论文标题
与字符无关的字体识别
Character-independent font identification
论文作者
论文摘要
有无数的字体，具有各种形状和样式。此外，还有许多字体在功能上只有细微的差异。因此，字体识别是一项艰巨的任务。在本文中，我们提出了一种确定任何两个字符是否来自同一字体的方法。由于字体之间的差异通常小于字母类别之间的差异，因此这很困难。此外，所提出的方法都可以与字体一起使用，无论它们是否存在于培训中。为了实现这一目标，我们使用了经过各种字体图像对训练的卷积神经网络（CNN）。在实验中，网络通过各种字体的图像对训练。然后，我们在网络看不见的不同字体上评估模型。评估的精度为92.27％。此外，我们分析了字符类和字体识别精度之间的关系。
There are a countless number of fonts with various shapes and styles. In addition, there are many fonts that only have subtle differences in features. Due to this, font identification is a difficult task. In this paper, we propose a method of determining if any two characters are from the same font or not. This is difficult due to the difference between fonts typically being smaller than the difference between alphabet classes. Additionally, the proposed method can be used with fonts regardless of whether they exist in the training or not. In order to accomplish this, we use a Convolutional Neural Network (CNN) trained with various font image pairs. In the experiment, the network is trained on image pairs of various fonts. We then evaluate the model on a different set of fonts that are unseen by the network. The evaluation is performed with an accuracy of 92.27%. Moreover, we analyzed the relationship between character classes and font identification accuracy.
