Abstract
The huge amount of documents in digital formats raised the need of effective content-based retrieval techniques. Since manual indexing is infeasible and subjective, automatic techniques are the obvious solution. In particular, the ability of properly identifying and understanding a document’s structure is crucial, in order to focus on the most significant components only. Thus, the quality of the layout analysis outcome biases the next understanding steps. Unfortunately, due to the variety of document styles and formats, the automatically found structure often needs to be manually adjusted. In this work we present a tool based on Markov Logic Networks to infer corrections rules to be applied to forthcoming documents. The proposed tool, embedded in a prototypical version of the document processing system DOMINUS, revealed good performance in real-world experiments.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Breuel, T.M.: Two geometric algorithms for layout analysis. In: Lopresti, D.P., Hu, J., Kashi, R.S. (eds.) DAS 2002. LNCS, vol. 2423, pp. 188–199. Springer, Heidelberg (2002)
Chang, F., Chu, S.Y., Chen, C.Y.: Chinese document layout analysis using adaptive regrouping strategy. Pattern Recognition 38(2), 261–271 (2005)
Dengel, A., Dubiel, F.: Computer understanding of document structure. International Journal of Imaging Systems and Technology 7, 271–278 (1996)
Esposito, F., Ferilli, S., Basile, T.M.A., Di Mauro, N.: Machine Learning for digital document processing: from layout analysis to metadata extraction. In: Marinai, S., Fujisawa, H. (eds.) Machine Learning in Document Analysis and Recognition. SCI, vol. 90, pp. 105–138. Springer, Heidelberg (2008)
Etemad, K., Doermann, D., Chellappa, R.: Multiscale segmentation of unstructured document pages using soft decision integration. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(1), 92–96 (1997)
Fawcett, T.: Roc graphs: Notes and practical considerations for researchers. Tech. rep., HP Laboratories (2004), http://www.hpl.hp.com/techreports/2003/HPL-2003-4.pdf
Getoor, L., Taskar, B.: Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning). MIT Press, Cambridge (2007)
Krishnamoorthy, M., Nagy, G., Seth, S., Viswanathan, M.: Syntactic segmentation and labeling of digitized pages from technical journals. IEEE Transactions on Pattern Analysis and Machine Intelligence 15(7), 737–747 (1993)
Laven, K., Leishman, S., Roweis, S.: A statistical learning approach to document image analysis. In: Proceedings of the Eighth International Conference on Document Analysis and Recognition, pp. 357–361. IEEE Computer Society, Los Alamitos (2005)
Liu, J., Tang, Y.Y., Suen, C.Y.: Chinese document layout analysis based on adaptive split-and-merge and qualitative spatial reasoning. Pattern Recognition 30(8), 1265–1278 (1997)
Malerba, D., Esposito, F., Altamura, O., Ceci, M., Berardi, M.: Correcting the document layout: A machine learning approach. In: Proceedings of the 7th Intern. Conf. on Document Analysis and Recognition, pp. 97–103. IEEE Comp. Soc., Los Alamitos (2003)
Okamoto, M., Takahashi, M.: A hybrid page segmentation method. In: Proceedings of the Second International Conference on Document Analysis and Recognition, pp. 743–748. IEEE Computer Society, Los Alamitos (1993)
Papadias, D., Theodoridis, Y.: Spatial relations, minimum bounding rectangles, and spatial data structures. International Journal of Geographical Information Science 11(2), 111–138 (1997)
Richardson, M., Domingos, P.: Markov logic networks. Machine Learning 62, 107–136 (2006)
Simon, A., Pret, J.-C., Johnson, A.P.: A fast algorithm for bottom-up document layout analysis. IEEE Transactions on PAMI 19(3), 273–277 (1997)
Wu, C.C., Chou, C.H., Chang, F.: A machine-learning approach for analyzing document layout structures with two reading orders. Pattern Recogn. 41(10), 3200–3213 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ferilli, S., Basile, T.M.A., Di Mauro, N. (2011). Markov Logic Networks for Document Layout Correction. In: Mehrotra, K.G., Mohan, C.K., Oh, J.C., Varshney, P.K., Ali, M. (eds) Modern Approaches in Applied Intelligence. IEA/AIE 2011. Lecture Notes in Computer Science(), vol 6703. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21822-4_28
Download citation
DOI: https://doi.org/10.1007/978-3-642-21822-4_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21821-7
Online ISBN: 978-3-642-21822-4
eBook Packages: Computer ScienceComputer Science (R0)