TY - JOUR
T1 - Automatic classification of spatial relationships among mathematical symbols using geometric features
AU - Aly, Walaa
AU - Uchida, Seiichi
AU - Suzuki, Masakazu
PY - 2009
Y1 - 2009
N2 - Machine recognition of mathematical expressions on printed documents is not trivial even when all the individual characters and symbols in an expression can be recognized correctly. In this paper, an automatic classification method of spatial relationships between the adjacent symbols in a pair is presented. This classification is important to realize an accurate structure analysis module of math OCR. Experimental results on very large databases showed that this classification worked well with an accuracy of 99.525% by using distribution maps which are defined by two geometric features, relative size and relative position, with careful treatment on document-dependent characteristics.
AB - Machine recognition of mathematical expressions on printed documents is not trivial even when all the individual characters and symbols in an expression can be recognized correctly. In this paper, an automatic classification method of spatial relationships between the adjacent symbols in a pair is presented. This classification is important to realize an accurate structure analysis module of math OCR. Experimental results on very large databases showed that this classification worked well with an accuracy of 99.525% by using distribution maps which are defined by two geometric features, relative size and relative position, with careful treatment on document-dependent characteristics.
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U2 - 10.1587/transinf.E92.D.2235
DO - 10.1587/transinf.E92.D.2235
M3 - Article
AN - SCOPUS:77950259338
SN - 0916-8532
VL - E92-D
SP - 2235
EP - 2243
JO - IEICE Transactions on Information and Systems
JF - IEICE Transactions on Information and Systems
IS - 11
ER -