Abstract
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.
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Keywords
- Voronoi Tessellation
- Foreground Pixel
- Gradient Orientation
- Handwriting Recognition
- Feature Extractor Technique
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Azeem, S.A., El Meseery, M.: Arabic handwriting recognition using concavity features and classifier fusion. In: Proceedings of the 2011 10th International Conference on Machine Learning and Applications and Workshops, ICMLA 2011, vol. 01, pp. 200–203 (2011)
Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (surf). Computer Vision and Image Understanding 110(3), 346–359 (2008)
Chang, C.C., Lin, C.J.: Libsvm: A library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 27:1–27:27 (2011)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 1, pp. 886–893. IEEE Computer Society (2005)
Diem, M., Fiel, S., Garz, A., Keglevic, M., Kleber, F., Sablatnig, R.: Icdar 2013 competition on handwritten digit recognition (hdrc 2013). In: 2013 12th International Conference on Document Analysis and Recognition (ICDAR), pp. 1422–1427 (2013)
Felzenszwalb, P., David, M., Deva, R.: A discriminatively trained, multiscale, deformable part model. In: International Conference on Computer Vision and Pattern Recognition (2008)
Heutte, L., Moreau, J., Plessis, B., Plagnaud, J., Lecourtier, Y.: Handwritten numeral recognition based on multiple feature extractors. In: Proceedings of the Second International Conference on Document Analysis and Recognition, pp. 167–170 (1993)
Hull, J.: A database for handwritten text recognition research. IEEE Transactions on Pattern Analysis and Machine Intelligence 16(5), 550–554 (1994)
Impedovo, S., Mangini, F., Pirlo, G., Barbuzzi, D., Impedovo, D.: Voronoi tessellation for effective and efficient handwritten digit classification. In: 2013 12th International Conference on Document Analysis and Recognition (ICDAR), pp. 435–439 (2013)
Ke, Y., Sukthankar, R.: Pca-sift: a more distinctive representation for local image descriptors. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 506–513. IEEE Computer Society (2004)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 2169–2178 (2006)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998)
Liu, C.L., Nakashima, K., Sako, H., Fujisawa, H.: Handwritten digit recognition: benchmarking of state-of-the-art techniques. Pattern Recognition 36(10), 2271–2285 (2003)
Liu, H., Ding, X.: Handwritten character recognition using gradient feature and quadratic classifier with multiple discrimination schemes. In: Proceedings of the Eighth International Conference on Document Analysis and Recognition, vol. 1, pp. 19–23 (2005)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)
Karic, M., Martinovic, G.: Improving offline handwritten digit recognition using concavity-based features. International Journal of Computers, Communications & Control 8(2), 220–234 (2013)
Oliveira, L., Sabourin, R., Bortolozzi, F., Suen, C.: Automatic recognition of handwritten numerical strings: a recognition and verification strategy. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(11), 1438–1454 (2002)
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Saavedra, J.M. (2014). Handwritten Digit Recognition Based on Pooling SVM-Classifiers Using Orientation and Concavity Based Features. In: Bayro-Corrochano, E., Hancock, E. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2014. Lecture Notes in Computer Science, vol 8827. Springer, Cham. https://doi.org/10.1007/978-3-319-12568-8_80
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DOI: https://doi.org/10.1007/978-3-319-12568-8_80
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