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

Albiglutide and cardiovascular outcomes in patients with type 2 diabetes and cardiovascular disease (Harmony Outcomes): a double-blind, randomised placebo-controlled trial

Background: Glucagon-like peptide 1 receptor agonists differ in chemical structure, duration of action, and in their effects on clinical outcomes. The cardiovascular effects of once-weekly albiglutide in type 2 diabetes are unknown. We aimed to determine the safety and efficacy of albiglutide in preventing cardiovascular death, myocardial infarction, or stroke. Methods: We did a double-blind, randomised, placebo-controlled trial in 610 sites across 28 countries. We randomly assigned patients aged 40 years and older with type 2 diabetes and cardiovascular disease (at a 1:1 ratio) to groups that either received a subcutaneous injection of albiglutide (30–50 mg, based on glycaemic response and tolerability) or of a matched volume of placebo once a week, in addition to their standard care. Investigators used an interactive voice or web response system to obtain treatment assignment, and patients and all study investigators were masked to their treatment allocation. We hypothesised that albiglutide would be non-inferior to placebo for the primary outcome of the first occurrence of cardiovascular death, myocardial infarction, or stroke, which was assessed in the intention-to-treat population. If non-inferiority was confirmed by an upper limit of the 95% CI for a hazard ratio of less than 1·30, closed testing for superiority was prespecified. This study is registered with ClinicalTrials.gov, number NCT02465515. Findings: Patients were screened between July 1, 2015, and Nov 24, 2016. 10 793 patients were screened and 9463 participants were enrolled and randomly assigned to groups: 4731 patients were assigned to receive albiglutide and 4732 patients to receive placebo. On Nov 8, 2017, it was determined that 611 primary endpoints and a median follow-up of at least 1·5 years had accrued, and participants returned for a final visit and discontinuation from study treatment; the last patient visit was on March 12, 2018. These 9463 patients, the intention-to-treat population, were evaluated for a median duration of 1·6 years and were assessed for the primary outcome. The primary composite outcome occurred in 338 (7%) of 4731 patients at an incidence rate of 4·6 events per 100 person-years in the albiglutide group and in 428 (9%) of 4732 patients at an incidence rate of 5·9 events per 100 person-years in the placebo group (hazard ratio 0·78, 95% CI 0·68–0·90), which indicated that albiglutide was superior to placebo (p<0·0001 for non-inferiority; p=0·0006 for superiority). The incidence of acute pancreatitis (ten patients in the albiglutide group and seven patients in the placebo group), pancreatic cancer (six patients in the albiglutide group and five patients in the placebo group), medullary thyroid carcinoma (zero patients in both groups), and other serious adverse events did not differ between the two groups. There were three (<1%) deaths in the placebo group that were assessed by investigators, who were masked to study drug assignment, to be treatment-related and two (<1%) deaths in the albiglutide group. Interpretation: In patients with type 2 diabetes and cardiovascular disease, albiglutide was superior to placebo with respect to major adverse cardiovascular events. Evidence-based glucagon-like peptide 1 receptor agonists should therefore be considered as part of a comprehensive strategy to reduce the risk of cardiovascular events in patients with type 2 diabetes. Funding: GlaxoSmithKline

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

Novel methods for writer identification and retrieval

Zusammenfassung in deutscher SpracheWriter identification is the task of identifying the writer of a handwritten document, based on a set of documents where the authors are known. It can be used e.g. for tasks in forensics and for historical document analysis. In contrast to this, writer retrieval is to receive a ranking of the pages in the set of documents sorted according to the similarity of handwriting and can be used for clustering a not indexed set of documents according to the individual handwriting. State-of-the-art methods calculate features on the contours of the characters, so pre-processing steps are needed to extract this contour. In contrast to this in this thesis, three novel approaches for writer identification and writer retrieval are presented. The first is based on the bag of words approach, which is well known for object recognition. SIFT features are calculated on the handwriting and then an occurrence histogram is generated which is then used for the identification of the writer. The second method is based on the Fisher vector. Again, SIFT features are generated on the handwriting, but this time the gradient vectors of a Gaussian Mixture Model (GMM) are used to generate the feature vector for writer identification. The last method is based on Convolutional Neural Network (CNN). A CNN is trained on image patches and the classification layer is cut off and the second last layer is used as feature vector for this patch. The mean vector of all patches on one page is the feature vector for the handwriting and is used for identification and retrieval. The methods presented are evaluated and compared to the state of the art on different scientific databases and additionally on a historic dataset using common evaluation metrics for writer identification. The evaluations show that the three methods proposed outperform the state of the art on many of the different tasks on these datasets. Advantages and possible weaknesses are discussed. The methods proposed achieve good results (>90%) on every dataset used for evaluation.11

Learning Features for Writer Retrieval and Identification using Triplet CNNs

The final publication is available via https://doi.org/10.1109/ICFHR-2018.2018.00045.This paper presents a method for writer retrieval and identification using a feature descriptor learned by a Convolutional Neural Network. Instead of using a network for classification, we propose the use of a triplet network that learns a similarity measure for image patches. Patches of the handwriting are extracted and mapped into an embedding where this similarity measure is defined by the L2 distance. The triplet network is trained by maximizing the interclass distance, while minimizing the intraclass distance in this embedding. The image patches are encoded using the learned feature descriptor. By applying the Vector of Locally Aggregated Descriptors encoding to these features, we generate a feature vector for each document image. A detailed parameter evaluation is given which shows that this method achieves a mean average precision of 86.1% on the ICDAR 2013 writer identification dataset, but future work has to be done to improve the performance on historic datasets. In addition, the strategy for clustering the feature space is investigated.European Union's Horizon 202

Word Beam Search: A Connectionist Temporal Classification Decoding Algorithm

The final publication is available via https://doi.org/10.1109/ICFHR-2018.2018.00052.Recurrent Neural Networks (RNNs) are used for sequence recognition tasks such as Handwritten Text Recognition (HTR) or speech recognition. If trained with the Connectionist Temporal Classification (CTC) loss function, the output of such a RNN is a matrix containing character probabilities for each time-step. A CTC decoding algorithm maps these character probabilities to the final text. Token passing is such an algorithm and is able to constrain the recognized text to a sequence of dictionary words. However, the running time of token passing depends quadratically on the dictionary size and it is not able to decode arbitrary character strings like numbers. This paper proposes word beam search decoding, which is able to tackle these problems. It constrains words to those contained in a dictionary, allows arbitrary non-word character strings between words, optionally integrates a word-level language model and has a better running time than token passing. The proposed algorithm outperforms best path decoding, vanilla beam search decoding and token passing on the IAM and Bentham HTR datasets. An open-source implementation is provided.European Union's Horizon 202

Mass Digitization of Archival Documents using Mobile Phones

The final publication is available via https://doi.org/10.1145/3151509.3151526.Digital copies of historical documents are needed for the Digital Humanities. Currently, cameras of standard mobile phones are able to capture documents with a resolution of about 330 dpi for document sizes up to DIN A4 (German standard, 297 x 210 mm), which allows a digitization of documents using a standard device. Thus, scholars are able to take images of documents in archives themselves without the need of book scanners or other devices. This paper presents a scanning app, which comprises a real time page detection, quality assessment (focus measure) and an automated detection of a page turn over if books are scanned. Additionally, a portable device - the ScanTent - to place the mobile phone during scanning is presented. The page detection is evaluated on the ICDAR2015 SmartDoc competition dataset and shows a reliable page detection with an average Jaccard index of 75%.European Union's Horizon 202

cBAD: ICDAR2017 Competition on Baseline Detection

The final publication is available via https://doi.org/10.1109/ICDAR.2017.222.The cBAD competition aims at benchmarking state-of-the-art baseline detection algorithms. It is in line with previous competitions such as the ICDAR 2013 Handwriting Segmentation Contest. A new, challenging, dataset was created to test the behavior of state-of-the-art systems on real world data. Since traditional evaluation schemes are not applicable to the size and modality of this dataset, we present a new one that introduces baselines to measure performance. We received submissions from five different teams for both tracks.European Union's Horizon 202

ICDAR2017 Competition on Historical Document Writer Identification (Historical-WI)

The final publication is available via https://doi.org/10.1109/ICDAR.2017.225.The ICDAR 2017 Competition on Historical Document Writer Identification is dedicated to record the most recent advances made in the field of writer identification. The goal of the writer identification task is the retrieval of pages, which have been written by the same author. The test dataset used in this competition consists of 3600 handwritten pages originating from 13th to 20th century. It contains manuscripts from 720 different writers where each writer contributed five pages. This paper describes the dataset, as well as the details of the competition. Five different institutions submitted six methods which were ranked using identification and retrieval metrics. The paper describes the competition details including the dataset, the evaluation measures used as well as a short description of each submitted method.European Union's Horizon 202