Handwriting Recognition Using HOG and Gabor Features
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-60594-9_6
Reference20 articles.
1. Gattal, A., Djeddi, C., Jamil, A., Bensefia, A.: Multi-scale oriented basic image features column for handwritten digit recognition. In: Abraham, A., et al. (eds.) Proceedings of the 12th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2020), pp. 289–298. Springer International Publishing, Cham (2021). https://doi.org/10.1007/978-3-030-73689-7_28
2. Bendib, I., Gattal, A., Marouane, G.: Handwritten digit recognition using deep CNN. In: Proceedings of the 1st International Conference on Intelligent Systems and Pattern Recognition. Association for Computing Machinery, Virtual Event, Tunisia, pp. 67–70 (2020). https://doi.org/10.1145/3432867.3432896
3. Gattal, A., Djeddi, C., Chibani, Y., Siddiqi, I.: Isolated handwritten digit recognition using oBIFs and background features. In: 2016 12th IAPR Workshop on Document Analysis Systems (DAS), pp. 305–310 (2016). https://doi.org/10.1145/3447568.3448465
4. Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278–2324 (1998). https://doi.org/10.1109/5.726791
5. Yamashita, Y., Wakahara, T.: Affine-Transformation and 2D-projection Invariant k-NN classification of handwritten characters via a new matching measure. Pattern Recogn. 52, 459–470 (2016). https://doi.org/10.1016/j.patcog.2015.10.002
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