Fast Zernike wavelet moments for Farsi character recognition

Authors:
Ali Broumandnia;Jamshid Shanbehzadeh
Affiliations:
Islamic Azad University, Science and Research Branch, Tehran, 14515/755, Iran;Islamic Azad University, Science and Research Branch, Tehran, 14515/755, Iran and Department of Computer Engineering, Tarbiat Moalem University, Tehran, Iran
Venue:
Image and Vision Computing
Year:
2007

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Abstract

Farsi character recognition (FCR) systems perform recognition of Farsi documents. This paper presents a novel approach of fast Farsi character recognition based on fast zernike wavelet moments and artificial neural networks. Fast Zernike wavelet moments and artificial neural networks are employed in feature extraction and classification, respectively. A simulation result shows superiority of novel scheme over similar ones in terms of precision 4.37 times in average, and improves recognition speed by about 8.0 times in average.