Novel Framework for Selecting the Optimal Feature Vector from Large Feature Spaces
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Online handwriting recognition for the Arabic letter set
CIT'11 Proceedings of the 5th WSEAS international conference on Communications and information technology
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Arabic character recognition algorithm using Modified Fourier Spectrum (MFS) is presented. The MFS descriptors are estimated by applying the Fast Fourier Transform (FFT) to the Arabic character primary part contour. Ten descriptors are estimated from the Fourier spectrum of the character primary part contour by subtracting the imaginary part from the real part (and not from the amplitude of the Fourier spectrum as is usually the case). These descriptors are then used in the training and testing of Arabic characters. The computation of the MFS descriptors requires less computation time than the computation of the Fourier descriptors. Experimental results have shown that the MFS features are suitable for Arabic character recognition. Average recognition rate of 95.9% was achieved for the model classes. The analysis of the errors indicates that this recognition rate can be improved by using the "hole" feature of a character and use cleaning corrupted data.