TrueType Transformer: Character and Font Style Recognition in Outline Format

We propose TrueType Transformer (T-3), 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. T-3 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, T-3 directly accepts the outline data without converting it into a bitmap image. Consequently, T-3 realizes a resolution-independent classification. Moreover, since the locations of the control points represent the fine and local structures of the font style, T-3 is suitable for font style classification, where such structures are very important. In this paper, we experimentally show the applicability of T-3 in character and font style recognition tasks, while observing how the individual control points contribute to classification results.