Maximizing Data Efficiency of HTR Models by Synthetic Text
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-70442-0_18
Reference23 articles.
1. Atienza, R.: Data augmentation for scene text recognition. In: 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), pp. 1561–1570. IEEE, Montreal (2021). https://doi.org/10.1109/ICCVW54120.2021.00181, https://ieeexplore.ieee.org/document/9607714/
2. Bhunia, A.K., Khan, S., Cholakkal, H., Anwer, R.M., Khan, F.S., Shah, M.: Handwriting transformers. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 1086–1094 (2021)
3. Carbonell, M., Mas, J., Villegas, M., Fornés, A., Lladós, J.: End-to-end handwritten text detection and transcription in full pages. In: 2019 International Conference on Document Analysis and Recognition Workshops (ICDARW), vol. 5, pp. 29–34. IEEE (2019)
4. Lecture Notes in Computer Science;S Cascianelli,2021
5. Coquenet, D.: towards end-to-end handwritten document recognition. Ph.D. thesis, Rouen University, France (2022)
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