Abstract book (pdf) - ICPR 2010

13:30-16:30,Paper TuBCT9.16
Offline Arabic Handwriting Identification using Language Diacritics
Lutf, Mohammed, Huazhong Univ. of Science and Tech.
You, Xinge, Huazhong Univ. of Science and Tech.
Li, Hong, Huazhong Univ. of Science and Tech.
In this paper, we present an approach for writer identification using off-line Arabic handwriting. The proposed method introduced
Arabic writing in a new form, by presenting Arabic writing in its basic components instead of alphabetic. We
split the input document into two parts: one for the letters and the other for the diacritics, we extract all diacritics from the
input image and calculate the LBP histogram for each diacritic then concatenate these histograms to use it as handwriting
features. We use the IFN/ENIT database in the experiments reported here and our tests involve 287 writers. The results
show that our method is very effective and makes the handling of the Arabic handwriting more easily than before.
13:30-16:30,Paper TuBCT9.17
Removing Rule-Lines from Binary Handwritten Arabic Document Images using Directional Local Profile
Shi, Zhixin, SUNY at Buffalo
Setlur, Srirangaraj, Univ. at Buffalo
Govindaraju, Venu, Univ. at Buffalo
In this paper, we present a novel approach for detecting and removing pre-printed rule-lines from binary handwritten
Arabic document images. The proposed technique is based on a directional local profiling approach for the detection of
the rule-line locations. Then a refined adaptive vertical run-length search is designed for removing the rule-line pixels
without much damaging to the text. They are also tolerate to the variations in the rule-lines such as broken lines, orientation
changes and variation in the thickness of the rule-lines. Analysis of experimental results on the DARPA MADCAT Arabic
handwritten document data indicates that the method is robust and is capable of correctly removing rule-lines.
13:30-16:30,Paper TuBCT9.18
A Bag-of-Pages Approach to Unordered Multi-Page Document Classification
Gordo, Albert, Univ. Autònoma de Barcelona
Perronnin, Florent, Xerox Res. Centre Europe
We consider the problem of classifying documents containing multiple unordered pages. For this purpose, we propose a
novel bag-of-pages document representation. To represent a document, one assigns every page to a prototype in a codebook
of pages. This leads to a histogram representation which can then be fed to any discriminative classifier. We also consider
several refinements over this initial approach. We show on two challenging datasets that the proposed approach significantly
outperforms a baseline system.
13:30-16:30,Paper TuBCT9.19
Fast Seamless Skew and Orientation Detection in Document Images
Konya, Iuliu Vasile, Fraunhofer IAIS
Eickeler, Stefan, Fraunhofer IAIS
Seibert, Christoph, Fraunhofer IAIS
Reliable and generic methods for skew detection are a necessity for any large-scale digitization projects. As one of the
first processing steps, skew detection and correction has a heavy influence on all further document analysis modules, such
as geometric and logical layout analysis. This paper introduces a generic, scale-independent algorithm capable of accurately
detecting the global skew angle of document images within the range [-90,90] degrees. By using the same framework, the
algorithm is then extended for Roman script documents so as to cope with the full range [-180,180) degrees of possible
skew angles. Despite its generality, the improved algorithm is very fast and requires no explicit parameters. Experiments
on a combined test set comprising around 110000 real-life images show the accuracy and robustness of the proposed
method.
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