%0 Conference Paper %B Proceedings of the 9th IAPR International Workshop on Document Analysis Systems %D 2010 %T Handwritten Arabic text line segmentation using affinity propagation %A Kumar,Jayant %A Abd-Almageed, Wael %A Kang,Le %A David Doermann %K affinity propagation %K arabic %K arabic documents %K breadth-first search %K clustering %K dijkstra's shortest path algorithm %K handwritten documents %K line detection %K text line segmentation %X 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. %B Proceedings of the 9th IAPR International Workshop on Document Analysis Systems %S DAS '10 %I ACM %C New York, NY, USA %P 135 - 142 %8 2010/// %@ 978-1-60558-773-8 %G eng %U http://doi.acm.org/10.1145/1815330.1815348 %R 10.1145/1815330.1815348