Generating Handwriting via Decoupled Style Descriptors-Reference-Cited by-同舟云学术

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Generating Handwriting via Decoupled Style Descriptors

Published:2020 Issue: Volume: Page:764-780
ISSN:0302-9743
Container-title:Computer Vision – ECCV 2020
language:
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Author:

Kotani Atsunobu^ORCID,Tellex Stefanie^ORCID,Tompkin James^ORCID

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-58610-2_45

Reference41 articles.

1. Adak, C., Chaudhuri, B.B., Lin, C.T., Blumenstein, M.: Intra-variable handwriting inspection reinforced with idiosyncrasy analysis (2019)

2. Aksan, E., Pece, F., Hilliges, O.: DeepWriting: making digital ink editable via deep generative modeling. In: SIGCHI Conference on Human Factors in Computing Systems, CHI 2018. ACM, New York (2018)

3. Alonso, E., Moysset, B., Messina, R.O.: Adversarial generation of handwritten text images conditioned on sequences. arXiv abs/1903.00277 (2019)

4. Azadi, S., Fisher, M., Kim, V.G., Wang, Z., Shechtman, E., Darrell, T.: Multi-content GAN for few-shot font style transfer. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7564–7573 (2018)

5. Balashova, E., Bermano, A.H., Kim, V.G., DiVerdi, S., Hertzmann, A., Funkhouser, T.: Learning a stroke-based representation for fonts. Comput. Graph. Forum 38(1), 429–442 (2019). https://doi.org/10.1111/cgf.13540. https://onlinelibrary.wiley.com/doi/abs/10.1111/cgf.13540

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