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Page layout analysis is a fundamental step of any document image understanding system. We introduce an approach that segments text appearing in page margins (a.k.a side-notes text) from manuscripts with complex layout format. Simple and discriminative features are extracted in a connected-component level and subsequently robust feature vectors are generated. Multilayer perception classifier is exploited to classify connected components to the relevant class of text. A voting scheme is then applied to refine the resulting segmentation and produce the final classification. In contrast to state-of-the-art segmentation approaches, this method is independent of block segmentation, as well as pixel level analysis. The proposed method has been trained and tested on a dataset that contains a variety of complex side-notes layout formats, achieving a segmentation accuracy of about 95%.

We present a method to segment historical document images into regions of different content. First, we segment text elements from non-text elements using a binarized version of the document. Then, we refine the segmentation of the non-text regions into drawings, background and noise. At this stage, spatial and color features are exploited to guarantee coherent regions in the final segmentation. Experiments show that the suggested approach achieves better segmentation quality with respect to other methods. We examine the segmentation quality on 252 pages of a historical manuscript, for which the suggested method achieves about 92% and 90% segmentation accuracy of drawings and text elements, respectively.

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Complex document layouts pose prominent challenges for document image understanding algorithms. These layouts impose irregularities on the location of text paragraphs which consequently induces difficulties in reading the text. In this paper we present a robust framework for analyzing historical manuscripts with complex layouts. This framework aims to provide a convenient reading experience for historians through topnotch algorithms for text localization, classification and dewarping. We segment text into spatially coherent regions and text-lines using texture-based filters and refine this segmentation by exploiting Markov Random Fields (MRFs). A principled technique is presented for dewarping curvy text regions using a non-linear geometric transformation. The framework has been validated using a subset of a publicly available dataset of historical documents and it provided promising results.