Scope

The overall theme of DATeCH 2022 is “Sharing and Sustaining Digitisation Knowledge”. Given the interdisciplinary, distributed and sometimes fragmented nature of the digitisation ecosystem, we invite authors to consider how such knowledge can be sustainably shared for the benefit of the community as a whole. We welcome scientific and practice-based contributions on - but not limited to - the following themes:

• Digital methods for the analysis of digital cultural heritage, including Text and Data Mining, Natural Language Processing, Network Analysis, etc.

• Image and Data Processing in the Cultural Heritage sector, including Computer Vision, Artificial Intelligence and Machine Learning

• Improving digitisation quality: defining and evaluating quality and usability of digitised cultural heritage

• Advancing Ground Truth production including synthetic and semantic level ground truth

• Digitisation practices and workflows, tools and infrastructure; institutional embedding of research results into professional practice

• Advanced digitisation of cultural heritage including multispectral imaging and 3D

• Interdisciplinary challenges in digital humanities and digital cultural heritage including intersections between cultural, natural and scientific heritage

• Standardisation, Interoperability and Data Formats: METS/ALTO, PAGE XML, TEI, IIIF, etc.

• Digital Preservation challenges for digitised cultural heritage including OAIS

• Ethical and legal considerations for Digitisation of Cultural Heritage

• Advancing OCR and/or HTR technologies including tools for minority, historical and ancient languages; methods and tools for post-correction of OCR and/or HTR results; OCR/HTR for complex documents, e.g. musical notation, mathematical formulas, tabular data, illustrated texts, comics, graphic novels

• Innovative access methods for historical texts and corpora, including corpus building, collections as data and ‘datasets on demand’

• Crowdsourcing techniques for collecting and annotating data in digital humanities, e.g. classification tasks for Machine Learning.

• Visualisation techniques and interfaces for search and research in digital humanities.

• Ontological and linked data based contextualisation of digitised and born-digital scholarly data resources

• Semantic integration, transformation, matching, knowledge representation and management.