EasyFont: A Style Learning-Based System to Easily Build Your Large-Scale Handwriting Fonts
EasyFont: A Style Learning-Based System to Easily Build Your Large-Scale Handwriting Fonts
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EasyFont：基于风格学习的系统，可轻松构建大型手写字体
DOI：
10.1145/3213767
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发表时间：
2019
影响因子：
6.2
通讯作者：
Jianguo Xiao
中科院分区：
文献类型：
--
作者：
Zhouhui Lian;Bo Zhao;Xudong Chen;Jianguo Xiao
生成具有大量字符的个人手写字体是一项无聊且耗时的任务。例如，商业字体产品的官方标准GB 18030 -2000由27，533个汉字组成。连贯和正确地写出如此巨大数量的字符通常是一个不可能的使命的普通人。为了解决这个问题，我们提出了一个系统，EasyFont，自动合成个人笔迹的所有（例如，通过从一小部分（只有1%）由普通人书写的精心挑选的样本中学习风格，来识别字体库中的（中文）字符。我们系统的主要技术贡献是双重的。首先，我们设计了一个有效的笔画提取算法，从训练好的字体骨架流形中构造最适合的参考数据，然后通过非刚性点集配准方法建立目标和参考字符之间的对应关系。其次，我们开发了一套新的技术来学习和恢复用户的整体手写风格和详细的手写行为。包括97名参与者的图灵测试的实验表明，该系统生成高质量的合成结果，这是从原来的笔迹难以区分。使用我们的系统，第一次，实用的手写字体库中的用户的个人风格与任意大数量的汉字可以自动生成。从我们的实验中还可以观察到，最近流行的基于深度学习的端到端方法无法正确处理此任务，这意味着许多应用程序需要专家知识和手工规则。
Generating personal handwriting fonts with large amounts of characters is a boring and time-consuming task. For example, the official standard GB18030-2000 for commercial font products consists of 27,533 Chinese characters. Consistently and correctly writing out such huge amounts of characters is usually an impossible mission for ordinary people. To solve this problem, we propose a system,EasyFont, to automatically synthesize personal handwriting for all (e.g., Chinese) characters in the font library by learning style from a small number (as few as 1%) of carefully-selected samples written by an ordinary person. Major technical contributions of our system are twofold. First, we design an effective stroke extraction algorithm that constructs best-suited reference data from a trained font skeleton manifold and then establishes correspondence between target and reference characters via a non-rigid point set registration approach. Second, we develop a set of novel techniques to learn and recover users’ overall handwriting styles and detailed handwriting behaviors. Experiments including Turing tests with 97 participants demonstrate that the proposed system generates high-quality synthesis results, which are indistinguishable from original handwritings. Using our system, for the first time, the practical handwriting font library in a user’s personal style with arbitrarily large numbers of Chinese characters can be generated automatically. It can also be observed from our experiments that recently-popularized deep learning based end-to-end methods are not able to properly handle this task, which implies the necessity of expert knowledge and handcrafted rules for many applications.