Arabic Printed Word Recognition Using Windowed Bernoulli HMMs

  • Ihab Khoury
  • Adrià Giménez
  • Alfons Juan
  • Jesús Andrés-Ferrer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8156)

Abstract

Hidden Markov Models (HMMs) are now widely used for off-line text recognition in many languages and, in particular, Arabic. In previous work, we proposed to directly use columns of raw, binary image pixels, which are directly fed into embedded Bernoulli (mixture) HMMs, that is, embedded HMMs in which the emission probabilities are modeled with Bernoulli mixtures. The idea was to by-pass feature extraction and to ensure that no discriminative information is filtered out during feature extraction, which in some sense is integrated into the recognition model. More recently, we extended the column bit vectors by means of a sliding window of adequate width to better capture image context at each horizontal position of the word image. However, these models might have limited capability to properly model vertical image distortions. In this paper, we have considered three methods of window repositioning after window extraction to overcome this limitation. Each sliding window is translated (repositioned) to align its center to the center of mass. Using this approach, state-of-art results are reported on the Arabic Printed Text Recognition (APTI) database.

Keywords

Bernoulli HMMs APTI Arabic Printed Recognition Sliding Window Repositioning 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Ihab Khoury
    • 1
  • Adrià Giménez
    • 1
  • Alfons Juan
    • 1
  • Jesús Andrés-Ferrer
    • 1
  1. 1.DSIC/ITIUniversitat Politècnica de ValènciaValènciaSpain

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