Under erasure: Jenny Holzer's war paintings

This article examines Jenny Holzer’s painterly reworkings of redacted American military documents, comparing her practice with some of Nancy Spero’s extended visual narratives of torture and victimization. As two artists immersed in a feminist visual politics (and poetics) of representation where language is both a vehicle and a form of expression, they adopt contrasting strategies of transformation: for Spero via allegory and the mythic, for Holzer through an aesthetic of negation. I read their work partly through Jacques Rancière’s notion of the necessity for bringing traumatic events into visibility, and I argue that in their respective scripto-visual artworks and sensitivity to the materiality of language and its performative dimension, they ‘make the inhuman perceptible’. I also consider their practice as evidence of an ongoing project of foregrounding arts responsibility as witness to history and the historical subject, seeing in their respective modes of figuration and emphasis upon surface (presence/absence, colour, writing) a means of inscribing the body in the text

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

Under erasure: Jenny Holzer's war paintings

This article examines Jenny Holzer’s painterly reworkings of redacted American military documents, comparing her practice with some of Nancy Spero’s extended visual narratives of torture and victimization. As two artists immersed in a feminist visual politics (and poetics) of representation where language is both a vehicle and a form of expression, they adopt contrasting strategies of transformation: for Spero via allegory and the mythic, for Holzer through an aesthetic of negation. I read their work partly through Jacques Rancière’s notion of the necessity for bringing traumatic events into visibility, and I argue that in their respective scripto-visual artworks and sensitivity to the materiality of language and its performative dimension, they ‘make the inhuman perceptible’. I also consider their practice as evidence of an ongoing project of foregrounding arts responsibility as witness to history and the historical subject, seeing in their respective modes of figuration and emphasis upon surface (presence/absence, colour, writing) a means of inscribing the body in the text

A Methodology for Mentoring Writing in Law Practice: Using Textual Clues to Provide Effective and Efficient Feedback

Becoming a successful legal writer is a process that begins in law school and continues intensively during the beginning years of a lawyer\u27s career. Throughout this process, in both contexts, a writer benefits enormously from feedback on his analysis, and how that analysis is conveyed, from those more experienced. Much has been written about how legal educators should respond to student written work, yet little addresses the role that supervising attorneys can play in mentoring the writing of less experienced colleagues. This article therefore proposes a methodology to help supervisor-mentors provide, in an efficient manner, effective feedback on junior lawyers\u27 writing. The article begins by discussing why a mentor should focus her feedback initially on the analytical foundation of a piece of writing and put off until later copy-editing and commenting on basic clarity of expression. It goes on to recommend a methodology by which a mentor can draw on her experience as a lawyer to identify, from the face of a document, a range of textual and structural clues that likely indicate analytical problems, even when time pressures prevent her from gaining an independent understanding of the document\u27s substance. The article explains how, based on these clues, a mentor can provide feedback that will help a junior lawyer revise a current piece of writing and develop skills to write more successfully in the future. The article concludes by applying this methodology to a hypothetical problem to illustrate this process and to provide the reader with a hands-on practicum using the suggested methodology

Document image analysis and recognition: a survey

This paper analyzes the problems of document image recognition and the existing solutions. Document recognition algorithms have been studied for quite a long time, but despite this, currently, the topic is relevant and research continues, as evidenced by a large number of associated publications and reviews. However, most of these works and reviews are devoted to individual recognition tasks. In this review, the entire set of methods, approaches, and algorithms necessary for document recognition is considered. A preliminary systematization allowed us to distinguish groups of methods for extracting information from documents of different types: single-page and multi-page, with text and handwritten contents, with a fixed template and flexible structure, and digitalized via different ways: scanning, photographing, video recording. Here, we consider methods of document recognition and analysis applied to a wide range of tasks: identification and verification of identity, due diligence, machine learning algorithms, questionnaires, and audits. The groups of methods necessary for the recognition of a single page image are examined: the classical computer vision algorithms, i.e., keypoints, local feature descriptors, Fast Hough Transforms, image binarization, and modern neural network models for document boundary detection, document classification, document structure analysis, i.e., text blocks and tables localization, extraction and recognition of the details, post-processing of recognition results. The review provides a description of publicly available experimental data packages for training and testing recognition algorithms. Methods for optimizing the performance of document image analysis and recognition methods are described.The reported study was funded by RFBR, project number 20-17-50177. The authors thank Sc. D. Vladimir L. Arlazarov (FRC CSC RAS), Pavel Bezmaternykh (FRC CSC RAS), Elena Limonova (FRC CSC RAS), Ph. D. Dmitry Polevoy (FRC CSC RAS), Daniil Tropin (LLC “Smart Engines Service”), Yuliya Chernysheva (LLC “Smart Engines Service”), Yuliya Shemyakina (LLC “Smart Engines Service”) for valuable comments and suggestions

The Anglo-Scottish Ballad and its Imaginary Contexts

This is the first book to combine contemporary debates in ballad studies with the insights of modern textual scholarship. Just like canonical literature and music, the ballad should not be seen as a uniquely authentic item inextricably tied to a documented source, but rather as an unstable structure subject to the vagaries of production, reception, and editing. Among the matters addressed are topics central to the subject, including ballad origins, oral and printed transmission, sound and writing, agency and editing, and textual and melodic indeterminacy and instability. While drawing on the time-honoured materials of ballad studies, the book offers a theoretical framework for the discipline to complement the largely ethnographic approach that has dominated in recent decades. Primarily directed at the community of ballad and folk song scholars, the book will be of interest to researchers in several adjacent fields, including folklore, oral literature, ethnomusicology, and textual scholarship

Ancient ancestors for modern practices: An evolutionary concept analysis of digital marginalia

Marginalia, the notes readers write in the blank spaces of their books, are significant objects of study in bibliography and book history, among other fields. Due to factors including findability and fragile book materials, marginalia from the nineteenth and twentieth centuries are difficult to study. The same does not necessarily have to be true for similar objects from the twenty-first century. This thesis uses Rodger’s evolutionary concept analysis to analyze the usage of digital marginalia in the scholarly literature from 1991 to 2020. Beginning with an overview of bibliography and the history of marginalia, this thesis situates digital marginalia in a bibliographic context. Digital marginalia’s definitions, characteristic attributes, events related to the creation of digital marginalia, and concepts related to the practice are then examined. Bringing in connections to bibliographic concepts, this thesis argues that digital marginalia and bibliography provide each other reciprocal value. Like their physical counterparts, digital marginalia provide evidence of users’ interactions with media, their social interactions through that media, and their sociocultural contexts

Advanced document data extraction techniques to improve supply chain performance

In this thesis, a novel machine learning technique to extract text-based information from scanned images has been developed. This information extraction is performed in the context of scanned invoices and bills used in financial transactions. These financial transactions contain a considerable amount of data that must be extracted, refined, and stored digitally before it can be used for analysis. Converting this data into a digital format is often a time-consuming process. Automation and data optimisation show promise as methods for reducing the time required and the cost of Supply Chain Management (SCM) processes, especially Supplier Invoice Management (SIM), Financial Supply Chain Management (FSCM) and Supply Chain procurement processes. This thesis uses a cross-disciplinary approach involving Computer Science and Operational Management to explore the benefit of automated invoice data extraction in business and its impact on SCM. The study adopts a multimethod approach based on empirical research, surveys, and interviews performed on selected companies.The expert system developed in this thesis focuses on two distinct areas of research: Text/Object Detection and Text Extraction. For Text/Object Detection, the Faster R-CNN model was analysed. While this model yields outstanding results in terms of object detection, it is limited by poor performance when image quality is low. The Generative Adversarial Network (GAN) model is proposed in response to this limitation. The GAN model is a generator network that is implemented with the help of the Faster R-CNN model and a discriminator that relies on PatchGAN. The output of the GAN model is text data with bonding boxes. For text extraction from the bounding box, a novel data extraction framework consisting of various processes including XML processing in case of existing OCR engine, bounding box pre-processing, text clean up, OCR error correction, spell check, type check, pattern-based matching, and finally, a learning mechanism for automatizing future data extraction was designed. Whichever fields the system can extract successfully are provided in key-value format.The efficiency of the proposed system was validated using existing datasets such as SROIE and VATI. Real-time data was validated using invoices that were collected by two companies that provide invoice automation services in various countries. Currently, these scanned invoices are sent to an OCR system such as OmniPage, Tesseract, or ABBYY FRE to extract text blocks and later, a rule-based engine is used to extract relevant data. While the system’s methodology is robust, the companies surveyed were not satisfied with its accuracy. Thus, they sought out new, optimized solutions. To confirm the results, the engines were used to return XML-based files with text and metadata identified. The output XML data was then fed into this new system for information extraction. This system uses the existing OCR engine and a novel, self-adaptive, learning-based OCR engine. This new engine is based on the GAN model for better text identification. Experiments were conducted on various invoice formats to further test and refine its extraction capabilities. For cost optimisation and the analysis of spend classification, additional data were provided by another company in London that holds expertise in reducing their clients' procurement costs. This data was fed into our system to get a deeper level of spend classification and categorisation. This helped the company to reduce its reliance on human effort and allowed for greater efficiency in comparison with the process of performing similar tasks manually using excel sheets and Business Intelligence (BI) tools.The intention behind the development of this novel methodology was twofold. First, to test and develop a novel solution that does not depend on any specific OCR technology. Second, to increase the information extraction accuracy factor over that of existing methodologies. Finally, it evaluates the real-world need for the system and the impact it would have on SCM. This newly developed method is generic and can extract text from any given invoice, making it a valuable tool for optimizing SCM. In addition, the system uses a template-matching approach to ensure the quality of the extracted information