@conference {13651,
	title = {Stroke-like Pattern Noise Removal in Binary Document Images},
	booktitle = {International Conference on Document Analysis and Recognition},
	year = {2011},
	month = {2011///},
	pages = {17 - 21},
	abstract = {This paper presents a two-phased stroke-like pattern noise (SPN) removal algorithm for binary document images. The proposed approach aims at understanding script-independent prominent text component features using supervised classification as a first step. It then uses their cohesiveness and stroke-width properties to filter and associate smaller text components with them using an unsupervised classification technique. In order to perform text extraction, and hence noise removal, at diacritic-level, this divide-and-conquer technique does not assume the availability of accurate and large amounts of ground-truth data at component-level for training purposes. The method was tested on a collection of degraded and noisy, machine-printed and handwritten binary Arabic text documents. Results show pixel-level precision and recall of 98\% and 97\% respectively.},
	author = {Agrawal,Mudit and David Doermann}
}
