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Preprocessing for hand writing data

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  • Preprocessing for hand writing data

    Dear all,

    I am collaborating on a project involving analysis of hand writing data. Some of the data are acquired using a Tablet PC (Thinkpad X61t), and – of course - pixelated. I wonder what is the best method to preprocess these data in order to minimize artifacts due to pixelation and unsystematic error/noise and would appreciate your support in choosing a good approach.

    Some of the methods that have been used are
    - Gaussian smoothing
    - low pass filter (Butterworth vs. FIR?)
    - Savitzky-Golay smoothing
    - non-parametric regression methods

    The last two seem to be particularly attractive as they allow to compute not only smoothened data but also higher derivatives. I think I understand the Savitzky-Golay approach (local polynomial fit), but don’t understand non-parametric regression methods, as discussed for instance in Marquardt & Mai (1994), A computational procedure for movement analysis in handwriting.

    I’d be grateful for recommendations and general information (if possible with references), as well as specific information concerning choice of parameters (cut-off frequency, window size) and implementation of the non-parametric approach.

    Best regards,

    Julius Verrel

    Center for Lifespan Psychology
    MPI for Human Development, Berlin, Germany
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