A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Two-dimensional directional wavelets and the scale-angle representation
Signal Processing
Pose estimation of SAR imagery using the two dimensional continuous wavelet transform
Pattern Recognition Letters
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
A new wavelet-based texture descriptor for image retrieval
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
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We present a novel preprocessing technique for handwritten numerals recognition, that relies on the extraction of multiscale features to characterize the classes. These features are obtained by means of different continuous wavelet transforms, which behave as scale-dependent bandpass filters, and give information on local orientation of the strokes. First a shape-preserving, smooth and smaller version of the digit is extracted. Second, a complementary feature vector is constructed, that captures certain properties of the digits, such as orientation, gradients and curvature at different scales. The accuracy with which the selected features describe the original digits is assessed with a neural network classifier of the multilayer perceptron (MLP) type. The proposed method gives satisfactory results, regarding the dimensionality reduction as well as the recognition rates on the testing sets of CENPARMI and MNIST databases; the recognition rate being 92.60 % for the CENPARMI data-base and 98.22 % for the MNIST database.