Mathematical symbol recognition with support vector machines

Authors:
Christopher Malon;Seiichi Uchida;Masakazu Suzuki
Affiliations:
Faculty of Mathematics, Kyushu University, Hakozaki 6-10-1, Higashi-ku, Fukuoka 812-8581, Japan;Faculty of Information Science and Electrical Engineering, Kyushu University, Motooka 744, Nishi-ku, Fukuoka 819-0395, Japan;Faculty of Mathematics, Kyushu University, Hakozaki 6-10-1, Higashi-ku, Fukuoka 812-8581, Japan
Venue:
Pattern Recognition Letters
Year:
2008

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Abstract

Single-character recognition of mathematical symbols poses challenges from its two-dimensional pattern, the variety of similar symbols that must be recognized distinctly, the imbalance and paucity of training data available, and the impossibility of final verification through spell check. We investigate the use of support vector machines to improve the classification of InftyReader, a free system for the OCR of mathematical documents. First, we compare the performance of SVM kernels and feature definitions on pairs of letters that InftyReader usually confuses. Second, we describe a successful approach to multi-class classification with SVM, utilizing the ranking of alternatives within InftyReader's confusion clusters. The inclusion of our technique in InftyReader reduces its misrecognition rate by 41%.