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.
CITATION STYLE
Saavedra, J. M. (2014). Handwritten digit recognition based on pooling SVM-classifiers using orientation and concavity based features. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8827, pp. 658–665). Springer Verlag. https://doi.org/10.1007/978-3-319-12568-8_80
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