A Novel Graph Database for Handwritten Word Images

  • Michael StaufferEmail author
  • Andreas Fischer
  • Kaspar Riesen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10029)


For several decades graphs act as a powerful and flexible representation formalism in pattern recognition and related fields. For instance, graphs have been employed for specific tasks in image and video analysis, bioinformatics, or network analysis. Yet, graphs are only rarely used when it comes to handwriting recognition. One possible reason for this observation might be the increased complexity of many algorithmic procedures that take graphs, rather than feature vectors, as their input. However, with the rise of efficient graph kernels and fast approximative graph matching algorithms, graph-based handwriting representation could become a versatile alternative to traditional methods. This paper aims at making a seminal step towards promoting graphs in the field of handwriting recognition. In particular, we introduce a set of six different graph formalisms that can be employed to represent handwritten word images. The different graph representations for words, are analysed in a classification experiment (using a distance based classifier). The results of this word classifier provide a benchmark for further investigations.


Graph benchmarking dataset Graph repository Graph representation for handwritten words 



This work has been supported by the Hasler Foundation Switzerland.


  1. 1.
    Conte, D., Foggia, P., Sansone, C., Vento, M.: Thirty years of graph matching in pattern recognition. Int. J. Pattern Recogn. Artif. Intell. 18(03), 265–298 (2004)CrossRefGoogle Scholar
  2. 2.
    Foggia, P., Percannella, G., Vento, M.: Graph matching and learning in pattern recognition in the last 10 years. Int. J. Pattern Recogn. Artif. Intell. 28(01), 1450001 (2014)MathSciNetCrossRefGoogle Scholar
  3. 3.
    De Santo, M., Foggia, P., Sansone, C., Vento, M.: A large database of graphs and its use for benchmarking graph isomorphism algorithms. Pattern Recogn. Lett. 24(8), 1067–1079 (2003)CrossRefzbMATHGoogle Scholar
  4. 4.
    Bunke, H., Riesen, K.: Recent advances in graph-based pattern recognition with applications in document analysis. Pattern Recogn. 44(5), 1057–1067 (2011)CrossRefzbMATHGoogle Scholar
  5. 5.
    Le Bodic, P., Héroux, P., Adam, S., Lecourtier, Y.: An integer linear program for substitution-tolerant subgraph isomorphism and its use for symbol spotting in technical drawings. Pattern Recogn. 45(12), 4214–4224 (2012)CrossRefGoogle Scholar
  6. 6.
    Fischer, A., Suen, C.Y., Frinken, V., Riesen, K., Bunke, H.: A fast matching algorithm for graph-based handwriting recognition. In: Kropatsch, W.G., Artner, N.M., Haxhimusa, Y., Jiang, X. (eds.) GbRPR 2013. LNCS, vol. 7877, pp. 194–203. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-38221-5_21 CrossRefGoogle Scholar
  7. 7.
    Riba, P., Llados, J., Fornes, A.: Handwritten word spotting by inexact matching of grapheme graphs. In: International Conference on Document Analysis and Recognition, pp. 781–785 (2015)Google Scholar
  8. 8.
    Wang, P., Eglin, V., Garcia, C., Largeron, C., Llados, J., Fornes, A.: A novel learning-free word spotting approach based on graph representation. In: International Workshop on Document Analysis Systems, pp. 207–211 (2014)Google Scholar
  9. 9.
    Bui, Q.A., Visani, M., Mullot, R.: Unsupervised word spotting using a graph representation based on invariants. In: International Conference on Document Analysis and Recognition, pp. 616–620 (2015)Google Scholar
  10. 10.
    Wang, K., Wang, Y., Zhang, Z.: On-line signature verification using segment-to-segment graph matching. In: International Conference on Document Analysis and Recognition, pp. 804–808 (2011)Google Scholar
  11. 11.
    Fotak, T., Bača, M., Koruga, P.: Handwritten signature identification using basic concepts of graph theory. Trans. Sig. Process. 7(4), 117–129 (2011)Google Scholar
  12. 12.
    Lavrenko, V., Rath, T., Manmatha, R.: Holistic word recognition for handwritten historical documents. In: International Workshop on Document Image Analysis for Libraries, pp. 278–287 (2004)Google Scholar
  13. 13.
    Fischer, A., Keller, A., Frinken, V., Bunke, H.: Lexicon-free handwritten word spotting using character HMMs. Pattern Recogn. Lett. 33(7), 934–942 (2012)CrossRefGoogle Scholar
  14. 14.
    Fischer, A., Indermühle, E., Bunke, H., Viehhauser, G., Stolz, M.: Ground truth creation for handwriting recognition in historical documents. In: International Workshop on Document Analysis Systems, New York, USA, pp. 3–10 (2010)Google Scholar
  15. 15.
    Guo, Z., Hall, R.W.: Parallel thinning with two-subiteration algorithms. Commun. ACM 32(3), 359–373 (1989)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Fischer, A., Riesen, K., Bunke, H.: Graph similarity features for HMM-based handwriting recognition in historical documents. In: International Conference on Frontiers in Handwriting Recognition, pp. 253–258 (2010)Google Scholar
  17. 17.
    Stauffer, M., Fischer, A., Riesen, K.: Graph-based keyword spotting in historical handwritten documents. In: International Workshop on Structural and Syntactic Pattern Recognition (2016)Google Scholar
  18. 18.
    Rodriguez, J.A., Perronnin, F.: Local gradient histogram features for word spotting in unconstrained handwritten documents. In: International Conference on Frontiers in Handwriting Recognition, pp. 7–12 (2008)Google Scholar
  19. 19.
    Almazán, J., Gordo, A., Fornés, A., Valveny, E.: Segmentation-free word spotting with exemplar SVMs. Pattern Recogn. 47(12), 3967–3978 (2014)CrossRefGoogle Scholar
  20. 20.
    Hull, J.: Survey and annotated bibliography. Series in Mach. Percept. Artif. Intell. 29, 40–64 (1998)CrossRefGoogle Scholar
  21. 21.
    Rath, T., Manmatha, R.: Word image matching using dynamic time warping. In: Computer Vision and Pattern Recognition, vol. 2, pp. II-521–II-527 (2003)Google Scholar
  22. 22.
    Riesen, K., Bunke, H.: Approximate graph edit distance computation by means of bipartite graph matching. Image Vis. Comput. 27(7), 950–959 (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Michael Stauffer
    • 1
    • 3
    Email author
  • Andreas Fischer
    • 2
  • Kaspar Riesen
    • 1
  1. 1.Institute for Information SystemsUniversity of Applied Sciences and Arts Northwestern SwitzerlandOltenSwitzerland
  2. 2.University of Fribourg and HES-SOFribourgSwitzerland
  3. 3.Department of InformaticsUniversity of PretoriaPretoriaSouth Africa

Personalised recommendations