  Pool of knowledge available to the mankind depends on the source of learning
resources, which can vary from ancient printed documents to present electronic
material. The rapid conversion of material available in traditional libraries
to digital form needs a significant amount of work if we are to maintain the
format and the look of the electronic documents as same as their printed
counterparts. Most of the printed documents contain not only characters and its
formatting but also some associated non text objects such as tables, charts and
graphical objects. It is challenging to detect them and to concentrate on the
format preservation of the contents while reproducing them. To address this
issue, we propose an algorithm using local thresholds for word space and line
height to locate and extract all categories of tables from scanned document
images. From the experiments performed on 298 documents, we conclude that our
algorithm has an overall accuracy of about 75% in detecting tables from the
scanned document images. Since the algorithm does not completely depend on rule
lines, it can detect all categories of tables in a range of scanned documents
with different font types, styles and sizes to extract their formatting
features. Moreover, the algorithm can be applied to locate tables in multi
column layouts with small modification in layout analysis. Treating tables with
their existing formatting features will tremendously help the reproducing of
printed documents for reprinting and updating purposes.
