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via Transkribus is covered: however, this is the only publicly available platform for HTR on individual collections of historical documents at time of writing and it represents the current state-of-the-art in this field.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

We present a competition on text block segmentation within the framework of the International Conference on Pattern Recognition (ICPR) 2020. The main goal of this competition is to automatically analyse the structure of historical newspaper pages with a subsequent evaluation of the participants' algorithms performance. In contrast to many existing segmentation methods, instead of working on pixels, the present study has a focus on clustering baselines/text lines into text blocks. Therefore, we introduce a new measure based on a baseline detection evaluation scheme. But also common pixel-based approaches could participate without restrictions. Working on baseline level addresses directly the application scenario where for a given image the contained text should be extracted in blocks for further investigations. We present the results of three submissions. The experiments have shown that text blocks can be reliably detected both on pages with a simple layout and on pages with a complex layout.

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