We have shown how Gestalt properties can be used for identifying various components in a document image. The idea that our mind makes a holistic approach to vision rather than a disintegrated approach has been shown to be effective for document analysis also. Since the major constituent components (textual or non-textual) in a document page are arranged in a rectilinear fashion, we first make an isothetic decomposition of different components in a document page.
After representing the page as a feature set of its polygonal covers corresponding to the distinct regions of interest, each polygon is iteratively decomposed into the sub-polygons tightly enclosing the corresponding sub-components so as to capture the overall information as well as the necessary details to the desired level of precision. Subsequently, these components and sub-components are analyzed one by one using Gestalt laws/properties, which have been explained in detail in the context of this work.
Text regions, tabular structures, and various graphic objects readily admit some of the Gestalt properties, whence they are successfully recognized by our technique. We have tested our algorithm on several benchmark datasets, and some relevant results have been produced here to demonstrate the effectiveness and elegance of the proposed method.