Classification of document page images based on visual similarity of layout structures

Searching for documents by their type or genre is a natural way to enhance the eeectiveness of document retrieval. The layout of a document contains a signiicant amount of information that can be used to classify a document's type in the absence of domain speciic models. A document type or genre can be deened by the user based primarily on layout structure. Our classiication approach is based on \visual similarity" of the layout structure by building a supervised classiier, given examples of the class. We use image features, such as the percentages of text and non-text (graphics, image, table, and ruling) content regions, column structures, variations in the point sizes of fonts, the density of content area, and various statistics on features of connected components which can be derived from class samples without class knowledge. In order to obtain class labels for training samples, we conducted a user relevance test where subjects ranked UW-I document images with respect to the 12 representative images. We implemented our classiication scheme using the OC1, a decision tree classiier, and report our ndings.