One of the trivial steps in the development of a classifier is the design of its architecture. This paper presents a
new algorithm, Multi Models Evolvement (MME) using Particle Swarm Optimization (PSO). This algorithm is
a modified version of the basic PSO, which is used to the unsupervised design of Hidden Markov Model (HMM)
based architectures. For instance, the proposed algorithm is applied to an Arabic handwriting recognizer based
on discrete probability HMMs. After the optimization of their architectures, HMMs are trained with the Baum-
Welch algorithm. The validation of the system is based on the IfN/ENIT database. The performance of the
developed approach is compared to the participating systems at the 2005 competition organized on Arabic
handwriting recognition on the International Conference on Document Analysis and Recognition (ICDAR). The
final system is a combination between an optimized HMM with 6 other HMMs obtained by a simple variation
of the number of states. An absolute improvement of 6% of word recognition rate with about 81% is presented.
This improvement is achieved comparing to the basic system (ARAB-IfN). The proposed recognizer outperforms
also most of the known state-of-the-art systems.
In this paper, we present a comparison between two different combination schemes for the improvement of the
performance of Arabic handwriting recognition systems. Several recognition systems (here considered as black
box systems) are used from the participating systems of the Arabic handwriting recognition competition at
ICDAR 2007. The outputs of these systems provide the input of our combination schemes. The first combination
schemes are based on fixed fusion using logical rules, while the second one are based on trainable rules. After
the normalization step of the recognition confidences and the combination of the outputs, the improvement is
evaluated in term of recognition rates of a multi-classifier system with or without reject. The participating
systems use the sets a to e of the IfN/ENIT database for training, and we use the set f for tests. Applying the
combination rules, the results show a high recognition rate of about 95% without reject, which corresponds to
an improvement of recognition rates between 8% and 15% compared to results at the ICDAR 2007 competition.
Conference Committee Involvement (7)
12th International Conference on Frontiers in Handwriting Recognition
16 November 2010 |
The First International Workshop on Vehicular Communication Technologies (VehiCom 2009)
21 June 2009 |
The Fifth International Workshop on Computer Sciences Practice in Arabic (CSPA 2009)
10 May 2009 |
The Fifth International Conference Sciences of Electronic, Technologies of Information and Telecommunications (SETIT 2009)
22 March 2009 |
The Fifth International Conference on Innovations in Information Technology (Innovations 2008)
16 December 2008 |
The First International Conference on E-Medical Systems (E-MEDISYS 2008)
29 October 2008 |
The second International Conference on Machine Intelligence (ACIDCA-ICMI 2005)
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