• DocumentCode
    1659969
  • Title

    Text detection in born-digital images using multiple layer images

  • Author

    Chao Zeng ; Wenjing Jia ; Xiangjian He

  • Author_Institution
    Res. Centre for Innovation in IT Services & Applic. (iNEXT), Univ. of Technol., Sydney, NSW, Australia
  • fYear
    2013
  • Firstpage
    1947
  • Lastpage
    1951
  • Abstract
    In this paper, a new framework for detecting text from webpage and email images is presented. The original image is split into multiple layer images based on the maximum gradient difference (MGD) values to detect text with both strong and weak contrasts. Connected component processing and text detection are performed in each layer image. A novel texture descriptor named T-LBP, is proposed to further filter out non-text candidates with a trained SVM classifier. The ICDAR 2011 born-digital image dataset is used to evaluate and demonstrate the performance of the proposed method. Following the same performance evaluation criteria, the proposed method outperforms the winner algorithm of the ICDAR 2011 Robust Reading Competition Challenge 1.
  • Keywords
    gradient methods; image texture; object detection; support vector machines; visual databases; ICDAR 2011 Robust Reading Competition Challenge 1; MGD values; SVM classifier; T-LBP; Web page; born-digital image dataset; email images; maximum gradient difference values; multiple layer images; nontext candidates; performance evaluation criteria; strong contrasts; text detection; texture descriptor; weak contrasts; winner algorithm; Conferences; Electronic mail; Image edge detection; Pattern recognition; Robustness; Support vector machines; Vectors; Multiple layer image; T-LBP; maximum gradient difference; text detection;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on
  • Conference_Location
    Vancouver, BC
  • ISSN
    1520-6149
  • Type

    conf

  • DOI
    10.1109/ICASSP.2013.6637993
  • Filename
    6637993