Abstract
In this paper a geometric hash function able to cluster similar shapes and its use for symbol spotting in technical documents is presented. A very compact representation of features describing each primitive composing a symbol are used as key indexes of a hash table. When querying a symbol in this indexing table a voting scheme is used to validate the hypothesis of where this symbol is likely to be found. This hashing technique aims to perform a fast spotting process to find candidate locations needing neither a previous segmentation step nor a priori knowledge or learning step involving multiple instances of the object to recognize.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Ballard, D.H.: Generalizing the Hough Transform to Detect Arbitrary Shapes. Pattern Recognition 13(2), 111–122 (1981)
Dosch, P., Lladós, J.: Vectorial Signatures for Symbol Discrimination. In: Lladós, J., Kwon, Y.B. (eds.) GREC 2003. LNCS, vol. 3088, pp. 154–165. Springer, Heidelberg (2004)
Lamdan, Y., Wolfson, H.J.: Geometric Hashing: A General and Efficient Model-Based Recognition Scheme. In: 2nd International Conference on Computer Vision, pp. 238–249 (1988)
Lockwood, E.H.: A Book of Curves. Cambridge University Press, Cambridge (1967)
Locteau, H., Adam, S., Trupin, E., Labiche, J., Héroux, P.: Symbol Spotting Using Full Visibility Graph Representation. In: 7th International Workshop on Graphics Recognition, pp. 49–50 (2007)
Müller, S., Rigoll, G.: Searching an Engineering Drawing Database for User-Specified Shapes. In: 5th International Conference on Document Analysis and Recognition, pp. 697–700 (1999)
Rosin, P.L., West, G.A.: Segmentation of Edges into Lines and Arcs. Image and Vision Computing 7(2), 109–114 (1989)
Rusiñol, M., Lladós, J.: Symbol Spotting in Technical Drawings Using Vectorial Signatures. In: Wenyin, L., Lladós, J. (eds.) GREC 2005. LNCS, vol. 3926, pp. 35–46. Springer, Heidelberg (2006)
Tabbone, S., Wendling, L., Zuwala, D.: A Hybrid Approach to Detect Graphical Symbols in Documents. In: Marinai, S., Dengel, A.R. (eds.) DAS 2004. LNCS, vol. 3163, pp. 342–353. Springer, Heidelberg (2004)
Tombre, K., Ah-Soon, C., Dosch, P., Masini, G., Tabbone, S.: Stable and Robust Vectorization: How to Make the Right Choices. In: Chhabra, A.K., Dori, D. (eds.) GREC 1999. LNCS, vol. 1941, pp. 3–18. Springer, Heidelberg (2000)
Zhang, D., Lu, G.: Review of Shape Representation and Description Techniques. Pattern recognition 37(1), 1–19 (2004)
Zuwala, D., Tabbone, S.: A Method for Symbol Spotting in Graphical Documents. In: Bunke, H., Spitz, A.L. (eds.) DAS 2006. LNCS, vol. 3872, pp. 518–528. Springer, Heidelberg (2006)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Rusiñol, M., Lladós, J. (2008). A Region-Based Hashing Approach for Symbol Spotting in Technical Documents. In: Liu, W., Lladós, J., Ogier, JM. (eds) Graphics Recognition. Recent Advances and New Opportunities. GREC 2007. Lecture Notes in Computer Science, vol 5046. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88188-9_11
Download citation
DOI: https://doi.org/10.1007/978-3-540-88188-9_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88184-1
Online ISBN: 978-3-540-88188-9
eBook Packages: Computer ScienceComputer Science (R0)