Digital Palaeography

This article seeks to explore new digital ways of distinguishing between scribal hands in medieval manuscripts. An analysis of traditional palaeographical approaches to hand identification will be followed by a discussion in which attention will be paid both to the use of computer software to enhance existing methods of scribal identification, and to the benefits of "Quill", an innovative automatic writer identification tool. A case study involving a manuscript of the collected works of Christine de Pizan (London, British Library, Harley 4431) will serve to demonstrate that traditional palaeographical methods of analysing scribal hands can greatly benefit from the use of specialised computer software

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

The Palaeographical Method under the Light of a Digital Approach

This paper has the twofold aim of reflecting upon a humanities computing approach to palaeography, and of making such reflections - together with its related experimental results - fruitful at the implementation level. Firstly, the paper explores the methodological issues related to the use of a digital tool to support the palaeographical analysis of medieval handwriting. It claims that humanities computing methods can assist in making explicit those processes of the palaeographical research that encompass detailed analyses, in particular of the handwriting and, more generally, of other idiosyncratic features of written cultural artefacts. Thus, palaeographical tools are to be contextualised and used within a broader methodological framework where their role is to mediate the vision, the comparison, the representation, the analysis and the interpretation of these objects. Secondly, the paper attempts to evaluate the experimentations carried out with a specific software and, in so doing, to test a humanities computing approach to palaeography at a practical level, so as to direct future implementations. Some of these implementations have already been carried out by the current developers of the application in question with whom the author collaborates closely, while others are still in progress and in need of future iterative refinements

A Comprehensive Study of ImageNet Pre-Training for Historical Document Image Analysis

Automatic analysis of scanned historical documents comprises a wide range of image analysis tasks, which are often challenging for machine learning due to a lack of human-annotated learning samples. With the advent of deep neural networks, a promising way to cope with the lack of training data is to pre-train models on images from a different domain and then fine-tune them on historical documents. In the current research, a typical example of such cross-domain transfer learning is the use of neural networks that have been pre-trained on the ImageNet database for object recognition. It remains a mostly open question whether or not this pre-training helps to analyse historical documents, which have fundamentally different image properties when compared with ImageNet. In this paper, we present a comprehensive empirical survey on the effect of ImageNet pre-training for diverse historical document analysis tasks, including character recognition, style classification, manuscript dating, semantic segmentation, and content-based retrieval. While we obtain mixed results for semantic segmentation at pixel-level, we observe a clear trend across different network architectures that ImageNet pre-training has a positive effect on classification as well as content-based retrieval

Automatic Dating of Historical Documents

With the growing number of digitized documents available to researchers it is becoming possible to answer scientific questions by simply analyzing the image content. In this article, a new approach for the automatic dating of historical documents is proposed. It is based on an approach only recently proposed for scribe identification. It uses local RootSIFT descriptors which are encoded using VLAD. The method is evaluated using a dataset consisting of context areas of medieval papal charters covering around 150 years from 1049 to 1198 AD. Experimental results show very promising mean absolute errors of about 17 years

PapyRow: A Dataset of Row Images from Ancient Greek Papyri for Writers Identification

Papyrology is the discipline that studies texts written on ancient papyri. An important problem faced by papyrologists and, in general by paleographers, is to identify the writers, also known as scribes, who contributed to the drawing up of a manuscript. Traditionally, paleographers perform qualitative evaluations to distinguish the writers, and in recent years, these techniques have been combined with computer-based tools to automatically measure quantities such as height and width of letters, distances between characters, inclination angles, number and types of abbreviations, etc. Recently-emerged approaches in digital paleography combine powerful machine learning algorithms with high-quality digital images. Some of these approaches have been used for feature extraction, other to classify writers with machine learning algorithms or deep learning systems. However, traditional techniques require a preliminary feature engineering step that involves an expert in the field. For this reason, publishing a well-labeled dataset is always a challenge and a stimulus for the academic world as researchers can test their methods and then compare their results from the same starting point. In this paper, we propose a new dataset of handwriting on papyri for the task of writer identification. This dataset is derived directly from GRK-Papyri dataset and the samples are obtained with some enhancement image operation. This paper presents not only the details of the dataset but also the operation of resizing, rotation, background smoothing, and rows segmentation in order to overcome the difficulties posed by the image degradation of this dataset. It is prepared and made freely available for non-commercial research along with their confirmed ground-truth information related to the task of writer identification

Historical Analyses of Disordered Handwriting

Handwritten texts carry significant information, extending beyond the meaning of their words. Modern neurology, for example, benefits from the interpretation of the graphic features of writing and drawing for the diagnosis and monitoring of diseases and disorders. This article examines how handwriting analysis can be used, and has been used historically, as a methodological tool for the assessment of medical conditions and how this enhances our understanding of historical contexts of writing. We analyze handwritten material, writing tests and letters, from patients in an early 20th-century psychiatric hospital in southern Germany (Irsee/Kaufbeuren). In this institution, early psychiatrists assessed handwriting features, providing us novel insights into the earliest practices of psychiatric handwriting analysis, which can be connected to Berkenkotter’s research on medical admission records. We finally consider the degree to which historical handwriting bears semiotic potential to explain the psychological state and personality of a writer, and how future research in written communication should approach these sources