• DocumentCode
    3489359
  • Title

    Triangular Mesh Based Stroke Segmentation for Chinese Calligraphy

  • Author

    Xiaoqing Wang ; Xiaohui Liang ; Linjia Sun ; Min Liu

  • Author_Institution
    Key Lab. of virtual Reality Technol. & Syst., Beihang Univ., Beijing, China
  • fYear
    2013
  • fDate
    25-28 Aug. 2013
  • Firstpage
    1155
  • Lastpage
    1159
  • Abstract
    This paper proposes a novel stroke extraction method for the Chinese character. In our method, a Chinese character is represented as a set of triangular mesh that is generated by using the canny contour detector and the constraint Delaunay triangulation (CDT). Based on the representation, the singular regions and the sub-strokes are firstly determined by the properties of triangular mesh. The point-to-boundary orientation distance (PBOD) of one triangular mesh is generated to discriminate whether it should be involved in singular regions. Then, the singular regions and sub-strokes are modeled with a graph. Two sub-strokes are connected if they are continuous in the singular region and recover the part contour of the stroke damaged by the singular region. Finally, this method is used to extract the stroke with variable width in Chinese characters, such as type of Kai in the tablet inscription. Experimental results show that the proposed method is feasible and effective.
  • Keywords
    character recognition; feature extraction; graph theory; image segmentation; mesh generation; CDT; Canny contour detector; Chinese calligraphy; Chinese character; PBOD; constraint Delaunay triangulation; graph; point-to-boundary orientation distance; singular regions; stroke extraction method; tablet inscription; triangular mesh based stroke segmentation; Character recognition; Data mining; Detectors; Feature extraction; Image segmentation; Junctions;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Document Analysis and Recognition (ICDAR), 2013 12th International Conference on
  • Conference_Location
    Washington, DC
  • ISSN
    1520-5363
  • Type

    conf

  • DOI
    10.1109/ICDAR.2013.234
  • Filename
    6628795