Transforming scholarship in the archives through handwritten text recognition

Abstract

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via Transkribus is covered: however, this is the only publicly available platform for HTR on individual collections of historical documents at time of writing and it represents the current state-of-the-art in this field.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.

General Models for Handwritten Text Recognition: Feasibility and State-of-the Art. German Kurrent as an Example

“…The current state-of-the art will be presented briefly in 2.1 according to which a reasonable recognition rate for alphabet scripts can be achieved provided that we have enough training material at our disposal. for these recent improvements is the high level of investment by the European Union, e.g., in the Recognition and Enrichment of Archival Documents project (READ), leading to the virtual research environment Transkribus (Muehlberger et al, 2019), and by national funding agencies, e.g., in eScriptorium (INRIA, 2021;Stökl Ben Ezra, 2019).…”

Automatic Estimation of Age Distributions from the First Ottoman Empire Population Register Series by Using Deep Learning

“…Therefore, an automatic method is needed to process these documents rapidly. Processing historical manuscripts is an up-to-date research topic that has seen dramatic growth recently [1][2][3]. However, historical Arabic document processing is a difficult research issue.…”

Optimisation of archival processes involving digitisation of typewritten documents

“…printed materials), some of them can be a bit more complicated, like typewritten documents, while for other, like handwritten documents, it can be very difficult and sometimes impossible to efficiently apply either optical or intelligent character recognition (ICR) and achieve meaningful results. It is expected that the handwritten text recognition (HTR) might benefit more from the application of machine learning (ML) and artificial intelligence (AI) approaches (Muehlberger et al , 2019) than the type of archival documents discussed in this work. However, HTR accuracy can also be improved using similar digitisation testing methodology as the one discussed here.…”