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.
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Malon, C., Uchida, S., Suzuki, M. (2006). Support Vector Machines for Mathematical Symbol Recognition. In: Yeung, DY., Kwok, J.T., Fred, A., Roli, F., de Ridder, D. (eds) Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2006. Lecture Notes in Computer Science, vol 4109. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11815921_14
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DOI: https://doi.org/10.1007/11815921_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37236-3
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