Text Classification and Layout Analysis of Paper Fragments∗

Document image analysis such as text classification and layout analysis allow for the automated extraction of document properties. In general these methodologies are pre-processing steps for Optical Character Recognition (OCR) systems. In contrast, the proposed method aims at clustering document snippets so that an automated clustering of documents can be performed. First, localized words are classified according to printed text, manuscript, and noise. The third class permits the correction of falsely segmented background elements. Having classified the text elements, a clustering is carried out which groups words into text lines and paragraphs. A back propagation of the class weights assigned to each word in the first step enables correcting wrong class labels. Finally, additional features such as the detection of underlined text or the paragraph layout (e.g. left aligned, centered) are extracted. The proposed method shows promising results on a dataset consisting of document fragments with varying shapes, content writing and layout.