Although a substantial corpus of digital materials is now available to scholarship across the disciplines, objective evidence of their use, impact, and value, based on a robust assessment, is sparse. Traditional methods of assessment of impact in the humanities, notably citation in scholarly publications, are not an effective way of assessing impact of digital content. These issues are problematic in the field of Digital Humanities where there is a need to effectively assess impact to justify its continued funding and existence. A number of qualitative and quantitative methods exist that can be used to monitor the use of digital resources in various contexts although they have yet to be applied widely. These have been made available to the creators, managers, and funders of digital content in an accessible form through the TIDSR (Toolkit for the Impact of Digital Scholarly Resources) developed by the Oxford Internet Institute. In 2011, the authors of this article developed the SPHERE project (Stormont Parliamentary Hansards: Embedded in Research and Education) specifically to use TIDSR to evaluate the use and impact of The Stormont Papers, a digital collection of the Hansards of the Stormont Northern Irish Parliament from 1921 to 1972. This article presents the methodology, findings, and analysis of the project. The authors argue that TIDSR is a useful and, critically, transferrable method to understand and increase the impact of digital resources. The findings of the project are modified into a series of wider recommendations on protecting the investment in digital resources by increasing their use, value, and impact. It is reasonable to suggest that effectively showing the impact of Digital Humanities is critical to its survival.

The digitization of historical documents is crucial for preserving the cultural heritage of the society. An essential step in this process is converting scanned images to text using Optical Character Recognition (OCR), which can enable further search, information extraction, etc. Unfortunately, this is a challenging problem as standard OCR tools are not tailored to deal with historical orthography or challenging layouts. Thus, it is standard to apply an additional text correction step on the OCR output when dealing with such documents. In this work, we focus on Bulgarian, and we create the first benchmark dataset for evaluating the OCR text correction for historical Bulgarian documents written in the first standardized Bulgarian orthography: the Drinov orthography from the 19th century. We further develop a method for automatically generating synthetic data in this orthography, as well as in the subsequent Ivanchev orthography, by leveraging vast amounts of contemporary literature Bulgarian texts. We then use state-of-the-art LLMs and encoder-decoder framework which we augment with diagonal attention loss and copy and coverage mechanisms to improve the post-OCR text correction. The proposed method reduces the errors introduced during the recognition. It improves the quality of the documents by 25%, which is an increase of 16% compared to the state-of-the-art on the ICDAR 2019 Bulgarian dataset. We release our data and code at https://github.com/angelbeshirov/post-ocr-text-correction.