The implications of handwritten text recognition for accessing the past at scale

Before Handwritten Text Recognition (HTR), manuscripts were costly to convert to machine-processable text for research and analysis. With HTR now achieving high levels of accuracy, we ask what near-future behaviour, interaction, experience, values and infrastructures may occur when HTR is applied to historical documents? When combined with mass-digitisation of GLAM (galleries, libraries, archives and museums) content, how will HTR’s application, use, and affordances generate new knowledge of the past, and affect our information environment? This paper’s findings emerge from a literature review surveying current understanding of the impact of HTR, to explore emerging issues over the coming decade. We aim to deconstruct the simplistic narrative that the speed, efficiency, and scale of HTR will “transform scholarship in the archives” (Muehlberger et al., 2019: 955), providing a more nuanced consideration of its application, possibilities, and opportunities. In doing so, our recommendations will assist researchers, data and platform providers, memory institutions and data scientists to understand how the results of HTR interact with the wider information environment.We find that HTR supports the creation of accurate transcriptions from historical manuscripts, and the enhancement of existing datasets. HTR facilitates access to a greater range of materials, including endangered languages, enabling a new focus on personal and private materials (diaries, letters), increasing access to historical voices not usually incorporated into the historical record, and increasing the scale and heterogeneity of available material. The production of general training models leads to a virtuous digitisation circle where similar datasets are easier – and therefore more likely – to be produced. This leads to the requirement for processes that will facilitate the storage, and discoverability of HTR generated content, and for memory institutions to rethink search and access to collections. Challenges include HTR’s dependency on digitisation, its relation to archival history and omission, and the entrenchment of bias in data sources. The paper details several near future issues, including: the potential of HTR for the basis of automated metadata extraction; the integration of advanced Artificial Intelligence (AI) processes (including Large Language Models (LLMs) and generative AI) into HTR systems; legal and moral issues such as copyright, privacy and data ethics which are challenged by the use of HTR; how individual contributions to shared HTR models can be credited; and the environmental costs of HTR infrastructure. We identify the need for greater collaboration between communities including historians, information scientists, and data scientists to navigate these issues, and for further skills support to allow non-specialist audiences to make the most of HTR. Data literacy will become increasingly important, as will building frameworks to establish data sharing, data consent, and reuse principles, particularly in building open repositories to share models and datasets. Finally, we suggest that an understanding of how HTR is changing the information environment is a crucial aspect of future technological development. <br/

On automating editions:The affordances of Handwritten Text Recognition platforms for scholarly editing

Recent developments in Handwritten Text Recognition (HTR) mean that automated editions – presentational editions generated from both digital images of text, and their corresponding transcriptions created by artificial intelligence – are now available to adopt, adapt, and critique. This paper responds to an absence within scholarly editing literature regarding HTR. HTR is a machine-learning approach that creates accurate transcriptions of images of handwritten documents. We highlight developments in text recognition technology, demonstrating that automated standardised editions are no longer a future possibility, but a reality necessary of consideration within a scholarly editing framework.We do this via a case study of creating a standardised online edition in the HTR platform Transkribus of the manuscripts of Marjorie Fleming (1803-1811), a Scottish child author who became posthumously famous for her free-thinking and precocious diaries. As well as providing a cost-effective way to generate machine-processable transcripts at scale, Transkribus can now generate digital online editions via its ‘read&amp;search’ platform. This provides an efficient mechanism to share and search digitised texts, bypassing previous procedures and disrupting established processes for data formatting, hosting, and delivery of online editions. However, we show that while read&amp;search can be considered a scholarly digital edition, it needs further development to be encountered as a critical digital edition, providing suggestions for ongoing development. Automating the process of creating scholarly digital editions will encourage others to create them, democratising the digital edition landscape, although we reflect on the ramifications this may have. <br/

Improved Functional Outcome After Peripheral Nerve Stimulation of the Impaired Forelimb Post-stroke

Lack of blood flow to the brain, i.e., ischemic stroke, results in loss of nerve cells and therefore loss of function in the effected brain regions. There is no effective treatment to improve lost function except restoring blood flow within the first several hours. Rehabilitation strategies are widely used with limited success. The purpose of this study was to examine the effect of electrical stimulation on the impaired upper extremity to improve functional recovery after stroke. We developed a rodent model using an electrode cuff implant onto a single peripheral nerve (median nerve) of the paretic forelimb and applied daily electrical stimulation. The skilled forelimb reaching test was used to evaluate functional outcome after stroke and electrical stimulation. Anterograde axonal tracing from layer V pyramidal neurons with biotinylated dextran amine was done to evaluate the formation of new neuronal connections from the contralesional cortex to the deafferented spinal cord. Rats receiving electrical stimulation on the median nerve showed significant improvement in the skilled forelimb reaching test in comparison with stroke only and stroke with sham stimulation. Rats that received electrical stimulation also exhibited significant improvement in the latency to initiate adhesive removal from the impaired forelimb, indicating better sensory recovery. Furthermore, axonal tracing analysis showed a significant higher midline fiber crossing index in the cervical spinal cord of rats receiving electrical stimulation. Our results indicate that direct peripheral nerve stimulation leads to improved sensorimotor recovery in the stroke-impaired forelimb, and may be a useful approach to improve post-stroke deficits in human patients

Pharmacological and non-pharmacological countermeasures to Space Motion Sickness: a systematic review

IntroductionSpace Motion Sickness (SMS) is a syndrome that affects around 70% of astronauts and includes symptoms of nausea, dizziness, fatigue, vertigo, headaches, vomiting, and cold sweating. Consequences range from discomfort to severe sensorimotor and cognitive incapacitation, which might cause potential problems for mission-critical tasks and astronauts and cosmonauts' well-being. Both pharmacological and non-pharmacological countermeasures have been proposed to mitigate SMS. However, their effectiveness has not been systematically evaluated. Here we present the first systematic review of published peer-reviewed research on the effectiveness of pharmacological and non-pharmacological countermeasures to SMS.MethodsWe performed a double-blind title and abstract screening using the online Rayyan collaboration tool for systematic reviews, followed by a full-text screening. Eventually, only 23 peer-reviewed studies underwent data extraction.ResultsBoth pharmacological and non-pharmacological countermeasures can help mitigate SMS symptoms.DiscussionNo definitive recommendation can be given regarding the superiority of any particular countermeasure approach. Importantly, there is considerable heterogeneity in the published research methods, lack of a standardized assessment approach, and small sample sizes. To allow for consistent comparisons between SMS countermeasures in the future, standardized testing protocols for spaceflight and ground-based analogs are needed. We believe that the data should be made openly available, given the uniqueness of the environment in which it is collected.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021244131

Matricaria chamomilla CH12 decreases handling stress in Nelore calves

Matricaria chamomilla CH12 is a phytotherapeutic or homeopathic product, which has been used to reduce stress. Here, we examined its effect on preventing handling stress in bovines. Sixty Nelore calves were randomly distributed into two equal groups. One group was administered Matricaria chamomilla CH12 in diet and the other the 'control' was not. Animals in both groups were maintained unstressed for 30 days to adjust to the feeding system and pasture, and were then stressed by constraint on the 31th, 38th, 45th and 60th experimental days. Blood samples were taken on these days after animals had been immobilization in a trunk contention for 5 min. Stress was followed by analyzing serum cortisol levels. These peaked on the 45th day and then decreased, but not to baseline, on the 60th day. On the 45th day cortisol levels were significantly lower in animals fed Matricaria chamomilla CH12, suggesting that this product reduces stress. These effects may be a consequence of its inhibiting cortisol production and its calming and anxiolytic effects

Exploring data provenance in handwritten text recognition infrastructure:Sharing and reusing ground truth data, referencing models, and acknowledging contributions. Starting the conversation on how we could get it done

This paper discusses best practices for sharing and reusing Ground Truth in Handwritten Text Recognition infrastructures, and ways to reference and acknowledge contributions to the creation and enrichment of data within these Machine Learning systems. We discuss how one can publish Ground Truth data in a repository and, subsequently, inform others. Furthermore, we suggest appropriate citation methods for HTR data, models, and contributions made by volunteers. Moreover, when using digitised sources (digital facsimiles), it becomes increasingly important to distinguish between the physical object and the digital collection. These topics all relate to the proper acknowledgement of labour put into digitising, transcribing, and sharing Ground Truth HTR data. This also points to broader issues surrounding the use of Machine Learning in archival and library contexts, and how the community should begin toacknowledge and record both contributions and data provenance