Segmentation of heterogeneous document images : an ... - Tel

determine the average height of text lines from text connected components, but
much harder to determine the spaces between lines. Furthermore, line-spacing
varies from one document to the other. To ensure that our kernel only captures
one text line, the value of σ should be tied to λ. The size of the kernel should
depend on the σ parameter. The size should normally be selected large enough
so that kernel coefficients of the border rows and columns contribute very little
to the sum of coefficients. As a rule of thumb the size (in pixels) of the kernel
window should be six times the value of σ or larger to ensure that the power
of coefficient values at the borders of the kernel window subside to 1% or lower
than the power of center coefficients. The second issue is that we want the λ
parameter to be dependent on the size of the text line. Our parameters are:
tel-00912566, version 1 - 2 Dec 2013
λ = 2 × Avg. text height or width
σ = λ
3.5
γ = 0.7
{ π To capture text lines
ψ =
0 To capture white space
Unfortunately, there is no efficient implementation of Gabor filter in which
the λ parameter varies locally. We could manually crop patches from a document
image and use a Gabor kernel for each patch that matches text heights locally,
but it would take a huge amount of time. Therefore, Gabor filtering using kernel
multiplication in frequency space and fixed parameters for each kernel per document
is still the only available option. As a consequence two different Gabor
kernels are used to capture text lines. For these kernels, the λ parameters are
set to 2 × Average Text Height and 4 × Average Text Height. Moreover, to capture
white space gaps, three Gabor kernels are used. The λ parameters are set
to 1.5 × Text Width, 3.5 × Average Text Width and 5.5 × Average Text Width.
Figure 4.6 shows the results of applying the mentioned Gabor filters to a document
in figure 4.5.
Figure 4.7 displays two additional examples of filtered images, highlighting
the effect of using various λ parameters to capture text lines of different font
sizes.
4.4 Feature functions
Each feature function has two parts. The first part depends on the label of the
site or a combination of the labels from its neighbors. The second part depends
on the observations. Theoretically, these observations can be generated from
anywhere on the image; however, we restrict them to be computed from and
around the site in question.
We described many features that we extract from document images. In order
to generate observations from these features, we compute mean and variance for
each feature map at the same site. These statistics serve as observations at each
62