Current software tools for computer generation of Persian calligraphy can be mostly described as conventional fonts and typesetting software, which basically neglect the ‘variations’ of real calligraphy performed by hand, in terms of personalization to different calligraphers’ styles, as well as their statistical characteristics. In this paper, we address the problem of natural-looking Persian calligraphy synthesis via a machine learning based approach, at the level of subwords. Given images of samples written by a calligrapher, we train a parametric model to imitate the style. The core idea is to make use of templates (fonts) as a source of background knowledge, and learn a probabilistic mapping from them to personal styles of calligraphers, which is posed as transformation of attributed graphs using neural networks with sliding windows. This can be understood as adding ‘naturalness’ to a Persian calligraphy font, in essence. We report both objective and subjective evaluations, including the model performance in writer (calligrapher) identification task and Visual Turing Test. The results of the latter suggest that humans are unable to distinguish the calligraphy synthesized by our approach from real calligraphy in many cases.

中文翻译：

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通过学习将模板转换为个人风格来实现基于模型的波斯书法合成

当前用于计算机生成波斯书法的软件工具主要可以描述为常规字体和排版软件，它们在针对不同书法家的风格及其统计特征的个性化方面基本上忽略了手工进行的真实书法的“变化”。 。在本文中，我们通过基于机器学习的方法在子词级别上解决了波斯文字看起来自然的问题。给定书法家写的样本图像，我们训练一个参数化模型来模仿样式。核心思想是利用模板（字体）作为背景知识的来源，并学习从它们到书法家个人风格的概率映射，这被认为是使用带有滑动窗口的神经网络对属性图进行的转换。从本质上讲，这可以理解为为波斯书法字体添加“自然性”。我们报告了客观和主观的评估，包括在作者（书法家）识别任务和Visual Turing Test中的模型表现。后者的结果表明，在许多情况下，人类无法区分通过我们的方法合成的书法与真实的书法。