NDLI: A discriminative learning approach for orientation detection of Urdu document images

Content Provider	IEEE Xplore Digital Library
Author	Rashid, S.F. Bukhari, S.S. Shafait, F. Breuel, T.M.
Copyright Year	2009
Description	Author affiliation: Image Understanding and Pattern Recognition (IUPR) Research Group, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany (Rashid, S.F.; Bukhari, S.S.; Breuel, T.M.) \|\| German Research Center for Artificial Intelligence (DFKI), D-67663 Kaiserslautern, Germany (Shafait, F.)
Abstract	Orientation detection is an important preprocessing step for accurate recognition of text from document images. Many existing orientation detection techniques are based on the fact that in Roman script text ascenders occur more likely than descenders, but this approach is not applicable to document of other scripts like Urdu, Arabic, etc. In this paper, we propose a discriminative learning approach for orientation detection of Urdu documents with varying layouts and fonts. The main advantage of our approach is that it can be applied to documents of other scripts easily and accurately. Our approach is based on classification of individual connected component orientation in the document image, and then the orientation of the page image is determined via majority count. A convolutional neural network is trained as discriminative learning model for the labeled Urdu books dataset with four target orientations: 0, 90, 180 and 270 degrees. We demonstrate the effectiveness of our method on dataset of Urdu documents categorized into the layouts of book, novel and poetry. We achieved 100% orientation detection accuracy on a test set of 328 document images.
Starting Page	1
Ending Page	5
File Size	416227
Page Count	5
File Format	PDF
ISBN	9781424448722
DOI	10.1109/INMIC.2009.5383110
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-14
Publisher Place	Pakistan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Optical character recognition software Shape Pattern recognition Image recognition Books Cellular neural networks Learning Artificial intelligence Text recognition
Content Type	Text
Resource Type	Article

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