@article {13488,
	title = {Adaptive Hindi OCRUsing Generalized Hausdorff Image Comparison},
	volume = {LAMP-TR-105,CFAR-TR-987,CS-TR-4519,UMIACS-TR-2003-87},
	year = {2003},
	month = {2003/08//},
	institution = {University of Maryland, College Park},
	abstract = {In this paper, we present an adaptive Hindi OCR using generalized Hausdor image comparison implemented as part of a rapidly retargetable language tool effort. The system includes: script identification, character segmentation, training sample creation and character recognition. The OCR design (completed in one month) was applied to a complete Hindi-English bilingual dictionary (with 1083 pages) and a collection of ideal images extracted from Hindi documents in PDF format. Experimental results show the recognition accuracy can reach 88\&$\#$x0025; for noisy images and 95\&$\#$x0025; for ideal images, both at the character level. The presented method can also be extended to design OCR systems for different scripts.},
	author = {Ma,Huanfeng and David Doermann}
}
