论文标题
通过脱钩样式描述符生成笔迹
Generating Handwriting via Decoupled Style Descriptors
论文作者
论文摘要
代表手写的中风风格的空间包括代表每个角色的风格和人类作家的整体风格的挑战。现有的VRNN表示手写的方法通常不会区分这些不同的样式组件，从而可以降低模型能力。取而代之的是，我们介绍了用于手写的脱钩样式描述符（DSD）模型，这些模型因角色和作者级样式而产生了影响，并允许我们的模型代表整体更大的样式空间。这种方法还提高了灵活性：给出了一些示例，我们可以以新的作家样式生成笔迹，现在还可以在作者样式上生成新角色的笔迹。在实验中，我们生成的结果优先于88％的最先进的基线方法，而在20位持有作家的作者识别任务中，我们的DSD从单个示例单词中获得了89.38％的精度。总体而言，DSD使我们能够提高现有手写中风生成方法的质量和灵活性。
Representing a space of handwriting stroke styles includes the challenge of representing both the style of each character and the overall style of the human writer. Existing VRNN approaches to representing handwriting often do not distinguish between these different style components, which can reduce model capability. Instead, we introduce the Decoupled Style Descriptor (DSD) model for handwriting, which factors both character- and writer-level styles and allows our model to represent an overall greater space of styles. This approach also increases flexibility: given a few examples, we can generate handwriting in new writer styles, and also now generate handwriting of new characters across writer styles. In experiments, our generated results were preferred over a state of the art baseline method 88% of the time, and in a writer identification task on 20 held-out writers, our DSDs achieved 89.38% accuracy from a single sample word. Overall, DSDs allows us to improve both the quality and flexibility over existing handwriting stroke generation approaches.
