Traditional media such as magazines and newspapers are undergoing deep transformations as they cope with the high volume and dynamicity of currently available information. In addition, with the emergence of decentralized publishing models, there is an increasing need for automated tools for authoring high-quality documents. Moreover, much of the dynamic information on the Web could also profit from such mechanisms for automatic presentation and summarization. This paper describes a solution to the problem of automatically producing a camera-ready magazine from a set of page templates and a sequence of variable content to be placed on those templates. The algorithm is able to find the optimal number of pages to hold the content, selecting the best templates to be used in the magazine in such a way that all pages are optimally used. The algorithm was integrated to Adobe's InDesign ® software, extending it to perform text fitting and rendering of magazine pages. The complete workflow is described in this paper, as well as an empirical evaluation and a discussion of future research directions.

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This work evaluates six state-of-the-art deep neural network (DNN) architectures applied to the problem of enhancing camera-captured document images. The results from each network were evaluated both qualitatively (i. e. with manual visual inspection) and quantitatively (i. e. using Image Quality Assessment metrics -IQA), and also compared with an existing approach based on traditional computer vision techniques. The best performing architectures generally produced good enhancement compared to the existing algorithm, showing that it is possible to use DNNs for document image enhancement. Furthermore, the best performing architectures could work as a baseline for future investigations on document enhancement using deep learning techniques. The main contributions of this paper are: a baseline of deep learning techniques that can be further improved to provide better results, and a evaluation methodology using IQA metrics for quantitatively comparing the produced images from the neural networks to a ground truth. CCS CONCEPTS• Computing methodologies → Reconstruction; Image processing; Neural networks.

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This paper presents an improved approach for automatically laying out content onto a document page, where the number and size of the items are unknown in advance. Our solution leverages earlier results from Oliveira (2008) wherein layouts are modeled by a guillotine partitioning of the page. The benefit of such method is its efficiency and ability to place as many items on a page as desired. In our model, items have flexible representations and texts may freely change their font sizes to fit a particular area of the page. As a consequence, the optimization goal is to find a layout that produces the least noticeable difference between font sizes, in order to obtain the most aesthetically pleasing layout. Finding the best areas for text requires knowledge of how typesetting engines actually render text for a particular setting. As such, we also model the behavior of the T E X typesetting engine when computing the height to be occupied by a text block as a function of the font size, text length and line width. An analytical approximation for text placement is then presented, refined by using curve fitting over T E Xgenerated data. As a practical result, the resulting layouts for a newspaper generation application are also presented. Finally, we discuss these results and directions for further research.