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

of text lines using a criterion that depends on the geometric appearances
and indentations of text lines in a paragraph.
1.5 Our datasets
One of the datasets used for testing and training in this work is a selection of
document images from a huge corpus, that were provided for the purpose of
this project. The original corpus contains historical handwritten manuscripts
and old printed forms, documents and advertisements, mostly written in French
language. We have chosen a subset of 100 documents from this corpus that
represents the original set. Figure 1.5 shows some samples of our own dataset.
tel-00912566, version 1 - 2 Dec 2013
In addition, we gathered 61 documents used as part of the dataset in IC-
DAR2009 [6] page segmentation competition and 100 documents from the dataset
used in ICDAR2011 [4] historical document layout analysis competition, to be
able to compare our results with the state-of-the-art methods. Some samples
from these datasets are shown in figure 1.6.
For each document a ground truth file is available. The ground truth file
contains the true structure of text regions and text lines for each document. By
far ground truth data are the most important part of the work for the purpose
of both training and testing various parts of the system. They are usually in
text format and contain coordinates of objects in a hierarchical structure. For
text and graphics separation we only need the original document image and the
location of true text regions. Every component that does not belong to a text
region is considered a non-text element. A collection of textual and non-textual
components are used for the purpose of training. In text region detection, we
divide every document into sites with predefined heights and widths. By knowing
whether a site is located on the text or non-text region of the ground truth,
we can easily generate our true labels for the purpose of training our region
detector. For text line and paragraph detection we consider the geometry appearance
and relative position of text lines defined in the ground truth structure.
In the first stages of the project, we had no ground truth date for any of
the datasets. So we developed a software to generate our own true data. Figure
1.7 shows a screen shot of our developed software. After opening an image,
the software renders a scene of all connected components extracted from the
image. The user can group several connected components as a text line. With
the same strategy the user can group several text lines as a paragraph. In case a
large connected component (a table or a frame of advertisement) occults other
smaller components, the user has the option to deactivate that particular component.
Finally, the application generates an XML file for each document with
the correct structure to be used as a ground truth. Figure 1.8 shows part of an
XML file created by our software.
Later, we got access to ground truth data for ICDAR2009 dataset. The
new ground truth data are also provided in XML format but the nodes and
elements of the new XML documents are different from ours. Because the
dataset and ground truth data belong to Prima group at University of Salford,
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