Support Vector Machines for Mathematical Symbol Recognition

  • Christopher Malon
  • Seiichi Uchida
  • Masakazu Suzuki
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4109)


Mathematical formulas challenge an OCR system with a range of similar-looking characters whose bold, calligraphic, and italic varieties must be recognized distinctly, though the fonts to be used in an article are not known in advance. We describe the use of support vector machines (SVM) to learn and predict about 300 classes of styled characters and symbols.


Support Vector Machine Confusion Matrix Directional Feature Linear Support Vector Machine Soft Margin 


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  1. 1.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)MATHGoogle Scholar
  2. 2.
    Eto, Y., Suzuki, M.: Mathematical formula recognition using virtual link network. In: Sixth International Conference on Document Analysis and Recognition (ICDAR), pp. 430–437. IEEE Computer Society Press, Los Alamitos (2001)Google Scholar
  3. 3.
    Hsu, C.-W., Chang, C.-C., Lin, C.-J.: A practical guide to support vector classification (July 2003),
  4. 4.
    Hsu, C.-W., Lin, C.-J.: A comparison of methods for multi-class support vector machines. IEEE Transactions on Neural Networks 13, 415–425 (2002)CrossRefGoogle Scholar
  5. 5.
  6. 6.
    Keerthi, S.S., Lin, C.-J.: Asymptotic behaviors of support vector machines with gaussian kernel. Neural Comput. 15(7), 1667–1689 (2003)MATHCrossRefGoogle Scholar
  7. 7.
    Suzuki, M., Tamari, F., Fukuda, R., Uchida, S., Kanahori, T.: Infty: an integrated ocr system for mathematical documents. In: DocEng 2003: Proceedings of the 2003 ACM symposium on Document engineering, pp. 95–104. ACM Press, New York (2003)CrossRefGoogle Scholar
  8. 8.
    Suzuki, M., Uchida, S., Nomura, A.: A ground-truthed mathematical character and symbol image database. In: ICDAR 2005: Proceedings of the Eighth International Conference on Document Analysis and Recognition (ICDAR 2005), Washington, DC, USA, pp. 675–679. IEEE Computer Society, Los Alamitos (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Christopher Malon
    • 1
  • Seiichi Uchida
    • 2
  • Masakazu Suzuki
    • 1
  1. 1.Engineering Division, Faculty of MathematicsKyushu UniversityFukuokaJapan
  2. 2.Faculty of Information Science and Electrical EngineeringKyushu UniversityFukuokaJapan

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