Today, people rely almost full time on digital texts. It is not surprising that handwriting earned a special status, and solutions to mimic real handwriting became attractive. A particular field called handwriting synthesis generates renderings of text which resemble natural writing but are synthesized from actual handwriting samples. The main idea behind samples’ current solutions is to collect enough samples to capture a given subject’s writing style, and therefore be able to reproduce it in new texts, with natural variability. Nevertheless, the question remains of how much input variability is enough to represent specific handwriting. In this paper, we address sample acquisition for handwriting synthesis. We conducted a study comparing written text similarity between two sets of samples, one using augmented pangrams (with a total of 473 characters) and the other using general texts (with 1586 characters). Our results show that the samples collected with pangrams are statistically equivalent in variation with samples collected using general texts, with many benefits, particularly the shorter time needed to collect the samples. We also made our data collection publicly available, providing a valuable original resource for future research.

今天，人们几乎全时都依靠数字文本。手写获得特殊地位也就不足为奇了，并且模仿真实手写的解决方案变得有吸引力。一个称为笔迹合成的特定字段会生成类似于自然笔迹但由实际笔迹样本合成的文本渲染。样本当前解决方案背后的主要思想是收集足够的样本以捕捉给定主题的写作风格，因此能够在自然变化的情况下将其复制到新文本中。然而，问题仍然在于多少输入可变性足以代表特定的笔迹。在本文中，我们解决了手写合成的样本获取问题。我们进行了一项研究，比较了两组样本之间的书面文字相似度，一个使用增强的pangram（共473个字符），另一个使用常规文本（1586个字符）。我们的结果表明，使用pangram收集的样本与使用常规文本收集的样本在统计上是等效的，具有很多好处，尤其是收集样本所需的时间更短。我们还公开收集了数据，为将来的研究提供了宝贵的原始资源。