Neural text line extraction in historical documents: a two-stage clustering approach

Accessibility of the valuable cultural heritage which is hidden in countless scanned historical documents is the motivation for the presented dissertation. The developed (fully automatic) text line extraction methodology combines state-of-the-art machine learning techniques and modern image processing methods. It demonstrates its quality by outperforming several other approaches on a couple of benchmarking datasets. The method is already being used by a wide audience of researchers from different disciplines and thus contributes its (small) part to the aforementioned goal.Das Erschließen des unermesslichen Wissens, welches in unzähligen gescannten historischen Dokumenten verborgen liegt, bildet die Motivation für die vorgelegte Dissertation. Durch das Verknüpfen moderner Verfahren des maschinellen Lernens und der klassischen Bildverarbeitung wird in dieser Arbeit ein vollautomatisches Verfahren zur Extraktion von Textzeilen aus historischen Dokumenten entwickelt. Die Qualität wird auf verschiedensten Datensätzen im Vergleich zu anderen Ansätzen nachgewiesen. Das Verfahren wird bereits durch eine Vielzahl von Forschern verschiedenster Disziplinen genutzt

READ-BAD: A New Dataset and Evaluation Scheme for Baseline Detection in Archival Documents

Text line detection is crucial for any application associated with Automatic Text Recognition or Keyword Spotting. Modern algorithms perform good on well-established datasets since they either comprise clean data or simple/homogeneous page layouts. We have collected and annotated 2036 archival document images from different locations and time periods. The dataset contains varying page layouts and degradations that challenge text line segmentation methods. Well established text line segmentation evaluation schemes such as the Detection Rate or Recognition Accuracy demand for binarized data that is annotated on a pixel level. Producing ground truth by these means is laborious and not needed to determine a method's quality. In this paper we propose a new evaluation scheme that is based on baselines. The proposed scheme has no need for binarization and it can handle skewed as well as rotated text lines. The ICDAR 2017 Competition on Baseline Detection and the ICDAR 2017 Competition on Layout Analysis for Challenging Medieval Manuscripts used this evaluation scheme. Finally, we present results achieved by a recently published text line detection algorithm.Comment: Submitted to DAS201

Safety, Tolerability and Clinical Effects of a Rapid Dose Titration of Subcutaneous Treprostinil Therapy in Pulmonary Arterial Hypertension: A Prospective Multi-Centre Trial

Background: Subcutaneous treprostinil has dose-dependent beneficial effects in patients with severe pulmonary arterial hypertension, but adverse effects like infusion site pain can lead to treatment discontinuation. Objectives: The objective of this study was to evaluate safety, tolerability and clinical effects of a rapid up-titration dosing regimen of subcutaneous treprostinil using proactive infusion site pain management. Methods: Effects of rapid up-titration dosing regimen on tolerability and clinical parameters were evaluated in this 16-week, open-label multi-centre study. Results: Thirty-nine patients with idiopathic or heritable pulmonary arterial hypertension on stable treatment with oral pulmonary arterial hypertension-approved drugs (90% on dual combination therapy) were included. Patients achieved a median treprostinil dosage of 35.7 ng/kg/min after 16 weeks. A good overall safety profile was demonstrated with 3 patients (8%) withdrawing due to infusion site pain, which occurred in 97% of patients. After 16 weeks, median 6-min walking distance, cardiac index, pulmonary vascular resistance, and tricuspid annular plane systolic excursion improved. Conclusions: Rapid up-titration of subcutaneous treprostinil was well tolerated, achieving a clinically effective dose associated with improvement of exercise capacity and haemodynamics after 16 weeks. A rapid dose titration regimen and proactive infusion site pain management may improve the handling of this therapy and contribute to better treatment outcome. (C) 2016 S. Karger AG, Basel

Transforming scholarship in the archives through handwritten text recognition:Transkribus as a case study

Purpose: An overview of the current use of handwritten text recognition (HTR) on archival manuscript material, as provided by the EU H2020 funded Transkribus platform. It explains HTR, demonstrates Transkribus, gives examples of use cases, highlights the affect HTR may have on scholarship, and evidences this turning point of the advanced use of digitised heritage content. The paper aims to discuss these issues. - Design/methodology/approach: This paper adopts a case study approach, using the development and delivery of the one openly available HTR platform for manuscript material. - Findings: Transkribus has demonstrated that HTR is now a useable technology that can be employed in conjunction with mass digitisation to generate accurate transcripts of archival material. Use cases are demonstrated, and a cooperative model is suggested as a way to ensure sustainability and scaling of the platform. However, funding and resourcing issues are identified. - Research limitations/implications: The paper presents results from projects: further user studies could be undertaken involving interviews, surveys, etc. - Practical implications: Only HTR provided via Transkribus is covered: however, this is the only publicly available platform for HTR on individual collections of historical documents at time of writing and it represents the current state-of-the-art in this field. - Social implications: The increased access to information contained within historical texts has the potential to be transformational for both institutions and individuals. - Originality/value: This is the first published overview of how HTR is used by a wide archival studies community, reporting and showcasing current application of handwriting technology in the cultural heritage sector