• DocumentCode
    2929672
  • Title

    The image Text Recognition Graph (iTRG)

  • Author

    Saidane, Zohra ; Garcia, Christophe ; Dugelay, Jean Luc

  • Author_Institution
    Orange Labs., Cesson-Sevigne, France
  • fYear
    2009
  • fDate
    June 28 2009-July 3 2009
  • Firstpage
    266
  • Lastpage
    269
  • Abstract
    This paper presents a graph based scheme for color text recognition in images and videos, which is particularly robust to complex background, low resolution or video coding artifacts. This scheme is based on a novel method named the image text recognition graph (iTRG) composed of five main modules: an image text segmentation module, a graph connection builder module, a character recognition module, a graph weight calculator module and an optimal path search module. The first two modules are based on convolutional neural networks so that the proposed system automatically learns how to robustly perform segmentation and recognition. The proposed method is evaluated on the public ICDAR 2003 test word dataset.
  • Keywords
    graph theory; image colour analysis; image recognition; image resolution; image segmentation; neural nets; text analysis; video coding; character recognition module; convolutional neural networks; image text recognition graph; low-resolution image; optimal path search; public ICDAR 2003 test word dataset; video coding; Character recognition; Image recognition; Image resolution; Image segmentation; Modular construction; Optical character recognition software; Robustness; Testing; Text recognition; Videos;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Multimedia and Expo, 2009. ICME 2009. IEEE International Conference on
  • Conference_Location
    New York, NY
  • ISSN
    1945-7871
  • Print_ISBN
    978-1-4244-4290-4
  • Electronic_ISBN
    1945-7871
  • Type

    conf

  • DOI
    10.1109/ICME.2009.5202486
  • Filename
    5202486