Teaching archive skills: a pedagogical journey with impact

This article considers the pedagogical practice of the Special Collections staff team at the University of Sussex and the impact of the group visit experience on student learning. It addresses our current group visit teaching offer to students at the University of Sussex and our move to a more student-led active learning approach. It considers the use of the ‘pedagogical toolkit’ including technology within the classroom, and the creation of a document identification form to encourage critical thinking. Our aim for any group visit is to provide a positive first experience and get students enthused about using archives. In 2017 we undertook our own impact study, detailed within the article, to follow the student journey with the intention of finding out if students returned to use archives for their studies as a result of their group visit. Moving forward, this article considers our future activities in response to the impact study and institutional initiatives

Risky business? Addressing the challenges of historical methods in the 'digital age'

Background: The 'digital age' has led to a renaissance in historical methods. The way in which nurse historians can search, collate and analyse sources has changed exponentially over the past two decades. The mass digitisation of books, newspapers and other documents has resulted in the removal of many long-standing barriers to performing historical research, such as budgetary and access restrictions. Despite these expanded opportunities, the nurse historian now faces new challenges when performing historical research. Aim: This paper aims to stimulate discussion on the risky business of conducting nursing historical research in the 'digital age'. In this paper, we examine the technology-born challenges encountered by nurse historians with the objective of proffering potential solutions to address such issues. Discussion: Three contemporary challenges faced by nurse historians are: not knowing how to contain and articulate online searching; being unable to reduce the number of optical character recognition inaccuracies with digitised archaic sources; and being unsure of how to safely incorporate technological tools into historical analysis. Conclusion: Used correctly, new technologies can augment and strengthen traditional historical methods. Nurse historians need to be mindful that the way in which technologies are used is controlled by the user, rather than the technology itself

An empirical study on writer identification and verification from intra-variable individual handwriting

© 2013 IEEE. The handwriting of a person may vary substantially with factors, such as mood, time, space, writing speed, writing medium/tool, writing a topic, and so on. It becomes challenging to perform automated writer verification/identification on a particular set of handwritten patterns (e.g., speedy handwriting) of an individual, especially when the system is trained using a different set of writing patterns (e.g., normal speed) of that same person. However, it would be interesting to experimentally analyze if there exists any implicit characteristic of individuality which is insensitive to high intra-variable handwriting. In this paper, we study some handcrafted features and auto-derived features extracted from intra-variable writing. Here, we work on writer identification/verification from highly intra-variable offline Bengali writing. To this end, we use various models mainly based on handcrafted features with support vector machine and features auto-derived by the convolutional network. For experimentation, we have generated two handwritten databases from two different sets of 100 writers and enlarged the dataset by a data-augmentation technique. We have obtained some interesting results

Understanding Optical Music Recognition

For over 50 years, researchers have been trying to teach computers to read music notation, referred to as Optical Music Recognition (OMR). However, this field is still difficult to access for new researchers, especially those without a significant musical background: Few introductory materials are available, and, furthermore, the field has struggled with defining itself and building a shared terminology. In this work, we address these shortcomings by (1) providing a robust definition of OMR and its relationship to related fields, (2) analyzing how OMR inverts the music encoding process to recover the musical notation and the musical semantics from documents, and (3) proposing a taxonomy of OMR, with most notably a novel taxonomy of applications. Additionally, we discuss how deep learning affects modern OMR research, as opposed to the traditional pipeline. Based on this work, the reader should be able to attain a basic understanding of OMR: its objectives, its inherent structure, its relationship to other fields, the state of the art, and the research opportunities it affords

Justice Blackmun, Abortion and the Myth of Medical Independence

In this article I test this conventional wisdom by explicitly placing medicine at the center of the analysis of Justice Blackmun\u27s opinions on abortion, and then interrogating the connection between law and medicine. Using the Blackmun papers opened to the public in 2004 and augmented by other documents and sources, I examine four critical periods in Blackmun\u27s life: his years at Mayo; his participation in a series of medicine-related cases prior to Roe; the period of intra-Court dynamics in Roe; and the post-Roe period in which a split developed between Blackmun and Roe\u27s critics over the use of medical rhetoric. My first conclusion is that the long-standing Mayo made him do it explanation of Roe is wrong and should be jettisoned

Content Recognition and Context Modeling for Document Analysis and Retrieval

The nature and scope of available documents are changing significantly in many areas of document analysis and retrieval as complex, heterogeneous collections become accessible to virtually everyone via the web. The increasing level of diversity presents a great challenge for document image content categorization, indexing, and retrieval. Meanwhile, the processing of documents with unconstrained layouts and complex formatting often requires effective leveraging of broad contextual knowledge. In this dissertation, we first present a novel approach for document image content categorization, using a lexicon of shape features. Each lexical word corresponds to a scale and rotation invariant local shape feature that is generic enough to be detected repeatably and is segmentation free. A concise, structurally indexed shape lexicon is learned by clustering and partitioning feature types through graph cuts. Our idea finds successful application in several challenging tasks, including content recognition of diverse web images and language identification on documents composed of mixed machine printed text and handwriting. Second, we address two fundamental problems in signature-based document image retrieval. Facing continually increasing volumes of documents, detecting and recognizing unique, evidentiary visual entities (\eg, signatures and logos) provides a practical and reliable supplement to the OCR recognition of printed text. We propose a novel multi-scale framework to detect and segment signatures jointly from document images, based on the structural saliency under a signature production model. We formulate the problem of signature retrieval in the unconstrained setting of geometry-invariant deformable shape matching and demonstrate state-of-the-art performance in signature matching and verification. Third, we present a model-based approach for extracting relevant named entities from unstructured documents. In a wide range of applications that require structured information from diverse, unstructured document images, processing OCR text does not give satisfactory results due to the absence of linguistic context. Our approach enables learning of inference rules collectively based on contextual information from both page layout and text features. Finally, we demonstrate the importance of mining general web user behavior data for improving document ranking and other web search experience. The context of web user activities reveals their preferences and intents, and we emphasize the analysis of individual user sessions for creating aggregate models. We introduce a novel algorithm for estimating web page and web site importance, and discuss its theoretical foundation based on an intentional surfer model. We demonstrate that our approach significantly improves large-scale document retrieval performance