Support vector machines for mathematical symbol recognition

Authors:
Christopher Malon;Seiichi Uchida;Masakazu Suzuki
Affiliations:
Engineering Division, Faculty of Mathematics, Kyushu University, Fukuoka, Japan;Faculty of Information Science and Electrical Engineering, Kyushu University, Fukuoka, Japan;Engineering Division, Faculty of Mathematics, Kyushu University, Fukuoka, Japan
Venue:
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Year:
2006

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Abstract

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.