Hand drawn symbol recognition by blurred shape model descriptor and a multiclass classifier

Alicia Fornés; Sergio Escalera; Josep Lladós; Gemma Sánchez; Joan Mas

doi:10.1007/978-3-540-88188-9_4

Hand drawn symbol recognition by blurred shape model descriptor and a multiclass classifier

Alicia Fornés^*, Sergio Escalera, Josep Lladós, Gemma Sánchez, Joan Mas

^*Corresponding author for this work

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

3 Citations (Scopus)

Abstract

In the document analysis field, the recognition of handwriting symbols is a difficult task because of the distortions due to hand drawings and the different writer styles. In this paper, we propose the Blurred Shape Model to describe handwritten symbols, and the use of Adaboost in an Error Correcting Codes framework to deal with multi-class categorization handwriting problems. It is a robust approach tolerant to the distortions and variability typically found in handwritten documents. This approach has been evaluated with the public GREC2005 database and an architectural symbol database extracted from a sketching interface, reaching high recognition rates compared with the state-of-the-art approaches.

Original language	English
Title of host publication	Graphics Recognition : Recent Advances and New Opportunities - 7th International Workshop, GREC 2007, Selected Papers
Number of pages	11
Publisher	Springer
Publication date	2008
Pages	29-39
ISBN (Print)	3540881840, 9783540881841
DOIs	https://doi.org/10.1007/978-3-540-88188-9_4
Publication status	Published - 2008
Externally published	Yes
Event	7th International Workshop on Graphics Recognition, GREC 2007 - Curitiba, Brazil Duration: 20 Sept 2007 → 21 Sept 2007

Conference

Conference	7th International Workshop on Graphics Recognition, GREC 2007
Country/Territory	Brazil
City	Curitiba
Period	20/09/2007 → 21/09/2007

Series	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5046 LNCS
ISSN	0302-9743

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Fornés, Alicia ; Escalera, Sergio ; Lladós, Josep et al. / Hand drawn symbol recognition by blurred shape model descriptor and a multiclass classifier. Graphics Recognition: Recent Advances and New Opportunities - 7th International Workshop, GREC 2007, Selected Papers. Springer, 2008. pp. 29-39 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 5046 LNCS).

@inproceedings{7a384e55cbc247e7b5b2a6bffc6ae649,

title = "Hand drawn symbol recognition by blurred shape model descriptor and a multiclass classifier",

abstract = "In the document analysis field, the recognition of handwriting symbols is a difficult task because of the distortions due to hand drawings and the different writer styles. In this paper, we propose the Blurred Shape Model to describe handwritten symbols, and the use of Adaboost in an Error Correcting Codes framework to deal with multi-class categorization handwriting problems. It is a robust approach tolerant to the distortions and variability typically found in handwritten documents. This approach has been evaluated with the public GREC2005 database and an architectural symbol database extracted from a sketching interface, reaching high recognition rates compared with the state-of-the-art approaches.",

author = "Alicia Forn{\'e}s and Sergio Escalera and Josep Llad{\'o}s and Gemma S{\'a}nchez and Joan Mas",

year = "2008",

doi = "10.1007/978-3-540-88188-9_4",

language = "English",

isbn = "3540881840",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "29--39",

booktitle = "Graphics Recognition",

address = "Germany",

note = "7th International Workshop on Graphics Recognition, GREC 2007 ; Conference date: 20-09-2007 Through 21-09-2007",

}

Fornés, A, Escalera, S, Lladós, J, Sánchez, G & Mas, J 2008, Hand drawn symbol recognition by blurred shape model descriptor and a multiclass classifier. in Graphics Recognition: Recent Advances and New Opportunities - 7th International Workshop, GREC 2007, Selected Papers. Springer, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5046 LNCS, pp. 29-39, 7th International Workshop on Graphics Recognition, GREC 2007, Curitiba, Brazil, 20/09/2007. https://doi.org/10.1007/978-3-540-88188-9_4

Hand drawn symbol recognition by blurred shape model descriptor and a multiclass classifier. / Fornés, Alicia; Escalera, Sergio; Lladós, Josep et al.
Graphics Recognition: Recent Advances and New Opportunities - 7th International Workshop, GREC 2007, Selected Papers. Springer, 2008. p. 29-39 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 5046 LNCS).

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

TY - GEN

T1 - Hand drawn symbol recognition by blurred shape model descriptor and a multiclass classifier

AU - Fornés, Alicia

AU - Escalera, Sergio

AU - Lladós, Josep

AU - Sánchez, Gemma

AU - Mas, Joan

PY - 2008

Y1 - 2008

N2 - In the document analysis field, the recognition of handwriting symbols is a difficult task because of the distortions due to hand drawings and the different writer styles. In this paper, we propose the Blurred Shape Model to describe handwritten symbols, and the use of Adaboost in an Error Correcting Codes framework to deal with multi-class categorization handwriting problems. It is a robust approach tolerant to the distortions and variability typically found in handwritten documents. This approach has been evaluated with the public GREC2005 database and an architectural symbol database extracted from a sketching interface, reaching high recognition rates compared with the state-of-the-art approaches.

AB - In the document analysis field, the recognition of handwriting symbols is a difficult task because of the distortions due to hand drawings and the different writer styles. In this paper, we propose the Blurred Shape Model to describe handwritten symbols, and the use of Adaboost in an Error Correcting Codes framework to deal with multi-class categorization handwriting problems. It is a robust approach tolerant to the distortions and variability typically found in handwritten documents. This approach has been evaluated with the public GREC2005 database and an architectural symbol database extracted from a sketching interface, reaching high recognition rates compared with the state-of-the-art approaches.

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U2 - 10.1007/978-3-540-88188-9_4

DO - 10.1007/978-3-540-88188-9_4

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SN - 3540881840

SN - 9783540881841

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Fornés A, Escalera S, Lladós J, Sánchez G, Mas J. Hand drawn symbol recognition by blurred shape model descriptor and a multiclass classifier. In Graphics Recognition: Recent Advances and New Opportunities - 7th International Workshop, GREC 2007, Selected Papers. Springer. 2008. p. 29-39. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 5046 LNCS). doi: 10.1007/978-3-540-88188-9_4

Hand drawn symbol recognition by blurred shape model descriptor and a multiclass classifier

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