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

(a) Original image (b) Height map (c) Width map
tel-00912566, version 1 - 2 Dec 2013
Figure 4.3:
Height and width maps
4.3.1 Height and width maps
These two maps represent the local average height and width of connected components
in a document image. We do not use them directly in feature functions;
instead, we use them as part of the computation for a distance-based feature.
Each pixel of a width/height map is the weighted average of the height or width
of all text-labeled connected components in a 300 × 300 block around it.
Each map can be computed using the results of two image convolutions that
are divided pixel by pixel. Consider that our goal is to generate the height map.
We know the location of pixels for all text-labeled components of the page. We
first prepare two sparse images. The first image A has height of a component for
pixels of text components, and the rest of pixels are zero. The second image B
has a value of one for every pixel of text components, and the rest of the pixels
are zero. We also have a weighting function K of size 300×300 pixels defined as:
K(x, y) = cos( πi πj
) cos(
300 300 )
where x and y are the coordinates of pixels with (0, 0) being the center of
the window. Then
HeightMap(i,j) = (K ∗ A) i,j
(K ∗ B) i,j
The width map can be computed in the same way but instead of having
the height of connected components in the A image, we use widths. Figure 4.3
displays one document image and its height and width map. We also normalize
our height and width maps by dividing every pixel’s value to the height and
width of the document, respectively.
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