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

2.2 Text region detection
Text region detection is the actual process of segmenting a page into separate
homogeneous text regions. In our view, text region detection is only one part of
the page segmentation process along side node separation, graphics separation
and other pre-processing and post-processing steps. However, in the literature
authors often use page segmentation, page decomposition or geometrical layout
analysis on behalf of region detection. Maybe one reason is that in some cases,
methods do not explicitly separate text and graphics before segmenting page
into text regions. In one case that we reviewed before [99], separation of text
and graphics comes even after detecting regions. So the point is that region
detection, page segmentation, page decomposition may all refer to the same
process with the same goal.
tel-00912566, version 1 - 2 Dec 2013
Many methods have been proposed for this goal. Back to the days when
Nagy wrote his famous review on document image analysis [65], he divided page
segmentation methods into two major top-down and bottom-up approaches categories.
Top-down analysis attempts to find larger components, like columns
and block of text before proceeding to find text lines, words and characters.
Bottom-up analysis forms words into text lines, lines into text blocks and so
on. Then within the last decade and due to advances in computation and optimization
techniques, more and more authors began to report a new emerged
category. Khurshid wrote [45]: ”Many other methods that do not fit into either
of these categories are therefore called hybrid methods.”.
It is part of our belief that this type of categorization does not represent the
majority of the available methods in the literature any more. It is more convenient
to divide current methods into three categories; distance-based, texturebased
and white space analysis. Distance-based methods try to analyze the
distance between connected components and to separate regions that fall apart.
Examples of these methods use Voronoi diagram [2, 47, 48], Delauney tessellation
[102] or run-length features [93, 32], map of foreground and background
runs .
Texture-based methods use wavelets analysis [49, 1], spline wavelets [27],
wavelet packets [30], Markov random fields (MRF) with pixel density features
[69], auto correlation [44] in a single or multi-resolution framework to segment
pages into text regions. Finally, methods that focus in the white background of
the document and try to find the maximal empty rectangles that can separate
columns of text [13, 85, 14].
In [84], Shafait et al, evaluate the performance of six famous algorithms,
including X-Y cut [66], run-length smearing [101], whitespace analysis [8], Docstrum
[71], and Voronoi-based [48] page segmentation. Also several methods for
historical document layout analysis are reported in [4], but unfortunately except
for their concise description in the paper, none of them is available publicly for
study and experimentation.
In the rest of this section, we briefly highlight some advantages and weaknesses
of each category, and then we provide an overview of our approach.
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