DSS: Synthesizing Long Digital Ink Using Data Augmentation, Style Encoding and Split Generation

Author:

Timofeev Aleksandr,Fadeeva Anastasiia,Afonin Andrei,Musat Claudiu,Maksai Andrii

Publisher

Springer Nature Switzerland

Reference43 articles.

1. 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, New York, NY, USA. ACM (2018)

2. Alonso, E., Moysset, B., Messina, R.O.: Adversarial generation of handwritten text images conditioned on sequences. In: 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 481–486 (2019)

3. Anil, C., et al.: Exploring length generalization in large language models. In: Oh, A.H., Agarwal, A., Belgrave, D., Cho, K. (eds.) Advances in Neural Information Processing Systems (2022). https://openreview.net/forum?id=zSkYVeX7bC4

4. Bengio, S., Vinyals, O., Jaitly, N., Shazeer, N.M.: Scheduled sampling for sequence prediction with recurrent neural networks. arXiv:abs/1506.03099 (2015)

5. Bishop, C.M.: Mixture density networks (1994)

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