DocumentCode :
1990412
Title :
Arabic character recognition using particle swarm optimization with selected and weighted moment invariants
Author :
Sarfraz, Muhammad ; Al-Awami, Ali Taleb Ali
Author_Institution :
Dept. of Inf. & Comput. Sci., King Fahd Univ. of Pet. & Miner., Dhahran
fYear :
2007
fDate :
12-15 Feb. 2007
Firstpage :
1
Lastpage :
4
Abstract :
A new Arabic character recognition system has been proposed using moments as features. The proposed scheme works in such a way that the features are selected as well as weighted using a swarm-based optimization technique. For the sake of simplicity, it has been assumed that the Arabic text has already been preprocessed and segmented. Recognition results have been achieved up to 82% of accuracy. Authors believe that the 82% of accuracy is mainly due to not using very effective segmentation technique, otherwise the results could be above 95% as has been observed in the case of object recognition in an earlier paper of the authors.
Keywords :
character recognition; image segmentation; particle swarm optimisation; Arabic character recognition; Arabic text; particle swarm optimization; swarm-based optimization technique; weighted moment invariants; Character recognition; Computer science; Face detection; Feature extraction; Minerals; Object recognition; Particle swarm optimization; Petroleum; Shape; Speech recognition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal Processing and Its Applications, 2007. ISSPA 2007. 9th International Symposium on
Conference_Location :
Sharjah
Print_ISBN :
978-1-4244-0778-1
Electronic_ISBN :
978-1-4244-1779-8
Type :
conf
DOI :
10.1109/ISSPA.2007.4555582
Filename :
4555582
Link To Document :
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