@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}
}
@conference {15894,
	title = {Probabilistic structured query methods},
	booktitle = {Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval},
	series = {SIGIR {\textquoteright}03},
	year = {2003},
	month = {2003///},
	pages = {338 - 344},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {Structured methods for query term replacement rely on separate estimates of term tes of replacement probabilities. Statistically significantfrequency and document frequency to compute a weight for each query term. This paper reviews prior work on structured query techniques and introduces three new variants that leverage estima improvements in retrieval effectiveness are demonstrated for cross-language retrieval and for retrieval based on optical character recognition when replacement probabilities are used to estimate both term frequency and document frequency.},
	keywords = {arabic, CLIR, OCR, structured queries, term replacement},
	isbn = {1-58113-646-3},
	doi = {10.1145/860435.860497},
	url = {http://doi.acm.org/10.1145/860435.860497},
	author = {Darwish,Kareem and Oard, Douglas}
}
@conference {15893,
	title = {Term selection for searching printed Arabic},
	booktitle = {Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval},
	series = {SIGIR {\textquoteright}02},
	year = {2002},
	month = {2002///},
	pages = {261 - 268},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	abstract = {Since many Arabic documents are available only in print, automating retrieval from collections of scanned Arabic document images using Optical Character Recognition (OCR) is an interesting problem. Arabic combines rich morphology with a writing system that presents unique challenges to OCR systems. These factors must be considered when selecting terms for automatic indexing. In this paper, alternative choices of indexing terms are explored using both an existing electronic text collection and a newly developed collection built from images of actual printed Arabic documents. Character n-grams or lightly stemmed words were found to typically yield near-optimal retrieval effectiveness, and combining both types of terms resulted in robust performance across a broad range of conditions.},
	keywords = {arabic, Information retrieval, OCR, term selection},
	isbn = {1-58113-561-0},
	doi = {10.1145/564376.564423},
	url = {http://doi.acm.org/10.1145/564376.564423},
	author = {Darwish,Kareem and Oard, Douglas}
}
