Classification of Document Page Images

Searching in a large heterogeneous collection of scanned document images often produces uncertain results in part because of the size of the collection and the lack of an ability to focus queries appropriately. Searching for documents by their type is a natural way to enhance the effectiveness of document retrieval in the workplace [2], and a such system is proposed in [4]. The goal of our work is to build classifiers that can determine the type or genre of a document image. We use primarily layout features since the layout of a document contains a significant amount of information that can be used to identify a document’s type. Layout analysis is necessary since our input image has no structural definition that is immediately perceivable by a computer. Classification is thus based on “visual similarity” of the structure without reference to models of particular kinds of pages. There has been some classification work reported but most require either domain specific models [3, 5, 6, 8] or are based on text obtained by optical character recognition (OCR) [3, 6, 8].