Advertisement

An Overview of Techniques for Graphics Recognition

  • Rangachar Kasturi
  • Rajesh Raman
  • Chakravarthy Chennubhotla
  • Lawrence O’Gorman

Abstract

An overview is presented of algorithms and techniques for document image analysis with an emphasis on those for graphics recognition and interpretation. Topics covered are data capture, segmentation into text and graphics regions, vectorization, identification of graphical primitives, and generation of succinct image interpretations. This paper is primarily survey in nature, but an effort is made to provide information to evaluate and compare techniques, both through reference to more focused articles, as well as through our own experience.

Keywords

Line Segment Document Image Optical Character Recognition Black Pixel Text String 
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. M. Anderson and J. C. Bezdek, “Curvature and tangential deflection of discrete arcs: A theory based on the commutator of matrix pairs and its application to vertex detection in planar shape data,” IEEE Trans. PAMI, 6 (l): 27–40, Jan. 1984.CrossRefMATHGoogle Scholar
  2. C. Arcelli, L. P. Cordelia, and S. Levialdi, “From local maxima to connected skeletons,” IEEE Trans. PAMI, 3 (2): 134–143, Mar., 1981.CrossRefGoogle Scholar
  3. 3.
    C. Arcelli and G. S. di Baja, “A width-independent fast thinning algorithm,” IEEE Trans. PAMI, 7 (4): 463–474, Jul. 1985.CrossRefGoogle Scholar
  4. H. Asada and M. Brady, “The curvature primal sketch,” IEEE Trans. PAMI, 8 (l): 26–33, Jan. 1986.CrossRefGoogle Scholar
  5. F. Attneave, “Some informational aspects of visual perception,” Psychol. Rev. 61: 183–193, 1954.CrossRefGoogle Scholar
  6. H. S. Baird, S. E. Jones, and S. J. Fortune, “Image segmentation using shape-directed covers,” Proc. 10th ICPR, Atlantic City, New Jersey, 1990.Google Scholar
  7. H. Bley, “Segmentation and preprocessing of electrical schematics using picture graphs,” Computer Graphics and Image Processing, 28: 271–288, 1984.CrossRefGoogle Scholar
  8. S. Bow and R. Kasturi, “A graphics recognition system for interpretation of line drawings,” In R. Kasturi and M. M. Trivedi (eds.), Image Analysis Applications, Marcel Dekker, 1990.Google Scholar
  9. H. Bunke and G. Allerman, “Probabilistic relaxation for the interpretation of electrical schematics,” Proc. ICPR, pp. 438–440, 1981.Google Scholar
  10. H. Bunke, “Automatic interpretation of lines and text in circuit diagrams,” In J. Kittler, K. S. Fu, and L. F. Pau (eds.), Pattern Recognition: Theory and Applications, pp. 297–310, D. Reidel, 1982.Google Scholar
  11. R. G. Casey and K. Y. Wong, “Document analysis systems and techniques,” in Image Analysis Applications, R. Kasturi and M. M. Trivedi (eds), Marcel Dekker, 1990.Google Scholar
  12. L. S. Davis, Handbook of Pattern Recognition and Image Processing, Academic Press, 1986.Google Scholar
  13. K. Deguchi, “Multi-scale curvatures for contour feature extraction,” Proc. 9th ICPR, pp. 1113–1115, 1988.Google Scholar
  14. 3.
    D. Dori, “A syntactic/geometric approach to recognition of dimensions in engineering machine drawings,” Computer Vision, Graphics, and Image Processing, 47: 1–21, 1989.CrossRefGoogle Scholar
  15. W. Doster, “Different states of a document’s content on its way from the Gutenbergian world to the electronic world,” Proc. 7th ICPR, Montreal, pp. 872–874, 1984.Google Scholar
  16. R. O. Duda and P. E. Hart, Pattern Classification and Scene Analysis, Wiley-Interscience, New York, pp. 338–339, 1973.MATHGoogle Scholar
  17. M. Ejiri, S. Kakumoto, T. Miyatake, S. Shimada, and K. Iwamura, “Automatic recognition of engineering drawings and maps,” in Image Analysis Applications, R. Kasturi and M. M. Trivedi (eds), Marcel Dekker, 1990.Google Scholar
  18. W. El-Masri, Recognition and Description of Graphical Primitives, M. S. Thesis, Electrical Engineering, Penn State University, 1987.Google Scholar
  19. G. J. Ettinger, “Large hierarchical object recognition using libraries of parameterized model sub-parts,” Proc. CVPR, pp. 32–41, 1988.Google Scholar
  20. C. S. Fahn, J. F. Wang, and J. Y. Lee, “A topology-based component extractor for understanding electronic circuit diagrams,” Computer Vision, Graphics and Image Processing, 44: 119–138, 1988.CrossRefGoogle Scholar
  21. M. A. Fischler and R. C. Bolles, “Perceptual organization and curve partitioning,” IEEE Trans. PAMI, 8 (1): 100–105, Jan. 1986.CrossRefGoogle Scholar
  22. L. A. Fletcher and R. Kasturi, “A robust algorithm for text string separation from mixed text/graphics image,” IEEE Trans. PAMI, 10:(6)910–918, Nov. 1988.CrossRefGoogle Scholar
  23. H. Freeman, “On the encoding of arbitrary geometric configurations,” IEEE Trans. Elec. Computers, Vol. EC-10: 260–268, 1961.CrossRefMathSciNetGoogle Scholar
  24. H. Freeman and L. S. Davis, “A corner-finding algorithm for chain-coded curves,” IEEE Trans. Computers 26: 297–303, March 1977.CrossRefGoogle Scholar
  25. N. C. Fulford, “The Fastrak automatic digitizing system,” Pattern Recognition, 14: 65–74, 1981.CrossRefGoogle Scholar
  26. J. R. Gattiker, An improved algorithm for text–string separation from mixed text/graphics images, M. S. Thesis, Electrical Engineering, Penn State University, 1988.Google Scholar
  27. V. Govindaraju, D. B. Sher, R. K. Srihari, and S. N. Srihari, “Locating human faces in newspaper photographs,” Proc. CVPR, pp. 549–554, San Diego, 1989.Google Scholar
  28. W. E. L. Grimson and T. Lozano-Perez, “Model-based recognition and localization from sparse range or tactile data,” Intl. J. of Robotics Research, 3 (3): 3–35, 1984.CrossRefGoogle Scholar
  29. Z. Guo and R. W. Hall, “Parallel thinning with two-subiteration algorithms,” Comm. ACM, 32: 359–373, 1989.CrossRefMathSciNetGoogle Scholar
  30. H. Harada, Y. Itoh, and M. Ishii, “Recognition of free-hand drawings in chemical plant engineering,” Proc. IEEE Workshop on Computer Architecture for Pattern Analysis and Image Database Management, pp. 146–153, 1985.Google Scholar
  31. R. M. Haralick, L. T. Watson, and T. J. Laffey, “The topographic primal sketch,” Int’l J. Robotics Res., 2: 50–72, 1983.CrossRefGoogle Scholar
  32. J. F. Harris, J. Kittler, B. Llewellyn, and G. Preston, “A modular system for interpreting binary pixel representations of line-structured data,” In J. Kittler, K. S. Fu, and L. F. Pau (eds.), Pattern Recognition: Theory and Applications, pp. 311–351, D. Reidel, 1982.Google Scholar
  33. C. J. Hilditch, “Linear skeletons from square cupboards,” Machine Intelligence, 4: 403–420, 1969.Google Scholar
  34. F. Holdermann and H. Kazmierczak, “Preprocessing of gray scale pictures,” Computer Graphics and Image Processing, 1: 66–80, 1972.CrossRefGoogle Scholar
  35. C. L. Huang and J. T. Tou, “Knowledge-based functional-symbol understanding in electronic circuit diagram interpretation,” Proc. SPIE Conf Applications of Artificial Intelligence 635, 1986.Google Scholar
  36. J. Illingworth and J. Kittler, “A survey of the Hough transform,” Computer Graphics and Image Processing, 44: 87–116, 1988.CrossRefGoogle Scholar
  37. H. Imai and M. Iri, “Computational-geometric methods for polygonal approximations of a curve,” Computer Graphics and Image Processing, 36: 31–41, 1986.CrossRefGoogle Scholar
  38. H. V. Jagadish and L. O’Gorman, “An object model for image recognition,” IEEE Computer, 22 (12): 33–41, Dec. 1989.CrossRefGoogle Scholar
  39. A. K. Jain, Fundamentals of Digital Image Processing, Prentice-Hall, Englewood Cliffs, NJ, 1989.MATHGoogle Scholar
  40. J. Jimenez and J. L. Navalon, “Some experiments in image vetorization,” IBM J. Res. Develop., 26 (6): 724–734, Nov. 1982.CrossRefGoogle Scholar
  41. M. Karima, K. S. Sadhal, and T. O. McNeil, “From paper drawings to computer aided design,” IEEE Computer Graphics and Applications, pp. 24–39, Feb. 1985.Google Scholar
  42. R. Kasturi, R. Fernandez, M. L. Amlani, and W. C. Feng, “Map data processing in geographical information systems,” Computer, 22 (12): 10–21, 1989.CrossRefGoogle Scholar
  43. R. Kasturi, S. T. Bow, W. El-Masri, J. Shah, J. Gattiker, and U. Mokate, “A system for interpretation of line drawings,” accepted for publication in IEEE Trans. PAMI, 1990.Google Scholar
  44. H. Kida, O. Iwaki, and K. Kawada, “Document recognition system for office automation,” Proc. 8th ICPR, pp. 446–448, Paris, 1986.Google Scholar
  45. Y. Kurozumi and W. A. Davis, “Polygonal approximation by minimax method,” Computer Graphics and Image Processing, 19, 248–264, 1982.CrossRefMATHGoogle Scholar
  46. M. S. Landy and Y. Cohen, “Vectorgraph coding: efficient coding of line drawings,” Computer Vision, Graphics and Image Processing, 30: 331–344, 1985.CrossRefGoogle Scholar
  47. J. G. Leu and L. Chen, “Polygonal approximation of 2-D shapes through boundary merging,” Pattern Recognition Letters, 7: 231–238, 1988.CrossRefGoogle Scholar
  48. D. B. Lysak and R. Kasturi, “Interpretation of line drawings with multiple views,” Proc. 10th ICPR (Pattern Recognition Systems and Applications Subconference), 1990.Google Scholar
  49. I. Masuda, N. Hagita, T. Akayama, T. Takahashi, and S. Naito, “Approach to smart document reader system,” Proc. CVPR, pp. 550–557, San Francisco, 1985.Google Scholar
  50. D. Merelli, F. Mussio, and M. Padula, “An approach to the definition, description, and extraction of structures in binary digital images,” Computer Vision, Graphics, and Image Processing, 30: 19–49, 1985.CrossRefGoogle Scholar
  51. E. Meynieux, S. Seisen, and K. Tombre, “Bilevel information recognition and coding in office paper documents,” Proc. 8th ICPR, pp. 442–445, Paris, 1986.Google Scholar
  52. B. T. Mitchell and A. M. Gillies, “A model-based computer vision system for recognizing handwritten ZIP codes,” Machine Vision and Applications, 2: 231–243, 1989.CrossRefGoogle Scholar
  53. S. Mori and T. Sakakura, “Line filtering and its application to stroke segmentation of hand-printed Chinese characters,” Proc. 7th ICPR, pp. 366–369, Montreal, 1984.Google Scholar
  54. T. H. Morrin, “A black-white representation of a gray scale picture,” IEEE Trans. Computers, 23 (2): 184–186, 1974.CrossRefMATHMathSciNetGoogle Scholar
  55. N. J. Naccache and R. Shinghal, “SPTA: A proposed algorithm for thinning binary patterns,” IEEE Trans. Systems, Man, and Cybernetics, SMC-14(3): 409–418, 1984.Google Scholar
  56. P. A. Nagin, A. R. Hanson, and E. M. Riseman, “Studies in global and local histogram guided relaxation algorithms,” IEEE Trans. PAMI, 4: 263–277, 1982.CrossRefGoogle Scholar
  57. S. Nagy, S. C. Seth, and S. D. Stoddard, “Document analysis with an expert system,” In E. S. Gelsema and L. N. Kanal (eds.), Proc. Int’l Workshop on Pattern Recognition in Practice 2, pp. 149–159, Elsevier/North-Holland, Amsterdam, June 1985.Google Scholar
  58. D. Niyogi and Srihari S. N. N., “A rule-based system for document understanding,” Proc. AAAI, pp. 789–793, Philadelphia, 1986.Google Scholar
  59. L. O’Gorman, “An analysis of feature detectability from curvature estimation,” Proc. CVPR, pp. 235–240, Ann Arbor, Michigan, June 1988.Google Scholar
  60. L. O’Gorman, “Curvilinear feature detection from curvature estimation,” Proc. 9th ICPR, pp. 1116–1119, Rome, 1988.Google Scholar
  61. L. O’Gorman, “kxk Thinning,” Computer Vision, Graphics, and Image Processing, Vol. 51, 1990 (in press).Google Scholar
  62. L. O’Gorman, “Primitives chain code,” Computer Vision, Graphics, and Image Processing, 1990 (in press).Google Scholar
  63. A. Okazaki, T. Kondo, K. Mori, and S. Tsunekawa, “Knowledge controlled pattern recognition technique for hand drawn logic symbols,” Proc. IEEE Workshop on Computer Architecture for Pattern Analysis and Image Database Management, pp. 524–531, 1985.Google Scholar
  64. A. Okazaki, T. Kondo, K. Mori, S. Tsunekawa, and E. Kawamoto, “An automatic circuit diagram reader with loop-structure-based symbol recognition” IEEE Trans. PAMI, 10 (3): 331–341, May 1988.CrossRefGoogle Scholar
  65. J. R. Parker, “Extracting vectors from raster images,” Computer and Graphics, 12 (l): 75–79, 1988.CrossRefGoogle Scholar
  66. T. Pavlidis, Structural Pattern Recognition, Springer Series Electrophysics 1, Springer-Verlag, Berlin, 1977.MATHGoogle Scholar
  67. T. Pavlidis, “Survey: A review of algorithms for shape analysis,” Computer Graphics and Image Processing, 7: 243–258, 1978.CrossRefGoogle Scholar
  68. T. Pavlidis, Algorithms for Graphics and Image Processing, Computer Science Press, Rockville, Maryland, 1982.Google Scholar
  69. T. Pavlidis and L. L. Cherry, “Vector and arc encoding of graphics and text,” Proc. 6th ICPR, Munich, pp. 610–613, October 1982.Google Scholar
  70. T. Pavlidis, “A hybrid vectorization algorithm,” Proc. 7th ICPR, Montreal, pp. 490–492, 1984.Google Scholar
  71. S. Peleg and A. Rosenfeld, “A min-max medial axis transformation,” IEEE Trans. PAMI, 1: 88–89, 1979.CrossRefGoogle Scholar
  72. D. J. Peuquet, “An examination of techniques for reforming digital cartographic data, Part 1: The raster-to-vector process,” Cartographica, 18: 34–48, 1981.CrossRefGoogle Scholar
  73. T. Y. Phillips and A. Rosenfeld, “A method of curve partitioning using arc-chord distance,” Pattern Recognition Letters, 5: 285–288, April 1987.CrossRefGoogle Scholar
  74. K. Ramachandran, “A coding method for vector representation of engineering drawings,” Proc. IEEE, 68: 813–817, 1980.CrossRefGoogle Scholar
  75. U. E. Ramer, “An iterative procedure for the polygonal approximation of plane curves,” Computer Graphics and Image Processing, 1: 244–256, 1972.CrossRefGoogle Scholar
  76. A. Rosenfeld and E. Johnston, “Angle detection on digital curves,” IEEE Trans. Computers, 22: 875–878, Sept. 1973.CrossRefGoogle Scholar
  77. A. Rosenfeld and J. S. Weszka, “An improved method of angle detection on digital curves,” IEEE Trans. Computers, 24: 940–941, Sept. 1975.CrossRefGoogle Scholar
  78. A. Rosenfeld and A. C. Kak, Digital Picture Processing, 2nd edition, Academic Press, 1982.Google Scholar
  79. P. K. Sahoo, S. Soltani, A. K. C. Wong, and Y. C. Chen, “A survey of thresholding techniques,” Computer Vision, Graphics, and Image Processing, 41 (2): 233–260, February 1988.CrossRefGoogle Scholar
  80. P. Saint-Marc, J. S. Chen, and G. Medioni, “Adaptive smoothing: a general tool for early vision,” Proc. CVPR, San Diego, pp. 618–624, 1989.Google Scholar
  81. P. V. Sankar and C. V. Sharma, “A parallel procedure for the detection of dominant points on a digital curve,” Computer Graphics and Image Processing, 7: 403–412, 1978.CrossRefGoogle Scholar
  82. D. Shah, Vector Representation of Raster Scanned Images, M. S. Thesis, Electrical Engineering, Penn State University, 1988.Google Scholar
  83. C.-C. Shih and R. Kasturi, “Generation of a Line-Description File for Graphics Recognition,” Proc. SPIE Conf. on Applications of Artificial Intelligence, 937: 568–575, 1988.Google Scholar
  84. C.-C. Shih and R. Kasturi, “Extraction of graphic primitives from images of paper-based drawings” Machine Vision and Applications, 2: 103–113, 1989.CrossRefGoogle Scholar
  85. R. M. K. Sinha, “A width-independent algorithm for character skeleton estimation,” Computer Vision, Graphics, and Image Processing, 40: 388–397, 1987.CrossRefGoogle Scholar
  86. A. Sirjani and G. R. Cross, “An algorithm for polygonal approximation of a digital object,” Pattern Recognition Letters 7: 299–303, 1988.CrossRefGoogle Scholar
  87. J. Sklansky and V. Gonzalez, “Fast polygonal approximation of digitized curves,” Pattern Recognition, 12: 327–331, 1980.CrossRefGoogle Scholar
  88. S. N. Srihari and G. M. Zack, “Document image analysis,” Proc. 8th ICPR, Paris, pp. 434–436, 1986.Google Scholar
  89. S. N. Srihari and V. Govindaraju, “Analysis of textual images using the Hough transform,” Machine Vision and Applications, 2: 141–153, 1989.CrossRefGoogle Scholar
  90. H. Tamura, “A comparison of line thinning algorithms from digital geometry viewpoint,” ICPR, Kyoto, Japan, pp. 715–719, Nov. 1978.Google Scholar
  91. C. H. Teh and R. T. Chin, “On the detection of dominant points on digital curves,” IEEE Trans. PAMI, ll(8): 859–872, 1989.CrossRefGoogle Scholar
  92. D. Ting and B. Prasada, “Digital processing techniques for encoding of graphics,” Proc. IEEE, 6: 756–767, 1980.Google Scholar
  93. I. Tomek, “Two algorithms for piecewise-linear continuous fit of functions of one variable,” IEEE Trans. Computers, C-23(4): 445–448, 1974.CrossRefGoogle Scholar
  94. F. M. Wahl, “A new distance mapping algorithm and its use for shape measurement on binary patterns,” Computer Graphics and Image Processing, 23: 218–226, 1983.CrossRefGoogle Scholar
  95. T. Wakayama, “A core line tracking algorithm based on maximal square moving,” IEEE Trans. PAMI, 4 (l): 68–74, Jan. 1982.CrossRefGoogle Scholar
  96. K. Wall, “Curve fitting based on polygonal approximation,” Proc. 8th ICPR, Paris, pp. 1273–1275, 1986.Google Scholar
  97. K. Wall and P. E. Danielsson, “A fast sequential method for polygonal approximation of digitized curves,” Computer Graphics and Image Processing, 28: 220–227, 1984.CrossRefGoogle Scholar
  98. S. Wang, A. Y. Wu, and A. Rosenfeld, “Image approximation from gray scale medial axis,” IEEE Trans. PAMI, 3: 687–697, 1981.CrossRefGoogle Scholar
  99. T. Watson, K. Arvind, R. W. Ehrich, and R. M. Haralick, “Extraction of lines and drawings from grey tone line drawing images,” Pattern Recognition, 17 (5): 493–507, 1984.CrossRefGoogle Scholar
  100. M. A. Wesley and S. Markowsky, “Fleshing out projections,” IBM J. Research and Development, 25 (6): 934–954, Nov. 1981.CrossRefMathSciNetGoogle Scholar
  101. J. Weszka, “A survey of threshold selection techniques,” Computer Graphics and Image Processing, 7: 259–265, 1978.CrossRefGoogle Scholar
  102. J. White and G. Rohrer, “Image thresholding for optical recognition and other applications requiring character image extraction,” IBM J. Res. Dev., 27 (4): 400–411, 1983.CrossRefGoogle Scholar
  103. C. M. Williams, “Bounded straight-line approximation of digitized planar curves and lines,” Computer Graphics and Image Processing, 16: 370–381, 1981.CrossRefGoogle Scholar
  104. C. M. Williams, “An efficient algorithm for the piecewise linear approximation of planar curves,” Computer Graphics and Image Processing, 8: 286–293, 1978.CrossRefGoogle Scholar
  105. A. P. Witkin, “Scale-space filtering,” Proc. 8th IJCAI, Karlsruhe, W. Germany, pp. 1019–1022, Aug. 1983.Google Scholar
  106. G. Woetzel, “A fast and economic scan-to-line conversion algorithm,” Computer Graphics, 12: 125–129, 1978.CrossRefGoogle Scholar
  107. K. Y. Wong, “Multi-function auto-thresholding algorithm,” IBM Technical Disclosure Bulletin, 21 (7): 3001–3003, 1978.Google Scholar
  108. K. Y. Wong, R. G. Casey, and F. M. Wahl, “Document analysis system,” IBM J. Res. Dev., 26 (6): 647–656, 1982.CrossRefGoogle Scholar
  109. H. Zen and S. Ozawa, “Extraction of the fair document from mixed mode manuscript,” Proc. CVPR, San Francisco, pp. 544–549, 1985.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1992

Authors and Affiliations

  • Rangachar Kasturi
    • 1
  • Rajesh Raman
    • 1
  • Chakravarthy Chennubhotla
    • 1
  • Lawrence O’Gorman
    • 2
  1. 1.Department of Electrical and Computer EngineeringThe Pennsylvania State UniversityUniversity ParkUSA
  2. 2.AT&T Bell LaboratoriesMurray HillUSA

Personalised recommendations