Abstract
The recognition of unconstrained handwriting images is usually based on vectorial representation and statistical classification. Despite their high representational power, graphs are rarely used in this field due to a lack of efficient graph-based recognition methods. Recently, graph similarity features have been proposed to bridge the gap between structural representation and statistical classification by means of vector space embedding. This approach has shown a high performance in terms of accuracy but had shortcomings in terms of computational speed. The time complexity of the Hungarian algorithm that is used to approximate the edit distance between two handwriting graphs is demanding for a real-world scenario. In this paper, we propose a faster graph matching algorithm which is derived from the Hausdorff distance. On the historical Parzival database it is demonstrated that the proposed method achieves a speedup factor of 12.9 without significant loss in recognition accuracy.
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Mahé, P., Ueda, N., Akutsu, T., Perret, J., Vert, J.: Graph kernels for molecular structure-activity relationship analysis with support vector machines. Journal of Chemical Information and Modeling 45(4), 939–951 (2005)
Bunke, H., Dickinson, P.J., Kraetzl, M., Wallis, W.D.: A Graph-Theoretic Approach to Enterprise Network Dynamics. Progress in Computer Science and Applied Logic, vol. 24. Birkhäuser (2006)
Llados, J., Marti, E., Villanueva, J.: Symbol recognition by error-tolerant subgraph matching between region adjacency graphs. IEEE Trans. PAMI 23(10), 1137–1143 (2001)
Lu, S., Ren, Y., Suen, C.Y.: Hierarchical attributed graph representation and recognition of handwritten chinese characters. Pattern Recognition 24(7), 617–632 (1991)
Bunke, H., Varga, T.: Off-line Roman cursive handwriting recognition. In: Chaudhuri, B. (ed.) Digital Document Processing, pp. 165–173. Springer (2007)
Ploetz, T., Fink, G.A.: Markov models for offline handwriting recognition: A survey. Int. Journal on Document Analysis and Recognition 12(4), 269–298 (2009)
Graves, A., Liwicki, M., Fernandez, S., Bertolami, R., Bunke, H., Schmidhuber, J.: A novel connectionist system for improved unconstrained handwriting recognition. IEEE Trans. PAMI 31(5), 855–868 (2009)
Fischer, A., Riesen, K., Bunke, H.: Graph similarity features for HMM-based handwriting recognition in historical documents. In: Proc. 12th Int. Conf. on Frontiers in Handwriting Recognition, pp. 253–258 (2010)
Pekalska, E., Duin, R.: The Dissimilarity Representations for Pattern Recognition: Foundations and Applications. World Scientific (2005)
Riesen, K., Bunke, H.: Graph Classification and Clustering Based on Vector Space Embedding. World Scientific (2010)
Bunke, H., Allermann, G.: Inexact graph matching for structural pattern recognition. Pattern Recognition Letters 1(4), 245–253 (1983)
Munkres, J.: Algorithms for the assignment and transportation problems. Journal of the Society for Industrial and Applied Mathematics 5(1), 32–38 (1957)
Riesen, K., Bunke, H.: Approximate graph edit distance computation by means of bipartite graph matching. Image and Vision Computing 27(7), 950–959 (2009)
Fischer, A., Wüthrich, M., Liwicki, M., Frinken, V., Bunke, H., Viehhauser, G., Stolz, M.: Automatic transcription of handwritten medieval documents. In: Proc. 15th Int. Conf. on Virtual Systems and Multimedia, pp. 137–142 (2009)
Fischer, A., Bunke, H.: Character prototype selection for handwriting recognition in historical documents with graph similarity features. In: Proc. 19th European Signal Processing Conference, pp. 1435–1439 (2011)
Huttenlocher, D.P., Klanderman, G.A., Kl, G.A., Rucklidge, W.J.: Comparing images using the Hausdorff distance. IEEE Trans. PAMI 15, 850–863 (1993)
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Fischer, A., Suen, C.Y., Frinken, V., Riesen, K., Bunke, H. (2013). A Fast Matching Algorithm for Graph-Based Handwriting Recognition. In: Kropatsch, W.G., Artner, N.M., Haxhimusa, Y., Jiang, X. (eds) Graph-Based Representations in Pattern Recognition. GbRPR 2013. Lecture Notes in Computer Science, vol 7877. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38221-5_21
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DOI: https://doi.org/10.1007/978-3-642-38221-5_21
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