DeepOtsu: Document Enhancement and Binarization using Iterative Deep Learning

This paper presents a novel iterative deep learning framework and apply it for document enhancement and binarization. Unlike the traditional methods which predict the binary label of each pixel on the input image, we train the neural network to learn the degradations in document images and produce the uniform images of the degraded input images, which allows the network to refine the output iteratively. Two different iterative methods have been studied in this paper: recurrent refinement (RR) which uses the same trained neural network in each iteration for document enhancement and stacked refinement (SR) which uses a stack of different neural networks for iterative output refinement. Given the learned uniform and enhanced image, the binarization map can be easy to obtain by a global or local threshold. The experimental results on several public benchmark data sets show that our proposed methods provide a new clean version of the degraded image which is suitable for visualization and promising results of binarization using the global Otsu's threshold based on the enhanced images learned iteratively by the neural network.Comment: Accepted by Pattern Recognitio

Computer-Aided Palaeography, Present and Future

The field of digital palaeography has received increasing attention in recent years, partly because palaeographers often seem subjective in their views and do not or cannot articulate their reasoning, thereby creating a field of authorities whose opinions are closed to debate. One response to this is to make palaeographical arguments more quantitative, although this approach is by no means accepted by the wider humanities community, with some arguing that handwriting is inherently unquantifiable. This paper therefore asks how palaeographical method might be made more objective and therefore more widely accepted by non-palaeographers while still answering critics within the field. Previous suggestions for objective methods before computing are considered first, and some of their shortcomings are discussed. Similar discussion in forensic document analysis is then introduced and is found relevant to palaeography, though with some reservations. New techniques of "digital" palaeography are then introduced; these have proven successful in forensic analysis and are becoming increasingly accepted there, but they have not yet found acceptance in the humanities communities. The reasons why are discussed, and some suggestions are made for how the software might be designed differently to achieve greater acceptance. Finally, a prototype framework is introduced which is designed to provide a common basis for experiments in "digital" palaeography, ideally enabling scholars to exchange quantitative data about scribal hands, exchange processes for generating this data, articulate both the results themselves and the processes used to produce them, and therefore to ground their arguments more firmly and perhaps find greater acceptance

Access to recorded interviews: A research agenda

Recorded interviews form a rich basis for scholarly inquiry. Examples include oral histories, community memory projects, and interviews conducted for broadcast media. Emerging technologies offer the potential to radically transform the way in which recorded interviews are made accessible, but this vision will demand substantial investments from a broad range of research communities. This article reviews the present state of practice for making recorded interviews available and the state-of-the-art for key component technologies. A large number of important research issues are identified, and from that set of issues, a coherent research agenda is proposed

A Survey of Geometric Analysis in Cultural Heritage

We present a review of recent techniques for performing geometric analysis in cultural heritage (CH) applications. The survey is aimed at researchers in the areas of computer graphics, computer vision and CH computing, as well as to scholars and practitioners in the CH field. The problems considered include shape perception enhancement, restoration and preservation support, monitoring over time, object interpretation and collection analysis. All of these problems typically rely on an understanding of the structure of the shapes in question at both a local and global level. In this survey, we discuss the different problem forms and review the main solution methods, aided by classification criteria based on the geometric scale at which the analysis is performed and the cardinality of the relationships among object parts exploited during the analysis. We finalize the report by discussing open problems and future perspectives

A knowledge based architecture for the virtual restoration of ancient photos

Historical images are essential documents of the recent past. Nevertheless, time and bad preservation corrupt their physical supports. Digitization can be the solution to extend their \u201clives\u201d, and digital techniques can be used to recover lost information. This task is often difficult and time-consuming, if commercial restoration tools are used for the purpose. A new solution is proposed to help non-expert users in restoring their damaged photos. First, we defined a dual taxonomy for the defects in printed and digitized photos. We represented our restoration domain with an ontology and we created some rules to suggest actions to perform in case of some specific events. Classes and properties of the ontology are included into a knowledge base, that grows dynamically with its use. A prototypal tool and a web application version have been implemented as an interface to the database, and to support non-expert users in the restoration process