The International Arab Journal of Information Technology (IAJIT)


Recognition of Handwritten Characters Based on

This paper is devoted to the off-line handwritten character recognition based on the two dimensional wavelet transform and a single support vector machine classifier. The wavelet transform provides a representation of the image in independent frequency bands. It performs a local analysis to characterize images of characters in time and scale space. The wavelet transform provides at each level of decomposition four sub-images: a smooth or approximation sub-image and three detail sub-images. In handwritten character recognition, the wavelet transform has received more attention and its performance is related not only to the use of the type of wavelet but also to the type of a sub-image used to provide features. Our objective here is thus to study these two previous points by conducting several tests using several wavelet families and several combinational features derived from sub-images. They show that the symlet wavelet of order 8 is the most efficient and the features derived from the approximation sub-image allow the best discrimination between the handwritten digits.
Training set Testing set R.Rate SVM rbf Our paper 60000 10000 98.76% SVM rbf Our paper 50000 10000 98.60% MLP

[2] 50000 10000 97.57% SVM

[17] 60000 10000 98.09% MLP

[18] 60000 10000 98.22% SVM

[20] 60000 10000 89.64% SVMs

[20] 60000 10000 99.32% 4. Conclusion In this paper, we have presented a technique of handwritten character recognition which combines a wavelet transform and a single support vector machine classifier. The wavelet transform allows us characterizing the character images by a set of features. The relevance of these features depends strongly on the choice of the type of the wavelet and sub-images derived from the wavelet transform. In this paper, several tests including several wavelets and smooth and details sub-images derived from the wavelets are conducted in order to determine the best wavelet and the best sub-image in the handwritten recognition framework. Experimental results on MNIST database reveal that sym8 wavelet outperforms other types of wavelets as those used in the previous works. They show also that features extracted from the smooth sub- image allowed achieving the best recognition rate. The proposed technique is efficient in comparison with other handwritten recognition methods published in the literature. As future work, we intend on one hand to integrate a normalization operation as preprocessing procedure in order to regulate the position and shape of character images, so as to reduce shape variation between the images of same class. On other hand, to investigate other features extracted from the four sub- images. References

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