Word and Sentence Extraction Using Irregular Pyramid

  • Poh Kok Loo
  • Chew Lim Tan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2423)

Abstract

This paper presents the result of our continued work on a further enhancement to our previous proposed algorithm. Moving beyond the extraction of word groups and based on the same irregular pyramid structure the new proposed algorithm groups the extracted words into sentences. The uniqueness of the algorithm is in its ability to process text of a wide variation in terms of size, font, orientation and layout on the same document image. No assumption is made on any specified document type. The algorithm is based on the irregular pyramid structure with the application of four fundamental concepts. The first is the inclusion of background information. The second is the concept of closeness where text information within a group is close to each other, in terms of spatial distance, as compared to other text areas. The third is the “majority win” strategy that is more suitable under the greatly varying environment than a constant threshold value. The final concept is the uniformity and continuity among words belonging to the same sentence.

Keywords

Text Image Document Image Pyramid Structure Word Fragment Word Group 
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.

References

  1. 1.
    G. Nagy, “Twenty years of document image analysis in PAMI”, IEEE Trans. PAMI, Vol. 22, No. 1, 38–62, (Jan 2000).MathSciNetGoogle Scholar
  2. 2.
    Richard G. Casey and Eric Lecolinet, “A survey of methods and strategies in character segmentation”, IEEE Trans. PAMI, Vol. 18, No. 7, (July 1996).Google Scholar
  3. 3.
    U. Pal and B.B. Chaudhuri, “Automatic separation of words in multi-lingual muti-script Indian documents”, In Proc. 4th Int. Conf. On Document Analysis and Recogn. (ICDAR’ 97).Google Scholar
  4. 4.
    Yalin Wang, Ihsin T. Phillips and Robert Haralick, “Statistical-based approach to word segmentation”, Proceedings of the ICPR 2000.Google Scholar
  5. 5.
    Dae-Seok Ryu, Sun-Mee Kang and Seong-Whan Lee, “Parameter-independent geometric document layout analysis”, IEEE, (2000).Google Scholar
  6. 6.
    K.Y. Wong, R.G. Casy and F.M. Wahl, “Document analysis system”, IBM J. Res. Development, Vol 26, 642–656 (1982).Google Scholar
  7. 7.
    G. Nagy and S. Seth, “Hierarchical representation of optically scanned documents”, In Proc. 7th Int. Conf. Patt. Recogn. (ICPR), 347–349 (1984).Google Scholar
  8. 8.
    C.L. Tan and P.O. Ng, “Text extraction using pyramid”, Pattern Recognition, Vol. 31, No. 1, 63–72 (1998).CrossRefGoogle Scholar
  9. 9.
    W.G. Kropatsch and A. Montanvert, “Irregular versus regular pyramid structures”, In U. Eckhardt, A. Hubler, W. Nagel, and G. Werner, editors, Geometrical Problems of Image Processing, 11–22 (1991).Google Scholar
  10. 10.
    W.G. Kropatsch, “Irregular pyramids”, Proceedings of the 15th OAGM meeting in Klagenfurt, 39–50 (1991).Google Scholar
  11. 11.
    Horace H.S. Ip and Stephen W.C. Lam, “Alternative strategies for irregular pyramid construction”, Image and Vision Computing, 14, 297–304 (1996).CrossRefGoogle Scholar
  12. 12.
    A. Montanvert, P. Meer and A. Rosenfeld, “Hierarchical image analysis using irregular tessellations”, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol 13, No. 4, 307–316 (1991).CrossRefGoogle Scholar
  13. 13.
    G. Bongiovanni, L. Cinque, S. Levialdi and A. Rosenfeld, “Image segmentation by a multiresolution approach”, Pattern Recognition, Vol. 26, 1845–1854, (1993).CrossRefGoogle Scholar
  14. 14.
    P.K. Loo and C.L. Tan, “Detection of word groups based on irregular pyramid”, Proc. 6th Int. Conf. on Document Analysis and Recogn (ICDAR), 2001.Google Scholar
  15. 15.
    Boulos Waked, Ching Y. Suen and Sabine Bergler, “Segmenting document images using white runs and vertical edges”, Proc. 6th Int. Conf. on Document Analysis and Recogn (ICDAR), 2001.Google Scholar
  16. 16.
    Hideyuki Negishi, Jien Kato, Hiroyuki Hase and Toyohide Watanable, “Character Extraction from Noisy Background for an Automatic Reference System”, In Proc. 5th Int. Conf. On Document Analysis and Recogn. (ICDAR), 143–146 (1999).Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Poh Kok Loo
    • 1
  • Chew Lim Tan
    • 2
  1. 1.School of the Built Environment & DesignSingapore PolytechnicSingapore
  2. 2.School of ComputingNational University of SingaporeSingapore

Personalised recommendations