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Tabular structures are used to present crucial information in a structuredand crisp manner. Detection of such regions is of great importance for properunderstanding of a document. Tabular structures can be of various layouts andtypes. Therefore, detection of these regions is a hard problem. Most of theexisting techniques detect tables from a document image by using priorknowledge of the structures of the tables. However, these methods are notapplicable for generalized tabular structures. In this work, we propose asimilarity measure to find similarities between pairs of rows in a tabularstructure. This similarity measure is utilized to identify a tabular region.Since the tabular regions are detected exploiting the similarities among allrows, the method is inherently independent of layouts of the tabular regionspresent in the training data. Moreover, the proposed similarity measure can beused to identify tabular regions without using large sets of parametersassociated with recent deep learning based methods. Thus, the proposed methodcan easily be used with resource constrained devices such as mobile deviceswithout much of an overhead.

Tabular Structure Detection from Document Images for Resource Constrained Devices Using A Row Based Similarity Measure