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I am currently writing my bachelors thesis about on-line handwriting recognition.

In this context, there are different methods to make the input data more useful to classifiers / to make learning more effective:

  • Scaling the drawn symbol
  • Shifting the drawn symbol
  • Rotating
  • Re-sampling
  • Wild point detection

I think all of those techniques are preprocessing, because I apply them before the image is "processed" by the training / recognition algorithm.

But aren't all of those techniques also normalization techniques? Where is the difference between "preprocessing" and "normalization"?

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    $\begingroup$ I'd say that normalization is one form of pre-processing. Noise removal would be another form of pre-processing. $\endgroup$ – Matt L. Jun 27 '14 at 16:23
  • $\begingroup$ So re-sampling and wild-point detection is noise removal and scaling / shifting / rotating is normalization? Sounds reasonable. Is there an official source for such terms? $\endgroup$ – Martin Thoma Jun 27 '14 at 16:53
  • $\begingroup$ Wild-point detection is certainly some type of noise removal, but why would you consider re-sampling as noise removal? I don't know of any source for these terms, but I think they can be used freely if you make sure you define what you mean by them. There is no standardization of these very general terms, at least as far as I know. $\endgroup$ – Matt L. Jun 27 '14 at 17:05
  • $\begingroup$ @MattL.: Thank you! Of course I will define the terms. I think re-sampling is probably both, normalization and noise removal. It is noise-removal, because some devices have a much higher time-sampling rate than what makes sense. Having that much data (which does not contain much more additional information) makes the recognizer perform worse. $\endgroup$ – Martin Thoma Jun 27 '14 at 17:10
  • $\begingroup$ @MattL. Do you want to post your comment as an answer? $\endgroup$ – Martin Thoma Jun 30 '14 at 16:28

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