Support Vector Machines for Mathematical Symbol Recognition

  • Christopher Malon
  • Seiichi Uchida
  • Masakazu Suzuki
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 
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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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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