Segmentation-free online arabic handwriting recognition

Fadi Biadsy, Raid Saabni, Jihad El-Sana

Research output: Contribution to journalArticlepeer-review

43 Scopus citations

Abstract

Arabic script is naturally cursive and unconstrained and, as a result, an automatic recognition of its handwriting is a challenging problem. The analysis of Arabic script is further complicated in comparison to Latin script due to obligatory dots/stokes that are placed above or below most letters. In this paper, we introduce a new approach that performs online Arabic word recognition on a continuous word-part level, while performing training on the letter level. In addition, we appropriately handle delayed strokes by first detecting them and then integrating them into the word-part body. Our current implementation is based on Hidden Markov Models (HMM) and correctly handles most of the Arabic script recognition difficulties. We have tested our implementation using various dictionaries and multiple writers and have achieved encouraging results for both writer-dependent and writer-independent recognition.

Original languageEnglish
Pages (from-to)1009-1033
Number of pages25
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Volume25
Issue number7
DOIs
StatePublished - 1 Nov 2011

Keywords

  • Arabic
  • HMM
  • Online handwriting recognition

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