MixUp Data Augmentation for Handwritten Arabic Mathematical Symbols Recognition
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-55729-3_3
Reference29 articles.
1. Hadj Ali, I., Mahjoub, M.A.: Database of handwritten Arabic mathematical formula images. In: 2016 13th International Conference on Computer Graphics, Imaging and Visualization (CGiV), pp. 145–149. IEEE (2016)
2. Li, Z., Wang, X., Liu, Y., et al.: Improving handwritten mathematical expression recognition via similar symbol distinguishing. IEEE Trans. Multimed. (2023)
3. Zhang, J., Du, J., Zhang, S., et al.: Watch, attend and parse: an end-to-end neural network-based approach to handwritten mathematical expression recognition. Pattern Recogn. 71, 196–206 (2017)
4. Alvaro, F., Sánchez, J.-A., Benedí, J.-M.: Recognition of on-line handwritten mathematical expressions using 2D stochastic context-free grammars and hidden Markov models. Pattern Recogn. Lett. 35, 58–67 (2014)
5. Zhang, H., Cisse, M., Dauphin, Y.N., et al.: MixUp: beyond empirical risk minimization. arXiv:1710.09412 (2017)
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