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

Chapter 1
Introduction
tel-00912566, version 1 - 2 Dec 2013
1.1 Introduction
For centuries, paper documents such as handwritten manuscripts and
books have been used as the main source for preserving knowledge.
Then, with the advent of printing, printed books, magazines and newspapers
have replaced the traditional way of preserving information.
Nowadays, the most common way to store documents is by storing them in a
digital format. It is fast to search, cheap and portable. While many of the
ancient documents have been lost over the years, a substantial number has been
preserved. Not only these documents are fragile, they are also inaccessible for
the public. As a consequence, many libraries and organizations around the
world have decided to convert their collections to a digital format.
Two most common ways for digitizing documents are to use image scanners
or digital cameras. However, both methods produce images that demand a large
storage and they are unsearchable. Here, document image analysis comes into
play. Document image analysis is the subfield of digital image processing with
the goal of converting document images to searchable text form. The whole
process starts with segmenting a document image into different parts such as
text, graphics and drawings and forming a layout structure of the document.
Finally, having a layout structure, methods can determine the reading order of
the document or send text regions to an optical character recognition (OCR)
module which converts text regions into searchable format.
In this thesis, we develop a method for segmenting a document image into its
different parts. Currently, Safig, the leader of Demat-Factory, use a software to
do the same task, but with the supervision of human. The undisclosed software
that they use has many tunable parameters that need to be monitored carefully
for each document in order to produce correct segmentation result. Our goal
is to automate this process and generalize the method in a way that it can be
applied to a broad range of documents. There are many open-source and commercial
applications such as Tesseract-OCR, OCRopus, ABBYY FineReader
that perform the same task. However, as we will examine later, they are more
fitted for well formatted printed documents that are free of noise. Our goal is to
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