Abstract
In this paper, we propose a novel technique for automatic table detection in document images. Lines and tables are among the most frequent graphic, non-textual entities in documents and their detection is directly related to the OCR performance as well as to the document layout description. We propose a workflow for table detection that comprises three distinct steps: (i) image pre-processing; (ii) horizontal and vertical line detection and (iii) table detection. The efficiency of the proposed method is demonstrated by using a performance evaluation scheme which considers a great variety of documents such as forms, newspapers/magazines, scientific journals, tickets/bank cheques, certificates and handwritten documents.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
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
Zanibbi, R., Blostein, D., Cordy, J.: A survey of table recognition. International Journal of Document Analysis and Recogntion (IJDAR) 7, 1–16 (2004)
Zheng, Y., Liu, C., Ding, X., Pan, S.: Form Frame Line Detection with Directional Single-Connected Chain. In: Proc. of the 6th Int. Conf. on Doc. Anal. & Recognition, pp. 699–703 (2001)
Neves, L., Facon, J.: Methodology of Automatic extraction of Table-Form Cells. In: IEEE Proc. of the XIII Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI 2000), pp. 15–21 (2000)
Kieninger, T., Dengel, A.: Applying the T-Recs Table Recognition System to the Business Letter Domain. In: Proc. of the 6th International Conference on Document Analysis & Recognition, Seattle, pp. 518–522 (2001)
Cesari, F., Marinai, S., Sarti, L., Soda, G.: Trainable Table Location in Document Images. In: Proc. of the International Conference of Pattern Recognition, vol. 3, pp. 236–240 (2002)
Gatos, B., Pratikakis, I., Perantonis, S.J.: An adaptive binarisation technique for low quality historical documents. In: Marinai, S., Dengel, A.R. (eds.) DAS 2004. LNCS, vol. 3163, pp. 102–113. Springer, Heidelberg (2004)
Yin, P.Y.: Skew detection and block classification of printed documents. Image and Vision Computing 19, 567–579 (2001)
Perantonis, S.J., Gatos, B., Papamarkos, N.: Block decomposition and segmentation for fast Hough transform evaluation. Pattern Recognition 32(5), 811–824 (1999)
Avila, B.T., Lins, R.D.: A new algorithm for removing noisy border from monochromatic documents. In: Proc. of the 2004 ACM Symp. on Applied Comp., pp. 1219–1225 (2004)
Antonacopoulos, A., Gatos, B., Karatzas, D.: ICDAR 2003 Page Segmentation Competition. In: Proc. of the 7th Int. Conf. on Document Analysis & Recognition, pp. 688–692 (2003)
Zheng Yefeng homepage (2005): http://www.ece.umd.edu/~zhengyf/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gatos, B., Danatsas, D., Pratikakis, I., Perantonis, S.J. (2005). Automatic Table Detection in Document Images. In: Singh, S., Singh, M., Apte, C., Perner, P. (eds) Pattern Recognition and Data Mining. ICAPR 2005. Lecture Notes in Computer Science, vol 3686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11551188_67
Download citation
DOI: https://doi.org/10.1007/11551188_67
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28757-5
Online ISBN: 978-3-540-28758-2
eBook Packages: Computer ScienceComputer Science (R0)