Multi-stage HMM based Arabic text recognition with rescoring

Irfan Ahmad, Gernot A. Fink

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Scopus citations

Abstract

In this paper, we present a multi-stage approach to handwritten Arabic text recognition using HMM where we separate the Arabic text image into core components and diacritics and recognize them separately using two separate HMM recognition systems. In the next stage, we combine the scores from both recognizers to make a final word hypothesis. This approach leads to huge reduction in the number of HMM models that need to be trained. Experiments conducted on a word recognition task using a publicly available benchmark database show the effectiveness of the technique. We achieve state-of-the-art results in addition to a compact model set for the recognition system.

Original languageEnglish
Title of host publication13th IAPR International Conference on Document Analysis and Recognition, ICDAR 2015 - Conference Proceedings
PublisherIEEE Computer Society
Pages751-755
Number of pages5
ISBN (Electronic)9781479918058
DOIs
StatePublished - 20 Nov 2015

Publication series

NameProceedings of the International Conference on Document Analysis and Recognition, ICDAR
Volume2015-November
ISSN (Print)1520-5363

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

Keywords

  • Arabic text recognition
  • Handwritten text recognition
  • Model set reduction
  • hidden Markov models
  • multi-stage recognition
  • rescoring

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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