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

The next advantage of using CRFs is that they provide a foundation that
easily induces global, local and regional knowledge of the document in the process.
Because of the conditional nature of the system, any interaction between
the fields’ labels in a Markov network can be learned from available knowledge.
In this chapter we first introduce two-dimensional CRFs. Next, we describe
feature functions which are the most important building blocks of CRFs. Then
we note different types of observations that we extract from a document image.
These observation are used inside our feature functions. This chapter also
includes methods for decoding optimal label configuration for two-dimensional
CRFs and methods for training parameters of the model. Finally, we note the
results.
4.1 Conditional random fields (CRFs)
tel-00912566, version 1 - 2 Dec 2013
First appeared in the domain of natural language processing, conditional random
fields are proposed by Lafferty et al. [50] as a framework for building
probabilistic models to segment and label sequence data. Example of such sequence
data can be found in a wide variety of problems in text and speech
processing such as part-of-speech (POS) tagging.
Among probabilistic models that perform the same task, we can name Hidden
Markov Models (HMMs) [78] that are well understood and widely used
throughout the literature. HMMs identify the most likely label sequence for
any given observation sequence. They assign a joint probability p(x, y) to pairs
of observation (x) and label (y) sequences. To define a joint probability of this
type, models must enumerate all possible observation sequences which is intractable
for most domains, unless the observation elements are independent of
each other within the observation sequence. Although this assumption is appropriate
for simple toy examples, most practical observations are best represented
in terms of multiple features with long-range dependencies. CRFs address this
issue by using a conditional probability p(y|x) over label sequence given an
observation sequence, rather than a joint distribution over both label and observation
sequences.
CRFs first appeared in the form of chain-conditional random fields. In other
words, several fields are connected in a sequential format and the label of each
field depends on the label of the field on its left and on the whole observation
sequence. This model best fits for applications in signal and natural language
processing in which the data appear naturally in a row format. In our application,
we deal with images, which can be expressed naturally in two dimensions.
Thus, we are interested in two-dimensional conditional random fields.
To obtain our two-dimensional random fields, we first divide the document
image into rectangular blocks with equal heights and widths. We call each block
a site. Contrary to other CRFs that use sites with fix sizes for all document
images, we choose the height and width of sites to be identical to half of the
mean of heights and widths of all text characters on the document, respectively.
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