In this paper, we demonstrate a total disentanglement of font images. Total disentanglement is a neural network-based method for decomposing each font image nonlinearly and completely into its style and content (i.e., character class) features. It uses a simple but careful training procedure to extract the common style feature from all `A'-`Z' images in the same font and the common content feature from all `A' (or another class) images in different fonts. These disentangled features guarantee the reconstruction of the original font image. Various experiments have been conducted to understand the performance of total disentanglement. First, it is demonstrated that total disentanglement is achievable with very high accuracy; this is experimental proof of the long-standing open question, ``Does `A'-ness exist?'' Hofstadter (1985). Second, it is demonstrated that the disentangled features produced by total disentanglement apply to a variety of tasks, including font recognition, character recognition, and one-shot font image generation.

翻译：在本文中，我们展示了字体图像的完全解耦。完全解耦是一种基于神经网络的方法，能够将每个字体图像非线性和完全地分解为风格和内容（即字符类别）特征。该方法通过简单而精心设计的训练过程，从同一字体中所有'A'-'Z'图像中提取共同的风格特征，并从不同字体中所有'A'（或其他类别）图像中提取共同的内容特征。这些解耦后的特征保证了原始字体图像的重构。我们进行了多种实验以理解完全解耦的性能。首先，实验证明完全解耦可以达到非常高的精度；这为长期存在的开放性难题——"是否存在'A-ness'？"（Hofstadter, 1985）——提供了实验性证据。其次，实验表明完全解耦产生的特征适用于多种任务，包括字体识别、字符识别以及一次性字体图像生成。