Paper Info
Title
Improving A Hmm-Based Off-Line Handwriting Recognition System Using Mme-Pso Optimization
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.
Year
DOI
Venue
2011
10.1117/12.876585
DOCUMENT RECOGNITION AND RETRIEVAL XVIII
Keywords
Field
DocType
Particle Swarm Optimization, Hidden Markov Models, Arabic Handwriting Recognition
Particle swarm optimization,Document analysis,Off line,Pattern recognition,Computer science,Arabic handwriting recognition,Word recognition,Handwriting recognition,Speech recognition,Artificial intelligence,Classifier (linguistics),Hidden Markov model
Conference
Volume
ISSN
Citations 
7874
0277-786X
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Mahdi Hamdani1594.79
Haikal El-Abed243629.39
Tarek M. Hamdani314316.16
Volker Märgner429529.02
Mohamed Adel Alimi51947217.16