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- W1551115843 abstract "Dealing with hundreds of features in character recognition systems is not unusual. This large number of features leads to the increase of computational workload of recognition process. There have been many methods which try to remove unnecessary or redundant features and reduce feature dimensionality. Besides because of the characteristics of Farsi scripts, it’s not possible to apply other languages algorithms to Farsi directly. In this paper some methods for feature subset selection using genetic algorithms are applied on a Farsi optical character recognition (OCR) system. Experimental results show that application of genetic algorithms (GA) to feature subset selection in a Farsi OCR results in lower computational complexity and enhanced recognition rate. Keywords—Feature Subset Selection, Genetic Algorithms, Optical Character Recognition." @default.
- W1551115843 created "2016-06-24" @default.
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- W1551115843 date "2008-06-29" @default.
- W1551115843 modified "2023-09-23" @default.
- W1551115843 title "Application of Genetic Algorithms to Feature Subset Selection in a Farsi OCR" @default.
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- W1551115843 doi "https://doi.org/10.5281/zenodo.1060615" @default.
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