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

tel-00912566, version 1 - 2 Dec 2013
Figure 4.2: This figure shows our two-dimensional conditional random fields model.
Blue lines represent the boundaries between site. We divide the document image into
rectangular blocks with equal heights and equal widths. The label on each site depends
on the label of sites in its vicinity and observations defined for that site. Sites depend
on observations by means of feature functions. The ground truth label for each site is
also available for the purpose of training and evaluation. Note that the area that is
shown on the image is part of a document that shows text lines from the main text and
part of a side note. Width and height of sites in this image are not correct according
and are shown for visualization purposes only.
The reason is that documents come in different sizes and resolutions, and the
size of each site must be normalized and the width should be small enough to
pass between side notes and the main body. Each site may take one of the two
labels; ”text” or ”non-text”. However, the label of each site depends on the
labels of the sites in its vicinity; this includes the sites on its left, right, top
and bottom. Furthermore, the label of each site may depend on observations
from the document image. Generally, in conditional random fields, there is no
restriction on where these observations come from. However, we restrict the
observations to be the result from several filtering operations on the document
image under the current site. Figure 4.2
Let x s = {x 1 , ..., x N } be the vector of observations available to site s and y s
be one of the labels {text, non-text}. For each site s, the conditional probability
of having a label y s given observations x s is defined as:
p s (y s |x s ) ∝ exp
( F
e
)
∑
∑F n
λ k fk(y e i,i∈N(s) , y s , x i,i∈N(s) , x s ) + µ k fk n (y s , x s )
k=1
where f n and f e are the node and edge feature functions,respectively, which
are discussed later in section 4.2. F e is the total number of edge feature functions
and F n is the total number of node feature functions. N(s) is the set of
neighbors of the site s and is often called Markovian blanket of s. λ and µ are
k=1
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