Handwritten digit recognition based on pooling SVM-classifiers using orientation and concavity based features

Jose M. Saavedra*

*Autor correspondiente de este trabajo

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

4 Citas (Scopus)

Resumen

In order to increase the performance in the handwritten digit recognition field, researchers commonly combine a variety of features to represent a pattern. This approach has showed to be very effective in practice. The classical approach to combine features is by concatenating the underlying feature vectors. A drawback of this approach is that it could generate high-dimensional descriptors, which increases the complexity of the training process. Instead, we propose to use a pooling based classifier, that allow us to get not only a faster training process but also outperforming results. For evaluation, we used two state-of-the-art handwritten digit datasets: CVL and MNIST. In addition, we show that a simple rectangular spatial division, that characterize our descriptors, yields competitive results and a smaller computation cost with respect to other more complex zoning techniques.

Idioma originalInglés
Título de la publicación alojadaProgress in Pattern Recognition Image Analysis, Computer Vision and Applications - 19th Iberoamerican Congress, CIARP 2014, Proceedings
EditoresEduardo Bayro-Corrochano, Edwin Hancock
EditorialSpringer Verlag
Páginas658-665
Número de páginas8
ISBN (versión digital)9783319125671
DOI
EstadoPublicada - 2014
Publicado de forma externa
Evento19th Iberoamerican Congress on Pattern Recognition, CIARP 2014 - Puerto Vallarta, México
Duración: 2 nov. 20145 nov. 2014

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen8827
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia19th Iberoamerican Congress on Pattern Recognition, CIARP 2014
País/TerritorioMéxico
CiudadPuerto Vallarta
Período2/11/145/11/14

Nota bibliográfica

Publisher Copyright:
© Springer International Publishing Switzerland 2014.

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