Abstract book (pdf) - ICPR 2010

13:30-16:30, Paper TuBCT9.3
Prototype-Based Methodology for the Statistical Analysis of Local Features in Stereotypical Handwriting Tasks
O’Reilly, Christian, École Pol. De Montreal
Plamondon, Réjean, École Pol. De Montréal
A three steps methodology is proposed to derive consistent sets of local features which may be easily compared between
the different samples of a stereotypical human handwriting movement, allowing the statistical analysis its local variability.
This technique is illustrated using the Sigma-Lognormal modeling of on-line triangular trajectory patterns obtained from
a standardized neuromuscular task. The overall approach can be adapted and generalized to the analysis of the end-effector
kinematics of many planar upper limb movements.
13:30-16:30, Paper TuBCT9.4
The Snippet Statistics of Font Recognition
Lidke, Jakub, Fraunhofer IAIS
Thurau, Christian, Fraunhofer IAIS
Bauckhage, Christian, Fraunhofer IAIS
This paper considers the topic of automatic font recognition. The task is to recognize a specific font from a text snippet.
Unlike previous contributions, we evaluate, how the frequencies of certain letters or words influence automatic recognition
systems. The evaluation provides estimates on the general feasibility of font recognition under various changing conditions.
Results on a data-set containing 747 different fonts shows that precision can vary between 16% and 94%, dependent on
(I) which letters are provided, (ii) how many letters are provided, and (iii) which language is used – as these factors considerably
influence the text snippet statistics. As a second contribution, we introduce a novel bag-of-features based approach
to font recognition.
13:30-16:30, Paper TuBCT9.5
A Study of Designing Compact Recognizers of Handwritten Chinese Characters using Multiple-Prototype based
Classifiers
Wang, Yongqiang, The Univ. of Hong Kong
Huo, Qiang, Microsoft Res. Asia
We present a study of designing compact recognizers of handwritten Chinese characters using multiple-prototype based
classifiers. A modified Quick prop algorithm is proposed to optimize a sample-separation-margin based minimum classification
error objective function. Split vector quantization technique is used to compress classifier parameters. Benchmark
results are reported for classifiers with different footprints trained from about 10 million samples on a recognition task
with a vocabulary of 9282 character classes which include 9119 Chinese characters, 62 alphanumeric characters, 101
punctuation marks and symbols.
13:30-16:30, Paper TuBCT9.6
Membership Functions for Zoning-Based Recognition of Handwritten Digits
Impedovo, Sebastiano, Univ. degli Studi di Bari
Impedovo, Donato, Pol. Di Bari
Pirlo, Giuseppe, Univ. degli Studi di Bari
Modugno, Raffaele, Univ. of Bari “Aldo Moro”
This paper focuses the role of membership functions in zoning based classification. In fact, the effectiveness of a zoning
methods depends not only on the way in which the pattern image is partitioned by the zoning, but also on the criteria
adopted to define the way in which a feature influences the diverse zones. For this purpose, an experimental investigation
is presented, that focuses the most valuable way in which a features spreads its influence on the zones of the pattern image.
The experimental tests have been carried out in the field of handwritten digit recognition, using the numeral digits of the
CEDAR database. The result points out the membership function has a paramount relevance on the classification performance
and demonstrate that the exponential model outperforms other membership functions.
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