  The context of this paper is the creation of large uniform archaeological
datasets from heterogeneous published resources, such as find catalogues - with
the help of AI and Big Data. The paper is concerned with the challenge of
consistent assemblages of archaeological data. We cannot simply combine
existing records, as they differ in terms of quality and recording standards.
Thus, records have to be recreated from published archaeological illustrations.
This is only a viable path with the help of automation. The contribution of
this paper is a new workflow for collecting data from archaeological find
catalogues available as legacy resources, such as archaeological drawings and
photographs in large unsorted PDF files; the workflow relies on custom software
(AutArch) supporting image processing, object detection, and interactive means
of validating and adjusting automatically retrieved data. We integrate
artificial intelligence (AI) in terms of neural networks for object detection
and classification into the workflow, thereby speeding up, automating, and
standardising data collection. Objects commonly found in archaeological
catalogues - such as graves, skeletons, ceramics, ornaments, stone tools and
maps - are detected. Those objects are spatially related and analysed to
extract real-life attributes, such as the size and orientation of graves based
on the north arrow and the scale. We also automate recording of geometric
whole-outlines through contour detection, as an alternative to landmark-based
geometric morphometrics. Detected objects, contours, and other automatically
retrieved data can be manually validated and adjusted. We use third millennium
BC Europe (encompassing cultures such as 'Corded Ware' and 'Bell Beaker', and
their burial practices) as a 'testing ground' and for evaluation purposes; this
includes a user study for the workflow and the AutArch software.
