• DocumentCode
    3695271
  • Title

    ICDAR2015 competition on recognition of documents with complex layouts - RDCL2015

  • Author

    A. Antonacopoulos;C. Clausner;C. Papadopoulos;S. Pletschacher

  • Author_Institution
    Pattern Recognition and Image Analysis (PRImA) Research Lab, School of Computing, Science and Engineering, University of Salford, Greater Manchester, M5 4WT, United Kingdom
  • fYear
    2015
  • Firstpage
    1151
  • Lastpage
    1155
  • Abstract
    This paper presents an objective comparative evaluation of page segmentation and region classification methods for documents with complex layouts. It describes the competition (modus operandi, dataset and evaluation methodology) held in the context of ICDAR2015, presenting the results of the evaluation of eight methods - four submitted, two state-of-the-art systems (one commercial and one open-source) and their two immediately previous versions. Three scenarios are reported in this paper, one evaluating the ability of methods to accurately segment regions and two evaluating both segmentation and region classification (one with emphasis on text and the other focusing only on text). The results indicate that an innovative approach has a clear advantage but there is still a considerable need to develop robust methods that deal with layout challenges, especially with the non-text content.
  • Keywords
    Optical character recognition software
  • Publisher
    ieee
  • Conference_Titel
    Document Analysis and Recognition (ICDAR), 2015 13th International Conference on
  • Type

    conf

  • DOI
    10.1109/ICDAR.2015.7333941
  • Filename
    7333941