• DocumentCode
    2199060
  • Title

    SCUT-COUCH Textline_NU: An Unconstrained Online Handwritten Chinese Text Lines Dataset

  • Author

    Yan, Hanyu ; Jin, Lianwen ; Viard-gaudin, Christian ; Mouchère, Harold

  • Author_Institution
    Sch. of Electron. & Inf. Eng., South China Univ. of Technol., Guangzhou, China
  • fYear
    2010
  • fDate
    16-18 Nov. 2010
  • Firstpage
    581
  • Lastpage
    586
  • Abstract
    An unconstrained online handwritten Chinese text lines dataset, SCUT-COUCH Textline_NU, a subset of SCUT-COUCH [1] [2], is built to facilitate the research of unconstrained online Chinese text recognition. Texts for hand copying are sampled from China Daily corpus with a stratified random manner. The current vision of SCUT-COUCH Textline_NU has 8,809 text lines (4,813 lines are collected by touch screen LCD and 3,996 by digital pen) and 159,866 characters in total that are written by more than 157 participants. To demonstrate that the dataset is practical, an over-segmentation, dynamic programming and semantic model based algorithm was presented for segmenting and recognizing the unconstrained online Chinese text lines. In preliminary experiments on the dataset, the proposed algorithm recognition achieves a baseline accuracy of 56.41%.
  • Keywords
    dynamic programming; handwritten character recognition; image segmentation; natural languages; set theory; text analysis; China daily corpus; SCUT-COUCH textline NU; dynamic programming; handcopying; over-segmentation; semantic model based algorithm; subset; unconstrained online Chinese text recognition; unconstrained online handwritten Chinese textline dataset; SCUT-COUCH Textline_NU; online Chinese handwritten dataset; online Chinese text line recognition;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Frontiers in Handwriting Recognition (ICFHR), 2010 International Conference on
  • Conference_Location
    Kolkata
  • Print_ISBN
    978-1-4244-8353-2
  • Type

    conf

  • DOI
    10.1109/ICFHR.2010.123
  • Filename
    5693626