Article Segmentation in Digitised Newspapers

Digitisation projects preserve and make available vast quantities of historical text. Among these, newspapers are an invaluable resource for the study of human culture and history. Article segmentation identifies each region in a digitised newspaper page that contains an article. Digital humanities, information retrieval (IR), and natural language processing (NLP) applications over digitised archives improve access to text and allow automatic information extraction. The lack of article segmentation impedes these applications. We contribute a thorough review of the existing approaches to article segmentation. Our analysis reveals divergent interpretations of the task, and inconsistent and often ambiguously defined evaluation metrics, making comparisons between systems challenging. We solve these issues by contributing a detailed task definition that examines the nuances and intricacies of article segmentation that are not immediately apparent. We provide practical guidelines on handling borderline cases and devise a new evaluation framework that allows insightful comparison of existing and future approaches. Our review also reveals that the lack of large datasets hinders meaningful evaluation and limits machine learning approaches. We solve these problems by contributing a distant supervision method for generating large datasets for article segmentation. We manually annotate a portion of our dataset and show that our method produces article segmentations over characters nearly as well as costly human annotators. We reimplement the seminal textual approach to article segmentation (Aiello and Pegoretti, 2006) and show that it does not generalise well when evaluated on a large dataset. We contribute a framework for textual article segmentation that divides the task into two distinct phases: block representation and clustering. We propose several techniques for block representation and contribute a novel highly-compressed semantic representation called similarity embeddings. We evaluate and compare different clustering techniques, and innovatively apply label propagation (Zhu and Ghahramani, 2002) to spread headline labels to similar blocks. Our similarity embeddings and label propagation approach substantially outperforms Aiello and Pegoretti but still falls short of human performance. Exploring visual approaches to article segmentation, we reimplement and analyse the state-of-the-art Bansal et al. (2014) approach. We contribute an innovative 2D Markov model approach that captures reading order dependencies and reduces the structured labelling problem to a Markov chain that we decode with Viterbi (1967). Our approach substantially outperforms Bansal et al., achieves accuracy as good as human annotators, and establishes a new state of the art in article segmentation. Our task definition, evaluation framework, and distant supervision dataset will encourage progress in the task of article segmentation. Our state-of-the-art textual and visual approaches will allow sophisticated IR and NLP applications over digitised newspaper archives, supporting research in the digital humanities

Archives, Access and Artificial Intelligence

Digital archives are transforming the Humanities and the Sciences. Digitized collections of newspapers and books have pushed scholars to develop new, data-rich methods. Born-digital archives are now better preserved and managed thanks to the development of open-access and commercial software. Digital Humanities have moved from the fringe to the center of academia. Yet, the path from the appraisal of records to their analysis is far from smooth. This book explores crossovers between various disciplines to improve the discoverability, accessibility, and use of born-digital archives and other cultural assets

Geometric correction of historical Arabic documents

Geometric deformations in historical documents significantly influence the success of both Optical Character Recognition (OCR) techniques and human readability. They may have been introduced at any time during the life cycle of a document, from when it was first printed to the time it was digitised by an imaging device. This Thesis focuses on the challenging domain of geometric correction of Arabic historical documents, where background research has highlighted that existing approaches for geometric correction of Latin-script historical documents are not sensitive to the characteristics of text in Arabic documents and therefore cannot be applied successfully. Text line segmentation and baseline detection algorithms have been investigated to propose a new more suitable one for warped Arabic historical document images. Advanced ideas for performing dewarping and geometric restoration on historical Arabic documents, as dictated by the specific characteristics of the problem have been implemented.In addition to developing an algorithm to detect accurate baselines of historical printed Arabic documents the research also contributes a new dataset consisting of historical Arabic documents with different degrees of warping severity.Overall, a new dewarping system, the first for Historical Arabic documents, has been developed taking into account both global and local features of the text image and the patterns of the smooth distortion between text lines. By using the results of the proposed line segmentation and baseline detection methods, it can cope with a variety of distortions, such as page curl, arbitrary warping and fold