Skip to main content
SpringerLink
Log in
Book cover

Learning Structure and Schemas from Documents pp 121–142Cite as

Transductive Learning of Logical Structures from Document Images

  • Chapter

Part of the book series: Studies in Computational Intelligence ((SCI,volume 375))

Abstract

A fundamental task of document image understanding is to recognize semantically relevant components in the layout extracted from a document image. This task can be automatized by learning classifiers to label such components. The application of inductive learning algorithms assumes the availability of a large set of documents, whose layout components have been previously labeled through manual annotation. This contrasts with the more common situation in which we have only few labeled documents and an abundance of unlabeled ones. A further degree of complexity of the learning task is represented by the importance of spatial relationships between layout components, which cannot be adequately represented by feature vectors. To face these problems, we investigate the application of a relational classifier that works in the transductive setting. Transduction is justified by the possibility of exploiting the large amount of information conveyed in the unlabeled documents and by the contiguity of the concept of positive autocorrelation with the smoothness assumption which characterizes the transductive setting. The classifier takes advantage of discovered emerging patterns that permit us to qualitatively characterize classes. Computational solutions have been tested on document images of scientific literature and the experimental results show the advantages and drawbacks of the approach.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aiello, M., Monz, C., Todoran, L.: Document understanding for a broad class of documents. IJDAR 5(1), 1–16 (2002)

    Article  MATH  Google Scholar 

  2. Akindele, O.T., Belaïd, A.: Construction of generic models of document structures using inference of tree grammars. In: ICDAR 1995: Proceedings of the Third International Conference on Document Analysis and Recognition, vol. 1, p. 206. IEEE Computer Society, Washington, DC, USA (1995)

    Chapter  Google Scholar 

  3. Allen, J.F.: Maintaining knowledge about temporal intervals. Commun. ACM 26(11), 832–843 (1983)

    Article  MATH  Google Scholar 

  4. Appice, A., Ceci, M., Malerba, D.: Transductive learning for spatial regression with co-training. In: Shin, S.Y., Ossowski, S., Schumacher, M., Palakal, M.J., Hung, C.-C. (eds.) SAC, pp. 1065–1070. ACM Press, New York (2010)

    Google Scholar 

  5. Appice, A., Ceci, M., Malgieri, C., Malerba, D.: Discovering relational emerging patterns. In: Basili, R., Pazienza, M.T. (eds.) AI*IA 2007. LNCS (LNAI), vol. 4733, pp. 206–217. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  6. Rosenfeld, A., Hummel, R., Zucker, S.: Scene labeling by relaxation operations. J IEEE Transactions SMC 6(6), 420–433 (1976)

    MathSciNet  MATH  Google Scholar 

  7. Baird, H.S., Casey, M.R.: Towards versatile document analysis systems. In: Bunke, H., Spitz, A.L. (eds.) DAS 2006. LNCS, vol. 3872, pp. 280–290. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  8. Bennett, K.P.: Combining support vector and mathematical programming methods for classification, pp. 307–326. MIT Press, Cambridge (1999)

    Google Scholar 

  9. Ceci, M., Appice, A.: Spatial associative classification: propositional vs. structural approach. Journal of Intelligent Information Systems 27(3), 191–213 (2006)

    Article  Google Scholar 

  10. Ceci, M., Appice, A., Malerba, D.: Emerging pattern based classification in relational data mining. In: Bhowmick, S.S., Küng, J., Wagner, R. (eds.) DEXA 2008. LNCS, vol. 5181, pp. 283–296. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  11. Ceci, M., Appice, A., Malerba, D.: Transductive learning for spatial data classification. In: Koronacki, J., Raś, Z.W., Wierzchoń, S.T., Kacprzyk, J. (eds.) Advances in Machine Learning I. SCI, vol. 262, pp. 189–207. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  12. Ceci, M., Berardi, M., Malerba, D.: Relational data mining and ILP for document image understanding. Applied Artificial Intelligence 21(4&5), 317–342 (2007)

    Article  Google Scholar 

  13. Ceci, M., Malerba, D.: Classifying web documents in a hierarchy of categories: a comprehensive study. J. Intell. Inf. Syst. 28(1), 37–78 (2007)

    Article  Google Scholar 

  14. Chapelle, O., Schölkopf, B., Zien, A.: A discussion of semi-supervised learning and transduction. In: Chapelle, O., Schölkopf, B., Zien, A. (eds.) Semi-Supervised Learning, pp. 457–462. MIT Press, Cambridge (2006)

    Google Scholar 

  15. Chen, Y., Wang, G., Dong, S.: Learning with progressive transductive support vector machines. Pattern Recognition Letters 24, 1845–1855 (2003)

    Article  Google Scholar 

  16. De Raedt, L.: Attribute-value learning versus inductive logic programming: the missing links. In: Page, D.L. (ed.) ILP 1998. LNCS (LNAI), vol. 1446, pp. 1–8. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  17. Dong, G., Li, J.: Efficient mining of emerging patterns: Discovering trends and differences. In: International Conference on Knowledge Discovery and Data Mining, pp. 43–52. ACM Press, New York (1999)

    Google Scholar 

  18. Dong, G., Zhang, X., Wong, L., Li, J.: CAEP: Classification by aggregating emerging patterns. In: Arikawa, S., Nakata, I. (eds.) DS 1999. LNCS (LNAI), vol. 1721, pp. 30–42. Springer, Heidelberg (1999)

    Chapter  Google Scholar 

  19. Esposito, F., Malerba, D., Semeraro, G.: Multistrategy learning for document recognition. Applied Artificial Intelligence 8(1), 33–84 (1994)

    Article  Google Scholar 

  20. Fan, H., Ramamohanarao, K.: An efficient singlescan algorithm for mining essential jumping emerging patterns for classification. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 456–462 (2002)

    Google Scholar 

  21. Gammerman, A., Azoury, K., Vapnik, V.: Learning by transduction. In: Proc. of the 14th Annual Conference on Uncertainty in Artificial Intelligence, UAI 1998, pp. 148–155. Morgan Kaufmann, San Francisco (1998)

    Google Scholar 

  22. Getoor, L.: Multi-relational data mining using probabilistic relational models: research summary. In: Knobbe, A., Van der Wallen, D.M.G. (eds.) Proc.of the 1st Workshop in Multi-relational Data Mining, Freiburg, Germany (2001)

    Google Scholar 

  23. Jensen, D., Neville, J.: Linkage and autocorrelation cause feature selection bias in relational learning. In: Proc. of the Nineteenth International Conference on Machine Learning (2002)

    Google Scholar 

  24. Joachims, T.: Transductive inference for text classification using support vector machines. In: Proc. of the 16th International Conference on Machine Learning, ICML 1999, pp. 200–209. Morgan Kaufmann, San Francisco (1999)

    Google Scholar 

  25. Joachims, T.: Transductive learning via spectral graph partitioning. In: Proc. of the 20th International Conference on Machine Learning, ICML 2003, Morgan Kaufmann, San Francisco (2003)

    Google Scholar 

  26. Krogel, M.-A., Scheffer, T.: Multi-relational learning, text mining, and semi-supervised learning for functional genomics. Machine Learning 57(1-2), 61–81 (2004)

    Article  MATH  Google Scholar 

  27. Kukar, M., Kononenko, I.: Reliable classifications with machine learning. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) ECML 2002. LNCS (LNAI), vol. 2430, pp. 219–231. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  28. Lisi, F.A., Malerba, D.: Inducing multi-level association rules from multiple relations. Machine Learning 55, 175–210 (2004)

    Article  MATH  Google Scholar 

  29. Liu, B., Hsu, W., Ma, Y.: Integrating classification and association rule mining. In: Knowledge Discovery and Data Mining KDD 1998, New York, pp. 80–86 (1998)

    Google Scholar 

  30. Malerba, D.: A relational perspective on spatial data mining. IJDMMM 1(1), 103–118 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  31. Malerba, D., Ceci, M., Berardi, M.: Machine learning for reading order detection in document image understanding. In: Marinai, S., Fujisawa, H. (eds.) Machine Learning in Document Analysis and Recognition. SCI, vol. 90, pp. 45–69. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  32. Mannila, H., Toivonen, H.: Levelwise search and borders of theories in knowledge discovery. Data Min. Knowl. Discov. 1(3), 241–258 (1997)

    Article  Google Scholar 

  33. Nagy, G.: Twenty years of document image analysis in pami. IEEE Trans. Pattern Anal. Mach. Intell. 22(1), 38–62 (2000)

    Article  Google Scholar 

  34. Niyogi, D., Srihari, S.N.: Knowledge-based derivation of document logical structure. In: ICDAR 1995: Proceedings of the Third International Conference on Document Analysis and Recognition, vol. 1, p. 472. IEEE Computer Society Press, Washington, DC, USA (1995)

    Chapter  Google Scholar 

  35. Palmero, G.I.S., Dimitriadis, Y.A.: Structured document labeling and rule extraction using a new recurrent fuzzy-neural system. In: ICDAR 1999: Proceedings of the Fifth International Conference on Document Analysis and Recognition, p. 181. IEEE Computer Society Press, Washington, DC, USA (1999)

    Chapter  Google Scholar 

  36. Pazzani, M.J., Mani, S., Shankle, W.R.: Beyond concise and colorful: Learning intelligible rules. In: KDD, pp. 235–238 (1997)

    Google Scholar 

  37. Porter, M.F.: An algorithm for suffix stripping. Readings in information retrieval, 313–316 (1997)

    Google Scholar 

  38. Quinlan, J.R.: C4.5: programs for machine learning. Morgan Kaufmann Publishers Inc., San Francisco (1993)

    Google Scholar 

  39. Robinson, J.A.: A machine oriented logic based on the resolution principle. Journal of the ACM 12, 23–41 (1965)

    Article  MATH  Google Scholar 

  40. Seeger, M.: Learning with labeled and unlabeled data. Technical report, Institute for Adaptive and Neural Computation. University of Edinburgh (2001)

    Google Scholar 

  41. Souafi-Bensafi, S., Parizeau, M., Lebourgeois, F., Emptoz, H.: Bayesian networks classifiers applied to documents. In: ICPR (1), p. 483 (2002)

    Google Scholar 

  42. Taskar, B., Segal, E., Koller, D.: Probabilistic classification and clustering in relational data. In: Nebel, B. (ed.) IJCAI, pp. 870–878. Morgan Kaufmann, San Francisco (2001)

    Google Scholar 

  43. Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)

    MATH  Google Scholar 

  44. Vapnik, V.: Statistical Learning Theory. Wiley, New York (1998)

    MATH  Google Scholar 

  45. Walischewski, H.: Automatic knowledge acquisition for spatial document interpretation. In: ICDAR, pp. 243–247. IEEE Computer Society Press, Los Alamitos (1997)

    Google Scholar 

  46. Zhang, X., Dong, G., Ramamohanarao, K.: Exploring constraints to efficiently mine emerging patterns from large high-dimensional datasets. In: KDD, pp. 310–314 (2000)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dipartimento di Informatica, Università degli Studi di Bari “Aldo Moro”, via Orabona 4, 70125, Bari, Italy

    Michelangelo Ceci, Corrado Loglisci & Donato Malerba

Authors
  1. Michelangelo Ceci
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Corrado Loglisci
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Donato Malerba
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. University of New York Tirana, Rr. Komuna E Parisit,, Tirana, Albania

    Marenglen Biba

  2. Technical University of Catalonia, Campus Nord, Ed. Omega, C/Jordi Girona 1-3, 08034, Barcelona, Spain

    Fatos Xhafa

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Ceci, M., Loglisci, C., Malerba, D. (2011). Transductive Learning of Logical Structures from Document Images. In: Biba, M., Xhafa, F. (eds) Learning Structure and Schemas from Documents. Studies in Computational Intelligence, vol 375. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22913-8_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-22913-8_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-22912-1

  • Online ISBN: 978-3-642-22913-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation