  Tabular structures are used to present crucial information in a structured
and crisp manner. Detection of such regions is of great importance for proper
understanding of a document. Tabular structures can be of various layouts and
types. Therefore, detection of these regions is a hard problem. Most of the
existing techniques detect tables from a document image by using prior
knowledge of the structures of the tables. However, these methods are not
applicable for generalized tabular structures. In this work, we propose a
similarity measure to find similarities between pairs of rows in a tabular
structure. This similarity measure is utilized to identify a tabular region.
Since the tabular regions are detected exploiting the similarities among all
rows, the method is inherently independent of layouts of the tabular regions
present in the training data. Moreover, the proposed similarity measure can be
used to identify tabular regions without using large sets of parameters
associated with recent deep learning based methods. Thus, the proposed method
can easily be used with resource constrained devices such as mobile devices
without much of an overhead.
