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

proximation of a Mumford-Shah functional. They indicate that boundary based
level-set methods such as [52, 53] depend on the number of boundary evolution
steps, and they are also sensitive to touching text lines. To handle these difficulties,
their method seeks to minimize a Mumford-Shah functional using a
piecewise constant approximation [92]. The initial estimates of the text lines
are the same as [85], and then they are refined by visiting each pixel of the
image in a given order. For each initial text line, a segmentation curve is set
to segment the text line into two regions; inner and outer. In each iteration,
these curves evolve by calculating their parameters based on the intensity of the
region. The final results may contain line fragments due to large gaps between
words; so morphological operators are used as part of the post-processing step
to merge some of these fragments.
tel-00912566, version 1 - 2 Dec 2013
The last reviewed method is published in paper [88] for detecting handwritten
Arabic text lines. Instead of summing values of adjacent pixels as in
projection profiles in [87], Shi et al. apply steerable directional filters, each with
a shape of an ellipse with a large focal distance. The height of the filter is chosen
to be the same as the height of an average text and the width to be five times
its height. Using a filter with a direction similar to the direction of text lines,
the pixel value for that location has a greater response than when using another
filter in any other direction. The result of filtering generates a map that is later
thresholded adaptively to enhance the location of text lines.
Distance based methods
We only review one method in this category. This method has been recently
proposed in [103] for segmentation of handwritten Chinese text regions into
text lines. The heart of this method is a minimum spanning tree algorithm.
In the first stage, the method extracts all the connected components of the
document. The reasonable assumption is that components which belong to a
single text line are close to one another compared to the components that belong
to different text lines. Therefore, a minimum spanning tree is applied to
connect neighboring components of the same line, and each line corresponds to
a sub-tree. Then because of variability of layout of text lines and occasionally
large gaps between words, the results are not prefect. Hence, the method use a
second-stage clustering procedure to dynamically cut the edges of the tree into
groups corresponding to correct text lines. A detection accuracy rate of 98.02%
is reported for 803 unconstrained documents.
2.3.3 Conclusion
We have noted many methods in this section for text line detection, and eventually
we have to decide which method is exploitable for detecting text lines
from our corpus. An evaluation and comparison between methods is valid only
if the results are available for the same dataset and with the same evaluation
metric. We identified three different performance evaluation metrics; Pixel Correspondence,
Match Counting and Overall Pixel-Level HitRate. We have already
described Match Counting in the first chapter. Unfortunately, in our case, it
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