Abstract book (pdf) - ICPR 2010

13:30-16:30,Paper TuBCT9.11
Writing Order Recovery from Off-Line Handwriting by Graph Traversal
Cordella, Luigi P., Univ. di Napoli Federico II
De Stefano, Claudio, Univ. di Napoli Federico II
Marcelli, Angelo, Univ. of Salerno
Santoro, Adolfo, Univ. of Salerno
We present a method to recover the dynamic writing order from static images of handwriting. The static handwriting is
initially represented by its skeleton, which is then converted into a graph, whose arcs correspond to the skeleton branches,
and nodes to either end point or branch point of the skeleton. Criteria derived by handwriting generation are then applied
to transform the graph in such a way that all its nodes, but the first and the last, have an even degree, so that it can be traversed
from the first to the last by using the Fleury’s algorithm. The experimental results show that combining criteria derived
from handwriting generation models with graph traversal leads to reconstruct the original sequence produced by a
writer even in case of complex handwriting, i.e handwriting with retracing, crossings and pen-up’s.
13:30-16:30,Paper TuBCT9.12
Holistic Urdu Handwritten Word Recognition using Support Vector Machine
Sagheer, Malik Waqas, CENPARMI, Concordia Univ.
He, Chun Lei, Concordia Univ.
Nobile, Nicola, Concordia Univ. CENPARMI
Suen, Ching Y.
Since the Urdu language has more isolated letters than Arabic and Farsi, a research on Urdu handwritten word is desired.
This is a novel approach to use the compound features and a Support Vector Machine (SVM) in offline Urdu word recognition.
Due to the cursive style in Urdu, a classification using a holistic approach is adapted efficiently. Compound feature
sets, which involves in structural and gradient features (directional features), are extracted on each Urdu word. Experiments
have been conducted on the CENPARMI Urdu Words Database, and a high recognition accuracy of 97.00% has been
achieved.
13:30-16:30,Paper TuBCT9.13
A Framework for the Combination of Different Arabic Handwritten Word Recognition Systems
El Abed, Haikal, Braunschweig Tech. Univ.
Märgner, Volker, Braunschweig Tech. Univ.
In this paper we present A Framework for the Combination of Different Arabic Handwritten Word Recognition Systems
to achieve a decision with a higher performance. This performance can be expressed by lower rejection rates and higher
recognition rates. The used methods range from voting schemes based on results of different recognizer to a neural network
decision based on normalized confidences. This work presents an extension of the well known combination methods for
a large lexicon, an extension from maximum 30 classes (e.g., 10 classes for digits classification) to 937 classes for the
IfN/ENIT-database. In addition, different reject rules based on the evaluation and analysis of individual and combined
systems output are discussed. Different threshold function for reject levels are tested and evaluated. Tests with a set of
recognizer, which participated in the ICDAR 2007 competition and based on set coming from the IfN/ENIT-database
show that a word error rate (WER) of 5.29% without reject and with a reject rate less than 25% even a word error rate of
less than 1%.
13:30-16:30,Paper TuBCT9.15
Degraded Character Recognition by Image Quality Evaluation
Liu, Chunmei, Tongji Univ.
The character image quality plays an important role in degraded character recognition which could tell the recognition
difficulty. This paper proposed a novel approach to degraded character recognition by three kinds of independent degradation
sources. It is composed of two stems: character image quality evaluation, character recognition. Firstly, it presents
the dual-evaluation to evaluate the image quality of the input character. Secondly, according to the input evaluation result,
the character recognition sub-systems adaptively act on. These sub-systems are trained by character sets whose image
qualities are similar to the input’s quality, and have special features and special classifiers respectively. Experiment results
demonstrate the proposed approach highly improved the performance of degraded character recognition system.
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