NDLI: An Historical Handwritten Arabic Dataset for Segmentation-Free Word Spotting

Content Provider	IEEE Xplore Digital Library
Author	Pantke, W. Dennhardt, M. Fecker, D. Margner, V. Fingscheidt, T.
Copyright Year	2014
Description	Author affiliation: Inst. for Commun. Technol., Tech. Univ. Braunschweig, Braunschweig, Germany (Pantke, W.; Dennhardt, M.; Fecker, D.; Margner, V.; Fingscheidt, T.)
Abstract	In this paper, we present a new and freely available dataset comprising 80 pages of an historical handwritten Arabic document in conjunction with a detailed ground truth for the development and evaluation of segmentation-free word spotting approaches. Besides information on the underlying manuscript and technical details, we introduce a comprehensive list of tags that each word is labeled with. These tags can be used for research on specific issues such as dealing with text in different colors. For comparison of different word spotters, a fixed set of 25 keywords with different properties is included. Furthermore, some specifics of spotting on Arabic manuscripts are discussed. We exemplarily present a state-of-the-art word spotting algorithm in its original and a new extended implementation and evaluate both approaches on the new dataset. For comparison, they are also tested on the widely used George Washington dataset. It is shown that the extended word spotter outperforms the original version in terms of mean average precision on both datasets.
Starting Page	15
Ending Page	20
File Size	634423
Page Count	6
File Format	PDF
ISBN	9781479943357
ISSN	21676445
e-ISBN	9781479943340
DOI	10.1109/ICFHR.2014.11
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-09-01
Publisher Place	Greece
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image color analysis Books Image segmentation Image resolution Standards Writing Shape evaluation dataset historical Arabic handwriting segmentation-free word spotting
Content Type	Text
Resource Type	Article

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