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

tel-00912566, version 1 - 2 Dec 2013
2.2.1 Distance-based methods
Run-length smearing (RLSA) [101] is perhaps the oldest known technique to
segment a page into homogeneous regions. It smears components using the perceived
text direction to form a distinct block of text. The Docstrum algorithm
[71] starts by finding the K-nearest neighbors of each connected component and
connects them by edges. Then the histogram of distances and angles are computed
for all edges. Text lines are found by grouping pairs of closest neighbors,
and the method proceeds to form text blocks by grouping text lines. Ferilli et
al., [32] indicate that white run lengths embody a distance-like feature similar
to the one explored in the Docstrum method, and borrow the idea to merge
closest neighbors based upon the values of horizontal and vertical white runs between
pairs of connected components. If adjacent regions from different columns
have a similar homogeneity, often they are merged. Because of this, other variants
of the run-length method have also been proposed to improve the results.
To name a few, Constrained Run-Length Algorithm (CRLA) [98] and selective
CRLA [93].
Clearly, methods based on Voronoi diagram like [47, 48] also follow the same
strategy to filter the edges of a graph and separate regions of text. Voronoi++
[2] is the latest proposed method that utilizes dynamic distance thresholding
instead of global thresholds that, to some extent, addresses the problem of over
segmentation around large characters and grouping of dissimilar text sizes. Despite
an increase of 33% in the detecting accuracy of regions compare to [48], the
text precision and recall of the method on Washington III (UW-III) database
are 68.78% and 78.30%, respectively.
The battle front in all these methods comes down to how well they can apply
distance thresholds to remove appropriate edges and group the remaining connected
components as a text region. In the mentioned works, different thresholds
have been set, not all of which are always suitable for other datasets. In other
words, these methods rely upon certain assumptions about document layouts,
and they fail when the underlying assumptions are not met. The other issue
with these methods is that because they rarely take advantage of the alignment
of components, they often merge side notes with the main text body. In fact,
we are not aware of a method that uses the alignment of components, shape of
regions or distance between components, all at the same time.
2.2.2 Whitespace analysis
Methods in this category try to analyze the background structure (whitespace)
of the document image to determine the physical document layout analysis. The
rudimentary algorithms for whitespace analysis were limited to axis-aligned rectangles.
As a consequence, they have to correct document rotation before the
search operation every time. In 2003, [13] Breuel developed a method that can
find maximal empty rectangles more efficiently, which would consequently be
applied to considerably more complex type of documents. The new method
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