Abstract book (pdf) - ICPR 2010

86.7M training samples which shows a 7-times speedup and higher minimum per-class recall, compared to previously reported
methods. The context of these experiments is the need for image classifiers able to handle an unbounded variety of
inputs: in our case, highly versatile document classifiers which require training sets as large as a billion training samples.
09:40-10:00, Paper WeAT6.3
Gaussian Mixture Models for Arabic Font Recognition
Slimane, Fouad, Univ. of Fribourg
Kanoun, Slim, ENIS
Alimi, Adel M., Univ. of Sfax
Ingold, Rolf, Univ. of Fribourg
Hennebert, Jean, Univ. of Applied Sciences
We present in this paper a new approach for Arabic font recognition. Our proposal is to use a fixed-length sliding window
for the feature extraction and to model feature distributions with Gaussian Mixture Models (GMMs). This approach presents
a double advantage. First, we do not need to perform a priori segmentation into characters, which is a difficult task
for arabic text. Second, we use versatile and powerful GMMs able to model finely distributions of features in large multidimensional
input spaces. We report on the evaluation of our system on the APTI (Arabic Printed Text Image) database
using 10 different fonts and 10 font sizes. Considering the variability of the different font shapes and the fact that our
system is independent of the font size, the obtained results are convincing and compare well with competing systems.
10:00-10:20, Paper WeAT6.4
Transfer of Supervision for Improved Address Standardization
Kothari, Govind, IBM
Faruquie, Tanveer, IBM Res. India
Subramaniam, L. Venkata, IBM Res. India
K, Hima Prasad, IBM Res. India
Mohania, Mukesh, IBM Res. India
Address Cleansing is very challenging, particularly for geographies with variability in writing addresses. Supervised learners
can be easily trained for different data sources. However, training requires labeling large corpora for each data source
which is time consuming and labor intensive to create. We propose a method to automatically transfer supervision from a
given labeled source to a target unlabeled source using a hierarchical dirichlet process. Each dirichlet process models data
from one source. The shared component distribution across these dirichlet processes captures the semantic relation between
data sources. A feature projection on the component distributions from multiple sources is used to transfer supervision.
10:20-10:40, Paper WeAT6.5
Bag of Characters and SOM Clustering for Script Recognition and Writer Identification
Marinai, Simone, Univ. of Florence
Miotti, Beatrice, Univ. of Florence
Soda, Giovanni, Univ. di Firenze
In this paper, we describe a general approach for script (and language) recognition from printed documents and for writer
identification in handwritten documents. The method is based on a bag of visual word strategy where the visual words
correspond to characters and the clustering is obtained by means of Self Organizing Maps (SOM). Unknown pages (words
in the case of script recognition) are classified comparing their vectorial representations with those of one training set
using a cosine similarity. The comparison is improved using a similarity score that is obtained taking into account the
SOM organization of cluster centroids. % Promising results are presented for both printed documents and handwritten
musical scores.
WeAT7 Dolmabahçe Hall C
Gait and Gesture Regular Session
Session chair: Shinoda, Koichi (Tokyo Institute of Technology)
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