Handwritten Arabic text line segmentation using affinity propagation

  • Authors:

  • Jayant Kumar;Wael Abd-Almageed;Le Kang;David Doermann

  • Affiliations:

  • University of Maryland, MD;University of Maryland, MD;University of Maryland, MD;University of Maryland, MD

  • Venue:
  • DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
  • Year:
  • 2010

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Abstract

In this paper, we present a novel graph-based method for extracting handwritten text lines in monochromatic Arabic document images. Our approach consists of two steps - Coarse text line estimation using primary components which define the line and assignment of diacritic components which are more difficult to associate with a given line. We first estimate local orientation at each primary component to build a sparse similarity graph. We then, use a shortest path algorithm to compute similarities between non-neighboring components. From this graph, we obtain coarse text lines using two estimates obtained from Affinity propagation and Breadth-first search. In the second step, we assign secondary components to each text line. The proposed method is very fast and robust to non-uniform skew and character size variations, normally present in handwritten text lines. We evaluate our method using a pixel-matching criteria, and report 96% accuracy on a dataset of 125 Arabic document images. We also present a proximity analysis on datasets generated by artificially decreasing the spacings between text lines to demonstrate the robustness of our approach.