Recognition of Handwritten Characters Based on
Wavelet Transform and SVM Classifier
Malika Ait Aider1,
Kamal Hammouche1, and Djamel Gaceb2
1Laboratoire
Vision Artificielle et Automatique des Systèmes, Université Mouloud Mammeri,
Algérie
2Laboratoire D'informatique
en Image et Systèmes D'information, Institut
National des Sciences Appliquées de Lyon, France
Abstract: 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.
Keywords: Feature extraction; wavelet transform,
handwritten character recognition; support vector machine; OCR.