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Proceedings Paper

Improving a HMM-based off-line handwriting recognition system using MME-PSO optimization
Author(s): Mahdi Hamdani; Haikal El Abed; Tarek M. Hamdani; Volker Märgner; Adel M. Alimi
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Paper Abstract

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.

Paper Details

Date Published: 24 January 2011
PDF: 9 pages
Proc. SPIE 7874, Document Recognition and Retrieval XVIII, 787408 (24 January 2011); doi: 10.1117/12.876585
Show Author Affiliations
Mahdi Hamdani, REGIM, Univ. of Sfax, ENIS (Tunisia)
Haikal El Abed, Technische Univ. Braunschweig (Germany)
Tarek M. Hamdani, REGIM, Univ. of Sfax, ENIS (Tunisia)
Volker Märgner, Technische Univ. Braunschweig (Germany)
Adel M. Alimi, REGIM, Univ. of Sfax, ENIS (Tunisia)


Published in SPIE Proceedings Vol. 7874:
Document Recognition and Retrieval XVIII
Gady Agam; Christian Viard-Gaudin, Editor(s)

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