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

{ 1 if ys = text and y
f 1 (y s , y i,i∈N(s) ) =
← = non-text
0 otherwise
where y ← is the label for the site on the left of s. Of course in the training
phase, the weight associated with feature function f 1 has a correlation with the
number of times that a site s has a ”text” label and the site of its left has a
”non-text” label given all sites in the training dataset. If the training algorithm
finds that the number of times a site with a ”non-text” label appears at the left
side of a site with a ”text” label, is greater than all other label configurations
combined, then the associated weight for this feature obtains a positive value.
In any other case, the weight would be zero or negative.
tel-00912566, version 1 - 2 Dec 2013
Now we look at a more complex feature which produces real values. In the
previous chapter, all text and graphical elements were separated. We render
two separate images; one for each. Imagine that we pick the image (G) with all
graphic components which also includes rule lines and table lines. Every pixel
on this image has a value of 1 for every pixel of the graphical components, and
the rest of the pixels have a value of 0. In the new node feature function f 2 ,
if the current site has the ”non-text” label, the value of the feature function is
the average of the intensity values of the pixels of the graphical image covered
by the current site.
{
Gs if y
f 2 (y s , G) =
s = non-text
0 otherwise
where G s is the average value of pixels on image G covered by the site s.
The value of feature function f 2 is always positive for sites that are located on
graphical drawing and zero elsewhere. Thus, the λ 2 weight for this function is
guaranteed to have a positive value. Suppose that we change the non-text label
in f 2 to text, then the λ 2 is guaranteed to have a negative value after training.
Often good feature function engineering can significantly increase the labeling
accuracy of the model. We will describe our observations thoroughly later
in this chapter.
4.3 Observations
We described that feature functions in CRFs are functions that depend on both
the observations and labels at one or two sites. In other words, labels and observations
are tied to each other in a feature function. But, before we design our
feature functions, we need to explain where our observations come from. Given
a document image, we often perform operations such as filtering or run-length
analysis on the image. Then each site in our model has access to the results
from these operations and by knowing the location of all the pixels on the site, it
makes an average estimate of the pixel values from the result of each operation
and generates a vector of observations. Later, we will bind these observations
with appropriate labels to be used in our feature functions. In this section, we
describe all the operations that lead to our observations.
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