Neural Networks for Handwriting Recognition - Computational Intelligence Paradigms in Advanced Pattern Classification - page 11

hat-feature : this binary feature indicates whether a delayed stroke has been re-
moved at the same horizontal position as the considered point.

•

• speed : the velocity is computed before resampling and then interpolated.

• x-coordinate : the x -position is taken after high-pass filtering, i.e., after subtract-

ing a moving average from the real horizontal position.

• y-coordinate : this feature represents the vertical position of the point after nor-

malization.

• writing direction : here we have a pair of features, given by the cosine and sine

of the angle between the line segment starting at the point and the x -axis.

• curvature : similarly to the writing direction, this is a pair of features, given by

the cosine and sine of the angle between the lines to the previous and the next
point.

• vicinity aspect : this feature is equal to the aspect of the trajectory (See Fig. 2)

• vicinity slope : this pair of features is given by the cosine and sine of the angle

of the straight line from the first to the last vicinity point (see Fig. 2).

• vicinity curliness : this feature is defined as the length of the trajectory in the vi-

cinity divided by max( x ( t ); y ( t )) (see Fig. 2).

vicinity linearity : here we use the average squared distance d² of each point in
the vicinity to the straight line from the first to the last vicinity point (see Fig. 2).

•

Fig. 3 Pseudo offline features

The features of the second class are all computed using a two-dimensional matrix
B representing the offline version of the data. For each position the number of
points on the trajectory of the strokes is stored. This can be seen as a low-
resolution image of the handwritten data. The following features are used:

• ascenders/descenders : these two features count the number of points above the

corpus line (ascenders) and below the baseline (descenders). Only points which