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

Figure 2.8: These images from [18] illustrate how the snakes grow to engulf a text
line.
tel-00912566, version 1 - 2 Dec 2013
In the review section for text region detection, we already reviewed distancebased
methods that try to segment document image into text regions. Some
methods use the same strategy to detect text lines. In one method [102] Delaunay
triangulation is used to find a set of edges between connected components.
Then the algorithm looks for the principal direction among the shortest edges
to estimate the direction of the text lines. This method can be applied to pages
containing slanted text lines successfully. Nevertheless, because the strategy for
filtering edges based on their lengths is somehow primitive and the method uses
a global threshold to remove edges, it becomes ineffective for detecting skewed
text lines or separating side notes.
To detect skewed text lines that exist on camera-captured documents, Bukhari
et al. [17, 18] propose a method based on active contour models. Active contour
model is a framework for finding the outline of an object from a possibly noisy
image [24]. The method starts with multiple initial closed contours (snakes)
that engulf text blobs. Each snake is associated with an energy that is usually
the sum of the internal and external forces, computed from the gradient vector
flow (GVF) at the boundaries of the contours. Then in each iteration, snakes
grow by a fixed number of pixels to minimize the associated energies. All overlapping
pairs of snakes are merged together to form a single snake that covers
a curled text line. Results indicate that this method has the potential to detect
highly curled text lines with different spacing and font sizes. The analysis of
the errors shows that most of them are due to over-segmentation of a single line
in the ground-truth into multiple detected text lines. Figure 2.8 illustrates how
snakes grow on characters to engulf the skewed text line.
Another method [19] is also proposed for detecting text lines in cameracaptured
documents. One advantage of this new method is its ability to locate
curled text lines. The approach is using an oriented anisotropic Gaussian
smoothing function to generate a filter bank. For each pixel, the maximum
value among all scales and orientations is selected for the final smoothed image.
Then Horn-Riley based ridge detection algorithm [41, 80] is used to enhance
text line structures from the smoothed image. The result from this work is
mentioned in CBDAR2007 document image de-warping contest [83]. Although
the method performs well on documents with printed text, it would be hard to
be adapted for handwritten documents where alignment of the strings and nonuniform
gaps between words are an issue and the results many contain many
text line fragments.
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