Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/46146
Título: LBP based line-wise script identification
Autores/as: Ferrer, Miguel A. 
Morales, Aythami
Pal, Umapada
Clasificación UNESCO: 3307 Tecnología electrónica
Palabras clave: optical character recognition
least squares approximations
support vector machines
document image processing
text detection
Fecha de publicación: 2013
Publicación seriada: Proceedings of the International Conference on Document Analysis and Recognition, ICDAR
Conferencia: 12th International Conference on Document Analysis and Recognition (ICDAR) 
12th International Conference on Document Analysis and Recognition, ICDAR 2013 
Resumen: Script identification is an important step in multi-script document analysis. As different textures present in text portion of a script are the main distinct features of the script, in this paper, we proposed a new algorithm for printed script identification based on texture analysis. Since local patterns is a unifying concept for traditional statistical and structural approaches of texture analysis, here the basic idea is to use the histogram of the local patterns as description of the script stroke directions distribution which is the characteristic of every script. As local pattern, the basic version of the Local Binary Patterns (LBP) and a modified version of the Orientation of the Local Binary Patterns (OLBP) are proposed. A Least Square Support Vector Machine (LS-SVM) is used as identifier. The scheme has been verified on two databases. The first or training database is a database with 200 sheets of 10 different scripts. The scripts font is provided by the Google translator. The second or test database has been obtained by scanning different newspapers and books. It contains 5 common scripts among 10 different scripts of the first database. From the experiment we obtained encouraging results.
URI: http://hdl.handle.net/10553/46146
ISSN: 1520-5363
DOI: 10.1109/ICDAR.2013.81
Fuente: Proceedings of the International Conference on Document Analysis and Recognition, ICDAR[ISSN 1520-5363] (6628646), p. 369-373
Colección:Actas de congresos
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