论文标题
trueType变压器：概述格式的字符和字体样式识别
TrueType Transformer: Character and Font Style Recognition in Outline Format
论文作者
论文摘要
我们提出了TrueType Transformer（T3），该变压器可以以轮廓格式执行字符和字体样式识别。大纲格式（例如TrueType）代表每个字符作为中风轮廓的控制点的顺序，并且经常用于Born-Digial文档中。 T3由深层神经网络（所谓的变压器）组织。最初提出了用于顺序数据的变压器，例如文本，因此适用于处理轮廓数据。换句话说，T3直接接受大纲数据而不将其转换为位图映像。因此，T3实现了独立于解决方案的分类。此外，由于控制点的位置代表了字体样式的精细和局部结构，因此T3适用于字体样式的分类，在此结构非常重要。在本文中，我们通过实验表明T3在字体和字体样式识别任务中的适用性，同时观察单个控制点如何促进分类结果。
We propose TrueType Transformer (T3), which can perform character and font style recognition in an outline format. The outline format, such as TrueType, represents each character as a sequence of control points of stroke contours and is frequently used in born-digital documents. T3 is organized by a deep neural network, so-called Transformer. Transformer is originally proposed for sequential data, such as text, and therefore appropriate for handling the outline data. In other words, T3 directly accepts the outline data without converting it into a bitmap image. Consequently, T3 realizes a resolution-independent classification. Moreover, since the locations of the control points represent the fine and local structures of the font style, T3 is suitable for font style classification, where such structures are very important. In this paper, we experimentally show the applicability of T3 in character and font style recognition tasks, while observing how the individual control points contribute to classification results.
