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

2.3.2 Handwritten text line detection
Detecting text lines from handwritten documents has been one of the growing
trends in the past decade because of the high demand for digitization and
preservation of historical documents by most libraries around the world. In general,
detection of handwritten text lines has more challenges compare to printed
text lines, and it demands robust methods to deal with. We divide proposed
methods into three different categories namely; projection-based, texture-based
and distance based methods.
Projection based methods
tel-00912566, version 1 - 2 Dec 2013
Projection based methods divide into two categories; global and piecewise projections.
Global projection based methods like X-Y cut have been one of the
earliest methods that were applied to printed documents. However, they were
not effective for detecting high skewed text lines. Moreover, the presence of
dark borders in some scanned documents or moderate noise could also affect
the detection rate of the method. To address this problem for handwritten historical
documents, piecewise projection based methods are proposed in many
works including [75, 90, 95]. Using piecewise projection, a document is divided
into either blocks or non-overlapping vertical zones with equal width. Then the
vertical projection is computed in each zone separately.
An example of using global projection profiles can be found in [62]. Manmatha
and Rothfeder use projection profiles to detect handwritten text lines, specifically
for the George Washington manuscripts. The assumption is that these
particular manuscripts contain lines that are approximately straight and roughly
parallel to the horizontal axis. Therefore, global projection profiles are used.
Global projections may fit this dataset, but in order to detect skewed text lines,
most authors use piecewise projections. Figure 2.9 displays application of this
method on one document for detecting text lines.
[87] presents an algorithm using adaptive local connectivity map for retrieving
text lines from historical handwritten documents. The algorithm is
designed for solving particularly complex problems, including fluctuating text
lines, touching or crossing text lines, and low quality image that do not lend
themselves easily to binarization. First, it defines a local projection profile at
each pixel of the image and transforms the original image into an Adaptive local
connectivity map (ALCM), an acronym coined by the authors, that is a graylevel
image in which each pixel’s value represents a summation of foreground
pixel intensities. By thresholding the ALCM map, text lines reveal themselves
in the form of a binary image followed by a connected component analysis. Figure
2.10 shows the steps of this method to extract text lines.
In [105] Zahour et al. divide an image into non-overlapping vertical strips
and compute a projection profile for each strip to segment it into blocks based
on the peaks and valleys of the profiles. Just like the other method in [87], this
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