Mathematical symbol recognition with support vector machines

Christopher Malon, Seiichi Uchida, Masakazu Suzuki

Research output: Contribution to journalArticle

34 Citations (Scopus)

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%.

Original languageEnglish
Pages (from-to)1326-1332
Number of pages7
JournalPattern Recognition Letters
Volume29
Issue number9
DOIs
Publication statusPublished - Jul 1 2008

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All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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