BibTeX

@inProceedings{dannells-etal-2019-evaluation-278761,
	title        = {Evaluation and refinement of an enhanced OCR process for mass digitisation. },
	abstract     = {Great expectations are placed on the capacity of heritage institutions to make their collections available in digital format. Datadriven research is becoming a key concept within the humanities and social sciences. Kungliga biblioteket’s (National Library of Sweden, KB)collections of digitised newspaper can thus be regarded as unique cultural data sets with information that rarely is conveyed in other media types. The digital format makes it possible to explore these resources in ways not feasible while in printed form. As texts are no longer only read but also subjected to computer based analysis the demand on the correct rendering of the original text increases. OCR technologies for converting images to machine-readable text play a fundamental part in making these resources available, but the effectiveness vary with the type of document being processed. This is evident in relation to the digitisation of newspapers where factors relating to their production, layout and paper quality often impair the OCR production. In order to improve the machine readable text, especially in relation to the digitisation of newspapers, KB initiated the development of an OCR-module where key parameters can be adjusted according to the characteristics of the material being processed. The purpose of this paper is to present the project goals and methods.},
	booktitle    = {Proceedings of the Digital Humanities in the Nordic Countries 4th Conference (DHN 2019), Copenhagen, Denmark, March 5-8, 2019. Edited by: Costanza Navarretta, Manex Agirrezabal, Bente Maegaard},
	author       = {Dannélls, Dana and Johansson, Torsten and Björk, Lars},
	year         = {2019},
	publisher    = {University of Copenhagen, Faculty of Humanities},
	address      = {Copenhagen},
}

@inProceedings{dannells-etal-2020-evaluation-296165,
	title        = {Evaluation of a Two-OCR Engine Method: First Results on Digitized Swedish Newspapers Spanning over nearly 200 Years},
	abstract     = {In this paper we present a two-OCR engine method that was developed at Kungliga biblioteket (KB), the National Library of Sweden, for improving the correctness of the OCR for mass digitization of Swedish newspapers. We report the first quantitative evaluation results on a material spanning over nearly 200 years. In this first evaluation phase we experimented with word lists for different time periods. Although there was no significant overall improvement of the OCR results, the evaluation shows that some combinations of word lists are successful for certain periods and should therefore be explored further. },
	booktitle    = { CLARIN Annual Conference 2020, (Virtual Event), 5-7 October, 2020. Book of Abstracts},
	author       = {Dannélls, Dana and Björk, Lars and Dirdal, Ove and Johansson, Torsten},
	year         = {2020},
}

@inProceedings{dannells-simon-2020-supervised-289944,
	title        = {Supervised OCR Post-Correction of Historical Swedish Texts: What Role Does the OCR System Play?},
	abstract     = {Current approaches for post-correction of OCR errors offer solutions that are tailored to a specific OCR system. This can be problematic if the post-correction method was trained on a specific OCR
system but have to be applied on the result of another system. Whereas OCR post-correction of historical text has received much attention lately, the question of what role does the OCR system play for the post-correction method has not been addressed. In this study we explore a dataset of
400 documents of historical Swedish text which has been OCR processed by three state-of-the-art OCR systems: Abbyy Finereader, Tesseract and Ocropus. We examine the OCR results of each system and present a supervised machine learning post-correction method that tries to approach
the challenges exhibited by each system. We study the performance of our method by using three evaluation tools: PrimA, Språkbanken evaluation tool and Frontiers Toolkit. Based on the evaluation analysis we discuss the impact each of the OCR systems has on the results of the post-
correction method. We report on quantitative and qualitative results showing varying degrees of OCR post-processing complexity that are important to consider when developing an OCR post-correction method.},
	booktitle    = {Proceedings of the Digital Humanities in the Nordic Countries, 5th Conference, Riga, Latvia, October 21-23, 2020},
	editor       = {Sanita Reinsone and Inguna Skadiņa and Anda Baklāne and Jānis Daugavietis},
	author       = {Dannélls, Dana and Simon, Persson},
	year         = {2020},
	publisher    = {CEUR-WS},
}

@inProceedings{dannells-etal-2021-engine-305700,
	title        = {A Two-OCR Engine Method for Digitized Swedish Newspapers },
	abstract     = {In  this  paper  we  present  a  two-OCR  engine  method  that  was  developed  at  Kungliga  biblioteket (KB), the National Library of Sweden, for improving the correctness of the OCR for mass digitization of Swedish newspapers. To evaluate the method a reference material spanning the years 1818–2018 was prepared and manually transcribed. A quantitative evaluation was then performed against the material. In this first evaluation we experimented with word lists for different time periods. The results show that even though there was no significant overall improvement of the OCR results, some combinations of word lists are successful for certain periods and should therefore be explored further.},
	booktitle    = {Selected Papers from the CLARIN Annual Conference 2020, Linköping Electronic Conference Proceedings 180},
	author       = {Dannélls, Dana and Björk, Lars and Dirdal, Ove and Johansson, Torsten},
	year         = {2021},
	publisher    = {Linköping University Electronic Press},
	address      = {Linköping},
	ISBN         = {978-91-7929-609-4},
}

@inProceedings{skelbye-dannells-2021-processing-306957,
	title        = {OCR Processing of Swedish Historical Newspapers Using Deep Hybrid CNN–LSTM Networks},
	abstract     = {Deep CNN–LSTM hybrid neural networks have proven to improve the accuracy of Optical Character Recognition (OCR) models for different languages. In this paper we examine to what extent these networks improve the OCR accuracy rates on Swedish historical newspapers. By experimenting
with the open source OCR engine Calamari, we are able to show that mixed deep CNN–LSTM hybrid models outperform previous models on the task of character recognition of Swedish historical newspapers spanning 1818–1848. We achieved an average character accuracy rate (CAR) of 97.43% which is a new state–of–the–art result on 19th century Swedish newspaper text. Our data, code and
models are released under CC BY licence.},
	booktitle    = {Proceedings of the International Conference on Recent Advances in Natural Language Processing, 1–3 September, 2021},
	editor       = {Galia Angelova and Maria Kunilovskaya and Ruslan Mitkov and Ivelina Nikolova-Koleva},
	author       = {Skelbye, Molly and Dannélls, Dana},
	year         = {2021},
	publisher    = {INCOMA },
	address      = {Shoumen, Bulgaria},
	ISBN         = {978-954-452-072-4},
}

@inProceedings{dannells-virk-2021-supervised-310123,
	title        = {A Supervised Machine Learning Approach for Post-OCR Error Detection for Historical Text },
	abstract     = {Training machine learning models with high accuracy requires careful feature engineering, which involves finding the best feature combinations and extracting their values from the data. The task becomes extremely laborious for specific problems such as post Optical Character Recognition (OCR) error detection because of the diversity of errors in the data. In this paper we present a machine learning approach which exploits character n-gram statistics as the only feature for the OCR error detection task. Our method achieves a significant improvement over the baseline reaching state-of-the-art results of 91% and 89% F1 measure on English and Swedish datasets respectively. We report various experiments to select the appropriate machine learning algorithm and to compare our approach to previously reported traditional approaches.},
	booktitle    = {Linköping Electronic Press Workshop and Conference Collection. Selected contributions from the Eighth Swedish Language Technology Conference (SLTC-2020), 25-27 November, 2020 },
	author       = {Dannélls, Dana and Virk, Shafqat},
	year         = {2021},
	publisher    = {Linköping Electronic Press },
	address      = {Linköping},
}

@book{alfter-2019-cross-303714,
	title        = {Cross-border collaborative journalism},
	abstract     = {Cross-Border Collaborative Journalism is a detailed guide to transnational reporting, a cutting-edge journalistic strategy. In the twenty-first century, the most pressing political and social issues, such as financial crises, wealth inequality, migration flows and environmental collapse, transcend national borders. In reaction, journalists are increasingly collaborating across the globe to produce impactful and in-depth reporting. Recent agenda-setting cross-border collaborations include LuxLeaks, Panama Papers and Football Leaks.

Brigitte Alfter takes the reader, step-by-step, through the history of cross-border collaborative journalism and the current working practices behind it. The book draws from the author’s own experience, as well as exclusive interviews with other pioneers of cross-border journalism, and notable case studies are integrated throughout. },
	author       = {Alfter, Brigitte},
	year         = {2019},
	publisher    = {Routledge},
	ISBN         = {9781138613638},
}