• DocumentCode
    2142169
  • Title

    CASIA Online and Offline Chinese Handwriting Databases

  • Author

    Liu, Cheng-Lin ; Yin, Fei ; Wang, Da-Han ; Wang, Qiu-Feng

  • Author_Institution
    Nat. Lab. of Pattern Recognition (NLPR), Inst. of Autom., Beijing, China
  • fYear
    2011
  • fDate
    18-21 Sept. 2011
  • Firstpage
    37
  • Lastpage
    41
  • Abstract
    This paper introduces a pair of online and offline Chinese handwriting databases, containing samples of isolated characters and handwritten texts. The samples were produced by 1,020 writers using Anoto pen on papers for obtaining both online trajectory data and offline images. Both the online samples and offline samples are divided into six datasets, three for isolated characters (DB1.0-C1.2) and three for handwritten texts (DB2.0-C2.2). The (either online or offline) datasets of isolated characters contain about 3.9 million samples of 7,356 classes (7,185 Chinese characters and 171 symbols), and the datasets of handwritten texts contain about 5,090 pages and 1.35 million character samples. Each dataset is segmented and annotated at character level, and is partitioned into standard training and test subsets. The online and offline databases can be used for the research of various handwritten document analysis tasks.
  • Keywords
    document image processing; handwritten character recognition; visual databases; Anoto pen on papers; CASIA; handwritten document analysis; handwritten texts; offline Chinese handwriting databases; offline databases; offline images; online Chinese handwriting databases; online trajectory data; standard training; test subsets; Character recognition; Databases; Handwriting recognition; Image segmentation; Text recognition; Training; Writing; Chinese handwriting databases; handwritten texts; isolated characters; offline; online;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Document Analysis and Recognition (ICDAR), 2011 International Conference on
  • Conference_Location
    Beijing
  • ISSN
    1520-5363
  • Print_ISBN
    978-1-4577-1350-7
  • Electronic_ISBN
    1520-5363
  • Type

    conf

  • DOI
    10.1109/ICDAR.2011.17
  • Filename
    6065272