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TrueType Transformer: Character and Font Style Recognition in Outline Format

2022; Springer Science+Business Media; Linguagem: Inglês

10.1007/978-3-031-06555-2_2

1611-3349

Yusuke Nagata, Jinki Otao, Daichi Haraguchi, Seiichi Uchida,

Digital Media Forensic Detection

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.