The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
La reconnaissance automatique d'expressions mathématiques sur des documents imprimés n'est pas anodine, même lorsque tous les caractères et symboles individuels d'une expression peuvent être reconnus correctement. Dans cet article, une méthode de classification automatique des relations spatiales entre les symboles adjacents dans une paire est présentée. Cette classification est importante pour réaliser un module d’analyse de structure précis de l’OCR mathématique. Les résultats expérimentaux sur de très grandes bases de données ont montré que cette classification fonctionnait bien avec une précision de 99.525 % en utilisant des cartes de distribution définies par deux caractéristiques géométriques, la taille relative et la position relative, avec un traitement attentif des caractéristiques dépendant du document.
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Walaa ALY, Seiichi UCHIDA, Masakazu SUZUKI, "Automatic Classification of Spatial Relationships among Mathematical Symbols Using Geometric Features" in IEICE TRANSACTIONS on Information,
vol. E92-D, no. 11, pp. 2235-2243, November 2009, doi: 10.1587/transinf.E92.D.2235.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E92.D.2235/_p
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@ARTICLE{e92-d_11_2235,
author={Walaa ALY, Seiichi UCHIDA, Masakazu SUZUKI, },
journal={IEICE TRANSACTIONS on Information},
title={Automatic Classification of Spatial Relationships among Mathematical Symbols Using Geometric Features},
year={2009},
volume={E92-D},
number={11},
pages={2235-2243},
abstract={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.},
keywords={},
doi={10.1587/transinf.E92.D.2235},
ISSN={1745-1361},
month={November},}
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TY - JOUR
TI - Automatic Classification of Spatial Relationships among Mathematical Symbols Using Geometric Features
T2 - IEICE TRANSACTIONS on Information
SP - 2235
EP - 2243
AU - Walaa ALY
AU - Seiichi UCHIDA
AU - Masakazu SUZUKI
PY - 2009
DO - 10.1587/transinf.E92.D.2235
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E92-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2009
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.
ER -