• DocumentCode
    594799
  • Title

    Structured document classification by matching local salient features

  • Author

    Siyuan Chen ; Yuan He ; Jun Sun ; Naoi, Satoshi

  • fYear
    2012
  • fDate
    11-15 Nov. 2012
  • Firstpage
    653
  • Lastpage
    656
  • Abstract
    Following the recent trend in using low level image features in classifying document images, in this paper we present a novel approach for structured document classification by matching the salient feature points between the query image and the reference images. Our method is robust to diverse training data size, image formats and qualities. Through matching the feature points, image registration is available for the query image as well. Although we aimed for the large domain of the structured document images, our method already achieved zero error rates in the tests on the benchmark NIST tax form databases.
  • Keywords
    document image processing; feature extraction; image classification; image matching; image registration; query processing; visual databases; benchmark NIST tax; image formats; image qualities; image registration; local salient feature point matching; low level image features; query image; reference images; structured document image classification; training data size; zero error rates; Accuracy; Databases; Layout; NIST; Text analysis; Training; Training data;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Pattern Recognition (ICPR), 2012 21st International Conference on
  • Conference_Location
    Tsukuba
  • ISSN
    1051-4651
  • Print_ISBN
    978-1-4673-2216-4
  • Type

    conf

  • Filename
    6460219