@conference {13564,
	title = {Handwritten Arabic text line segmentation using affinity propagation},
	booktitle = {Proceedings of the 9th IAPR International Workshop on Document Analysis Systems},
	series = {DAS {\textquoteright}10},
	year = {2010},
	month = {2010///},
	pages = {135 - 142},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	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.},
	keywords = {affinity propagation, arabic, arabic documents, breadth-first search, clustering, dijkstra{\textquoteright}s shortest path algorithm, handwritten documents, line detection, text line segmentation},
	isbn = {978-1-60558-773-8},
	doi = {10.1145/1815330.1815348},
	url = {http://doi.acm.org/10.1145/1815330.1815348},
	author = {Kumar,Jayant and Abd-Almageed, Wael and Kang,Le and David Doermann}
}
