Abstract
Handwriting stroke generation is crucial for improving the performance of tasks such as handwriting recognition and writers order recovery. In handwriting stroke generation, it is significantly important to imitate the sample calligraphic style. The previous studies have suggested utilizing the calligraphic features of the handwriting. However, they had not considered word spacing (word layout) as an explicit handwriting feature, which results in inconsistent word spacing for style imitation. Firstly, this work proposes multi-scale attention features for calligraphic style imitation. These multi-scale feature embeddings highlight the local and global style features. Secondly, we propose to include the words layout, which facilitates word spacing for handwriting stroke generation. Moreover, we propose a conditional diffusion model to predict strokes in contrast to previous work, which directly generated style images. Stroke generation provides additional temporal coordinate information, which is lacking in image generation. Hence, our proposed conditional diffusion model for stroke generation is guided by calligraphic style and word layout for better handwriting imitation and stroke generation in a calligraphic style. Our experimentation shows that the proposed diffusion model outperforms the current state-of-the-art stroke generation and is competitive with recent image generation networks.
Abstract (translated)
手写笔画生成对于提高诸如手写识别和书写顺序恢复等任务的性能至关重要。在手写笔画生成过程中，模仿样本书法风格显得尤为重要。之前的研究建议利用手写的书法特征，但是他们没有将单词间距（词布局）视为显式的书法特征，这导致了风格模仿中的不一致的单词间距问题。 首先，这项工作提出了多尺度注意力特性用于模仿书法风格。这些多尺度特性嵌入突出了局部和全局的风格特征。其次，我们提议引入词布局，以帮助手写笔画生成时保持适当的单词间距。此外，我们提出了一种条件扩散模型来预测笔画，这与之前直接生成风格图像的工作不同。笔画生成提供了缺失于图像生成中的额外时间坐标信息。因此，我们的提出的用于笔画生成的条件扩散模型通过书法样式和词布局来进行指导，以更好地模仿手写并以书法风格生成笔画。 实验表明，所提出的扩散模型在笔画生成方面优于当前最先进的方法，并且与最近的图像生成网络竞争激烈。
URL
https://arxiv.org/abs/2509.15678
