As text generative models can give increasingly long answers, we tackle the
problem of synthesizing long text in digital ink. We show that the commonly
used models for this task fail to generalize to long-form data and how this
problem can be solved by augmenting the training data, changing the model
architecture and the inference procedure. These methods use contrastive
learning technique and are tailored specifically for the handwriting domain.
They can be applied to any encoder-decoder model that works with digital ink.
We demonstrate that our method reduces the character error rate on long-form
English data by half compared to baseline RNN and by 16% compared to the
previous approach that aims at addressing the same problem. We show that all
three parts of the method improve recognizability of generated inks. In
addition, we evaluate synthesized data in a human study and find that people
perceive most of generated data as real.

使用对比学习技术改进训练数据、模型架构和推理过程，合成数字墨迹的长文本问题得以解决，可减少字符错误率，并通过人类研究证明生成数据真实可信。