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Proceedings Paper

A hybrid classifier for handwritten mathematical expression recognition
Author(s): Ahmad-Montaser Awal; Harold Mouchère; Christian Viard-Gaudin
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Paper Abstract

In this paper we propose a hybrid symbol classifier within a global framework for online handwritten mathematical expression recognition. The proposed architecture aims at handling mathematical expression recognition as a simultaneous optimization of symbol segmentation, symbol recognition, and 2D structure recognition under the restriction of a mathematical expression grammar. To deal with the junk problem encountered when a segmentation graph approach is used, we consider a two level classifier. A symbol classifier cooperates with a second classifier specialized to accept or reject a segmentation hypothesis. The proposed system is trained with a set of synthetic online handwritten mathematical expressions. When tested on a set of real complex expressions, the system achieves promising results at both symbol and expression interpretation levels.

Paper Details

Date Published: 18 January 2010
PDF: 10 pages
Proc. SPIE 7534, Document Recognition and Retrieval XVII, 753410 (18 January 2010); doi: 10.1117/12.840023
Show Author Affiliations
Ahmad-Montaser Awal, IRCCyN/IVC, CNRS, Ecole Polytechnique de l'Univ. de Nantes (France)
Harold Mouchère, IRCCyN/IVC, CNRS, Ecole Polytechnique de l'Univ. de Nantes (France)
Christian Viard-Gaudin, IRCCyN/IVC, CNRS, Ecole Polytechnique de l'Univ. de Nantes (France)


Published in SPIE Proceedings Vol. 7534:
Document Recognition and Retrieval XVII
Laurence Likforman-Sulem; Gady Agam, Editor(s)

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