Automatic Labeling of Handwritten Mathematical Symbols via Expression Matching

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6658)


Mathematical expression recognition is one of the challenging problems in the field of handwritten recognition. Public datasets are often used to evaluate and compare different computer solutions for recognition problems in several domains of applications. However, existing public datasets for handwritten mathematical expressions and symbols are still scarce both in number and in variety. Such scarcity makes large scale assessment of the existing techniques a difficult task. This paper proposes a novel approach, based on expression matching, for generating ground-truthed exemplars of expressions (and, therefore, of symbols). Matching is formulated as a graph matching problem in which symbols of input instances of a manually labeled model expression are matched to the symbols in the model. Pairwise matching cost considers both local and global features of the expression. Experimental results show achievement of high accuracy for several types of expressions, written by different users.


Match Rate Model Expression Handwritten Recognition Edge Cost Shape Context 
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.


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  1. 1.
    Blostein, D., Grbavec, A.: Recognition of mathematical notation. In: Bunke, H., Wang, P. (eds.) Handbook of Character Recognition and Document Image Analysis, pp. 557–582. World Scientific, Singapore (1997)CrossRefGoogle Scholar
  2. 2.
    Chan, K.F., Yeung, D.Y.: Mathematical expression recognition: A survey. International Journal on Document Analysis and Recognition 3, 3–15 (2000)CrossRefGoogle Scholar
  3. 3.
    Garain, U., Chaudhuri, B.B.: Recognition of online handwritten mathematical expressions. IEEE Trans Syst., Man, and Cybernetics Part B: Cybernetics 34(6), 2366–2376 (2004)CrossRefGoogle Scholar
  4. 4.
    Tapia, E., Rojas, R.: Recognition of on-line handwritten mathematical expressions using a minimal spanning tree construction and symbol dominance. In: Lladós, J., Kwon, Y.-B. (eds.) GREC 2003. LNCS, vol. 3088, pp. 329–340. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  5. 5.
    LaViola Jr., J.J., Zeleznik, R.C.: A practical approach for writer-dependent symbol recognition using a writer-independent symbol recognizer. IEEE Trans. Pattern Anal. Mach. Intell. 29, 1917–1926 (2007)CrossRefGoogle Scholar
  6. 6.
    Awal, A.M., Mouchère, H., Viard-Gaudin, C.: Towards handwritten mathematical expression recognition. In: Proceedings of 10th International Conference on Document Analysis and Recognition, pp. 1046–1050 (2009)Google Scholar
  7. 7.
    Suzuki, M., Uchida, S., Nomura, A.: A ground-truthed mathematical character and symbol image database. In: Proceedings of the Eighth International Conference on Document Analysis and Recognition, pp. 675–679. IEEE Computer Society, Los Alamitos (2005)Google Scholar
  8. 8.
    MacLean, S., Labahn, G., Lank, E., Marzouk, M., Tausky, D.: Grammar-based techniques for creating ground-truthed sketch corpora. International Journal on Document Analysis and Recognition, 1–10 (2010)Google Scholar
  9. 9.
    Noma, A., Pardo, A., Cesar Jr, R.M.: Structural matching of 2D electrophoresis gels using deformed graphs. Pattern Recognition Letters 32(1), 3–11 (2011)CrossRefGoogle Scholar
  10. 10.
    Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Trans. Pattern Anal. Mach. Intell. 24(4), 509–522 (2002)CrossRefGoogle Scholar
  11. 11.
    Kuhn, H.W.: The hungarian method for the assignment problem. Naval Res. Logist. Quart. 2, 83–97 (1955)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Jouili, S., Mili, I., Tabbone, S.: Attributed graph matching using local descriptions. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2009. LNCS, vol. 5807, pp. 89–99. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  13. 13.
    Riesen, K., Bunke, H.: Approximate graph edit distance computation by means of bipartite graph matching. Image Vision Comput. 27, 950–959 (2009)CrossRefGoogle Scholar

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© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Department of Computer Science, Institute of Mathematics and StatisticsUniversity of São PauloBrazil

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