Abstract book (pdf) - ICPR 2010

features widely used in existing DSI solutions, this new feature is much more robust to variations in slant, font and style
of printed documents. Experimental results show that the method achieves promising identification performances.
13:30-16:30,Paper TuBCT9.48
Using Spatial Relations for Graphical Symbol Description
K. C., Santosh, INRIA – LORIA and INPL
Wendling, Laurent, Univ. Paris Descartes
Lamiroy, Bart, LORIA – INPL
In this paper, we address the use of unified spatial relations for symbol description. We present a topologically guided directional
relation signature. It references a unique point set instead of one entity in a pair, thus avoiding problems related
to erroneous choices of reference entities and preserves symmetry. We experimentally validate our method on showing its
ability to serve in a symbol retrieval application, based only on a spatial relational descriptor that represents the links between
the decomposed structural patterns called “vocabulary” in a spatial relational graph.
13:30-16:30,Paper TuBCT9.49
Automatic Discrimination between Confusing Classes with Writing Styles Verification in Arabic Handwritten Numeral
Recognition
He, Chun Lei, Concordia Univ.
Lam, Louisa, Concordia Univ.
Suen, Ching Y.
In handwriting recognition, confusing/conflicting writing styles can result in irreducible errors, so the study of writing
style consistencies is important for applications. In Arabic Handwritten Numeral Recognition, most errors occur between
samples of classes two and three due to their very similar shapes in some writing styles. In this paper, an automated writing
style detection process is effectively implemented in the pair-wise verification of samples in these two classes. As a result,
the recognition results have improved significantly with a reduction by 25% of previous errors. With rejection, when the
LDA (Linear Discriminant Analysis) measurement rejection threshold is adjusted to maintain the same error rate, the
recognition rate increases from 96.87% to 97.81%.
13:30-16:30,Paper TuBCT9.50
Random Subspace Method in Text Categorization
Gangeh, Mehrdad, Univ. of Waterloo
Kamel, Mohamed S, Univ. of Waterloo
Duin, Robert, TU Delft
In text categorization (TC), which is a supervised technique, a feature vector of terms or phrases is usually used to represent
the documents. Due to the huge number of terms in even a moderate-size text corpus, high dimensional feature space is
an intrinsic problem in TC. Random subspace method (RSM), a technique that divides the feature space to smaller ones
each submitted to a (base) classifier (BC) in an ensemble, can be an effective approach to reduce the dimensionality of the
feature space. Inspired by a similar research on functional magnetic resonance imaging (fMRI) of brain, here we address
the estimation of ensemble parameters, i.e., the ensemble size (L) and the dimensionality of feature subsets (M) by defining
three criteria: usability, coverage, and diversity of the ensemble. We will show that relatively medium M and small L yield
an ensemble that improves the performance of a single support vector machine, which is considered as the state-of-the-art
in TC.
13:30-16:30,Paper TuBCT9.51
Shape-DNA: Effective Character Restoration and Enhancement for Arabic Text Documents
Caner, Gulcin, Pol. Rain, Inc.
Haritaoglu, Ismail, Pol. Rain, Inc.
We present a novel learning-based image restoration and enhancement technique for improving character recognition performance
of OCR products for degraded documents or documents/text captured with mobile devices such as cameraphones.
The proposed technique is language independent and can simultaneously increase the effective resolution and
restore broken characters with artifacts due to image capturing device such as a low quality/low resolution camera, or due
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