Wavelet-Based Feature Extraction for Handwritten Numerals

  • Diego Romero
  • Ana Ruedin
  • Leticia Seijas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5716)


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.


Continuous Wavelet Transform Dimensionality Reduction Pattern Recognition Handwritten Numerals 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Diego Romero
    • 1
  • Ana Ruedin
    • 1
  • Leticia Seijas
    • 1
  1. 1.Departamento de Computación, Facultad de Ciencias Exactas y NaturalesUniversidad de Buenos AiresBuenos AiresArgentina

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