, G.: A neural-based architecture for spot-noisy logo recognition. In: Proceedings of Fourth IAPR Int. Conf. on Document Analysis and Recognition, ICDAR’97. Volume 1. (1997) 175–179 Ulm, Germany.CrossRefGoogle Scholar
  50. [50]
    Francesconi, E., Frasconi, P., Gori, M., Mariani, S., Sheng, J., Soda, G., Sperduti, A.: Logo recognition by recursive neural networks. In Tombre, K., Chhabra, A., eds.: Graphics Recognition-Algorithms and Systems. Springer, Berlin (1998) Vol. 1389 of LNCS.Google Scholar
  51. [51]
    Kim, S., Suh, J., Kim, J.: Recognition of logic diagrams by identifying loops and rectilinear polylines. In: Proceedings of Second IAPR Int. Conf. on Document Analysis and Recognition, ICDAR’93. (1993) 349–352 Tsukuba, Japan.Google Scholar
  52. [52]
    Parker, J., Pivovarov, J., Royko, D.: Vector templates for symbol recognition. In: Proceedings of 15th. Int. Conf. on Pattern Recognition. Volume 2. (2000) 602–605 Barcelona, Spain.CrossRefGoogle Scholar
  53. [53]
    Adam, S., Ogier, J., Cariou, C., Gardes, J., Mullot, R., Lecourtier, Y.: Combination of invariant pattern recognition primitives on technical documents. In Chhabra, A., Dori, D., eds.: Graphics Recognition-Recent Advances. Springer, Berlin (2000) 238–245 Vol. 1941 of LNCS.CrossRefGoogle Scholar
  54. [54]
    Arias, J., Lai, C., Surya, S., Kasturi, R., Chhabra, A.: Interpretation of telephone system manhole drawings. PRL 16 (1995) 355–369.Google Scholar
  55. [55]
    Boatto, L., Consorti, V., Del Buono, M., Di Zenzo, S., Eramo, V., Espossito, A., Melcarne, F., Meucci, M., Morelli, A., Mosciatti, M., Scarci, S., Tucci, M.: An interpretation system for land register maps. Computer 25 (1992) 25–33.CrossRefGoogle Scholar
  56. [56]
    Samet, H., Soffer, A.: A legend-driven geographic symbol recognition system. In: Proceedings of 12th. Int. Conf. on Pattern Recognition (b). (1994) 350–355 Jerusalem, Israel.Google Scholar
  57. [57]
    Samet, H., Soffer, A.: Marco: Map retrieval by content. IEEE Trans. on PAMI 18 (1996) 783–797.Google Scholar
  58. [58]
    De Stefano, C., Tortorella, F., Vento, M.: An entropy based method for extracting robust binary templates. Machine Vision and Applications 8 (1995) 173–178.CrossRefGoogle Scholar
  59. [59]
    Armand, J.: Musical score recognition: a hierarchical and recursive approach. In: Proceedings of Second IAPR Int. Conf. on Document Analysis and Recognition, ICDAR’93. (1993) 906–909 Tsukuba, Japan.Google Scholar
  60. [60]
    Doermann, D., Rivlin, E., Weiss, I.: Applying algebraic and differential invariants for logo recognition. Machine Vision and Applications 9 (1996) 73–86.Google Scholar
  61. [61]
    Soffer, A., Samet, H.: Using negative shape features for logo similarity matching. In: Proceedings of 14th. Int. Conf. on Pattern Recognition (1). (1998) 571–573.Google Scholar
  62. [62]
    Suda, P., Bridoux, C., Kammerer, Maderlechner, G.: Logo and word matching using a general approach to signal registration. In: Proceedings of Fourth IAPR Int. Conf. on Document Analysis and Recognition, ICDAR’97. Volume 1. (1997) 61–65 Ulm, Germany.CrossRefGoogle Scholar
  63. [63]
    Lee, H., Lee, M.: Understanding mathematical expressions using procedure-oriented transformation. PR 27 (1994) 447–457.Google Scholar
  64. [64]
    Yu, Y., Samal, A., Seth, C.: A system for recognizing a large class of engineering drawings. IEEE Trans. on PAMI 19 (1997) 868–890.Google Scholar
  65. [65]
    Okazaki, A., Kondo, T., Mori, K., Tsunekawa, S., Kawamoto, E.: An automatic circuit diagram reader with loop-structure-based symbol recognition. IEEE Trans. on PAMI 10 (1988) 331–341.Google Scholar
  66. [66]
    Jorge, J., Fonseca, M.: A simple approach to recognise geometric shapes interactively. In Chhabra, A., Dori, D., eds.: Graphics Recognition-Recent Advances. Springer, Berlin (2000) 266–274 Vol. 1941 of LNCS.CrossRefGoogle Scholar
  67. [67]
    Yu, B.: Automatic understanding of symbol-connected diagrams. In: Proceedings of Third IAPR Int. Conf. on Document Analysis and Recognition, ICDAR’95. (1995) 803–806 Montreal, Canada.Google Scholar
  68. [68]
    Tombre, K., Dori, D.: Interpretation of engineering drawings. In Bunke, H., Wang, P., eds.: Handbook of character recognition and document image analysis. World Scientific Publishing Company (1997) 457–484.Google Scholar
  69. [69]
    Antoine, D., Collin, S., Tombre, K.: Analysis of technical documents: The REDRAW system. In Baird, H., Bunke, H., Yamamoto, K., eds.: Structured document image analysis. Springer Verlag (1992) 385–402.Google Scholar
  70. [70]
    Madej, D.: An intelligent map-to-CAD conversion system. In: Proceedings of 1st. Int. Conf. on Document Analysis and Recognition. (1991) 602–610 Saint Malo, France.Google Scholar
  71. [71]
    Randriamahefa, R., Cocquerez, J., Fluhr, C., Pépin, F., Philipp, S.: Printed music recognition. In: Proceedings of Second IAPR Int. Conf. on Document Analysis and Recognition, ICDAR’93. (1993) 898–901 Tsukuba, Japan.Google Scholar
  72. [72]
    Ramel, J., Boissier, G., Emptoz, H.: A structural representation adapted to handwritten symbol recognition. In Chhabra, A., Dori, D., eds.: Graphics Recognition: Recent Advances. Springer-Verlag, Berlin (2000) 228–237 Vol. 1941 of LNCS.CrossRefGoogle Scholar
  73. [73]
    Kasturi, R., Bow, S., El-Masri, W., Shah, J., Gattiker, J., Mokate, U.: A system for interpretation of line drawings. IEEE Trans. on PAMI 12 (1990) 978–992.Google Scholar
  74. [74]
    Ventura, A., Schettini, R.: Graphic symbol recognition using a signature technique. In: Proceedings of 12th. Int. Conf. on Pattern Recognition (b). (1994) 533–535 Jerusalem, Israel.Google Scholar
  75. [75]
    Wilfong, G., Sinden, F., Ruedisueli, L.: On-line recognition of handwritten symbols. IEEE Trans. on PAMI 18 (1996) 935–940.Google Scholar
  76. [76]
    Ablameyko, S.: An Introduction to Interpretation of Graphic Images. SPIE Optical Engineering Press (1997)Google Scholar
  77. [77]
    Dosch, P., Masini, G., Tombre, K.: Improving arc detection in graphics recognition. In: Proceedings of 15th. Int. Conf. on Pattern Recognition. Volume 2. (2000) 243–246 Barcelona, Spain.CrossRefGoogle Scholar
  78. [78]
    Ablameyko, S., Bereishik, V., Frantskevich, O., Homenko, M., Paramonova, N.: Knowledge-based recognition of crosshatched areas in engineering drawings. In Amin, A., Dori, D., Pudil, P., Freeman, H., eds.: Advances in Pattern Recognition. Vol. 1451 of LNCS (1998) 460–467.CrossRefGoogle Scholar
  79. [79]
    Lladós, J., Martí, E., López-Krahe, J.: A Hough-based method for hatched pattern detection in maps and diagrams. In: Proceedings of 5th Int. Conf. on Document Analysis and Recognition. (1999) 479–482 Bangalore, India.Google Scholar
  80. [80]
    Fahmy, H., Blonstein, D.: A graph grammar programming style for recognition of music notation. Machine Vision and Applications 6 (1993) 83–99.CrossRefGoogle Scholar
  81. [81]
    Landay, J., Myers, B.: Sketching interfaces: Toward more human interface design. IEEE Computer 34 (2001) 56–64.Google Scholar
  82. [82]
    Wenyin, L., Jin, X., Qian, W., Sun, Z.: Inputing composite graphic objects by sketching a few constituent simple shapes. In: Proceedings of Fourth IAPR Work, on Graphics Recognition. (2001) 73–84 Kingston, Canada.Google Scholar
  83. [83]
    Blostein, D., et al.: User interfaces for on-line diagram recognition. In: Proceedings of Fourth IAPR Work. on Graphics Recognition. (2001) 95–106 Kingston, Canada.Google Scholar
  84. [84]
    Paek, S., Smith, J.: Detecting image purpose in world-wide documents. In Lopresti, D., Zhou, J., eds.: Document Recognition V. SPIE, Bellingham, Whashington, USA (1998) 151–158 Vol. 3305 of Proceedings of SPIE.Google Scholar
  85. [85]
    Duda, R., Hart, P., Stork, D.: Pattern Classification and Scene Analysis. John Wiley and Sons, New York (2000)Google Scholar
  86. [86]
    Fu, K.: Syntactic Pattern Recognition and Applications. Prentice-Hall, Englewood Cliffs, N. J. (1982)MATHGoogle Scholar
  87. [87]
    Jain, A., Duin, R., Mao, J.: Statistical pattern recognition: a review. IEEE Trans. on PAMI 22 (2000) 4–37.Google Scholar
  88. [88]
    Pavlidis, T.: Structural Pattern Recognition. Springer-Verlag, New York (1977)MATHGoogle Scholar
  89. [89]
    Trier, O., Jain, A., Taxt, T.: Feature extraction methods for character recognition-a survey. PR 29 (1996) 641–662.Google Scholar
  90. [90]
    Furuta, M., Kase, N., Emori, S.: Segmentation and recognition of symbols for handwritten piping and instrument diagram. In: Proceedings of the 7th IAPR Int. Conf. on Pattern Recognition. (1984) 626–629.Google Scholar
  91. [91]
    Lin, X., Shimotsuji, S., Minoh, M., Sakai, T.: Efficient diagram understanding with characteristic pattern detection. Computer Vision, Graphics and Image Processing 30 (1985) 84–106.CrossRefGoogle Scholar
  92. [92]
    Lladós, J.: Combining Graph Matching and Hough Transform for Hand-Drawn Graphical Document Analysis. Application to Architectural Drawings. PhD thesis, Universitat Autònoma de Barcelona and Université de Paris 8 (1997)Google Scholar
  93. [93]
    Burr, D.: Elastic matching of line drawings. IEEE Trans. on PAMI 3 (1981) 708–713.Google Scholar
  94. [94]
    Doermann, D.: The indexing and retrieval of document images: A survey. Technical report, University of Maryland (1998) Technical Report CS-TR-3876.Google Scholar
  95. [95]
    Casacuberta, F., de Antonio, M.: A greedy algorithm for computing approximate median strings. In: VII Spanish Simposium on Pattern Recognition and Image Analysis. (1997) 193–198 Barcelona.Google Scholar
  96. [96]
    Wong, A., You, M.: Entropy and distance of random graphs with application to structural pattern recognition. IEEE Trans. on PAMI 7 (1985) 599–609.MATHGoogle Scholar
  97. [97]
    Cordella, L., Foggia, P., Genna, R., Vento, M.: Prototyping structural descriptions: an inductive learning approach. In Amin, A., Dori, D., Pudil, P., Freeman, H., eds.: Advances in Pattern Recognition. Springer Verlag, Berlin (1998) 339–348.CrossRefGoogle Scholar
  98. [98]
    Valveny, E., Martí, E.: Learning structural descriptions of graphic symbols using deformable template matching. In: Proceedings of 6th Int. Conf. on Document Analysis and Recognition. (2001) Seattle, USA.Google Scholar
  99. [99]
    Messmer, B.: Efficient Graph Matching Algorithms for Preprocessed Model Graphs. PhD thesis, University of Bern (1995)Google Scholar
  100. [100]
    Sossa, H., Horaud, R.: Model indexing: The graph-hashing approach. In: Proceedings of IEEE Conf. on Computer Vision and Pattern Recognition. (1992) 811–814 Champaign, Illinois.Google Scholar
  101. [101]
    Liu, W., Dori, D.: A protocol for performance evaluation of line detection algorithms. Machine Vision and Applications 9 (1997) 240–250.CrossRefGoogle Scholar
  102. [102]
    Liu, W., Dori, D.: A proposed scheme for performance evaluation of graphics/text separation algorithms. In Tombre, K., Chhabra, A., eds.: Graphics Recognition: Algorithms and Systems. Springer, Berlin (1998) 359–371 Vol. 1389 of LNCS.Google Scholar
  103. [103]
    Aksoy, S., Ye, M., Schauf, M., Song, M., Wang, Y., Haralick, R., Parker, J., Pivovarov, J., Royko, D., Sun, C., Farneboock, G.: Algorithm performance contest. In: Proceedings of 15th. Int. Conf. on Pattern Recognition. Volume 4. (2000) 870–876 Barcelona, Spain.CrossRefGoogle Scholar
  104. [104]
    Yamada, H., Yamamoto, K., Hosokawa, K.: Directional mathematical morphology and reformalized Hough transformation for the analysis of topographic maps. IEEE Trans. on PAMI 15 (1993) 380–387.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Josep Lladós
    • 1
  • Ernest Valveny
    • 1
  • Gemma Sánchez
    • 1
  • Enric Martí
    • 1
  1. 1.Computer Vision Center, Dept. InformàticaUniversitat Autònoma de BarcelonaBellaterra (Barcelona)Spain

Personalised recommendations