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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.

- Publication
- IEICE TRANSACTIONS on Information Vol.E92-D No.11 pp.2235-2243

- Publication Date
- 2009/11/01

- Publicized

- Online ISSN
- 1745-1361

- DOI
- 10.1587/transinf.E92.D.2235

- Type of Manuscript
- PAPER

- Category
- Pattern Recognition

The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.

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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 -