A New Approach for Segmentation and Recognition of Arabic Handwritten Touching Numeral Pairs

  • Authors:

  • Huda Alamri;Chun Lei He;Ching Y. Suen

  • Affiliations:

  • CENPARMI (Center for Pattern Recognition and Machine Intelligence), Computer Science and Software Engineering Department, Concordia University, Montreal, Canada;CENPARMI (Center for Pattern Recognition and Machine Intelligence), Computer Science and Software Engineering Department, Concordia University, Montreal, Canada;CENPARMI (Center for Pattern Recognition and Machine Intelligence), Computer Science and Software Engineering Department, Concordia University, Montreal, Canada

  • Venue:
  • CAIP '09 Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns
  • Year:
  • 2009

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Abstract

In this paper, we propose a new approach on segmentation and recognition of off-line unconstrained Arabic handwritten numerals, which failed to be segmented with connected component analysis. In our approach, the touching numerals are automatically segmented when a set of parameters is chosen. Models with different sets of parameters for each numeral pair are designed for recognition. Each image in each model is recognized as an isolated numeral. After normalizing and binarizing the images, gradient features are extracted and recognized using SVMs. Finally, a post-processing is proposed by based on the optimal combinations of the recognition probabilities for each model. Experiments were conducted on the CENPARMI Arabic, Dari, and Urdu touching numeral pair databases [1,12].