Developing the ArchAIDE application: A digital workflow for identifying, organising and sharing archaeological pottery using automated image recognition

Anichini, F., Banterle, F., Buxeda i Garrigós, J., Callieri, M., Dershowitz, N., Diaz, D. L., Evans, T., Gattiglia, G., Gualandi, M. L., Hervas, M.A., Itkin, B., Madrid i Fernandez, M., Miguel Gascón, E., Remmy, M., Richards, J., , Scopigno, R. , Vila, L., Wolf, L., Wright, H., Zallocco, M.

Summary

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Every day, archaeologists are working to discover and tell stories using objects from the past, investing considerable time, effort and funding to identify and characterise individual finds. Pottery is of fundamental importance for the comprehension and dating of archaeological contexts, and for understanding the dynamics of production, trade flows, and social interactions. Today, characterisation and classification of ceramics are carried out manually, through the expertise of specialists and the use of analogue catalogues held in archives and libraries. While not seeking to replace the knowledge and expertise of specialists, the ArchAIDE project (archaide.eu) worked to optimise and economise identification process, developing a new system that streamlines the practice of pottery recognition in archaeology, using the latest automatic image recognition technology. At the same time, ArchAIDE worked to ensure archaeologists remained at the heart of the decision-making process within the identification workflow, and focussed on optimising tasks that were repetitive and time consuming. Specifically, ArchAIDE worked to support the essential classification and interpretation work of archaeologists (during both fieldwork and post-excavation analysis) with an innovative app for tablets and smartphones. This paper summarises the work of this three-year project, funded by the European Union's Horizon 2020 Research and Innovation Programme under grant agreement N.693548, with a consortium of partners which has representing both the academic and industry-led ICT domains, and the academic and development-led archaeology domains. The collaborative work of the archaeological and technical partners created a pipeline where potsherds are photographed, their characteristics compared against a trained neural network, and the results returned with suggested matches from a comparative collection with typical pottery types and characteristics. Once the correct type is identified, all relevant information for that type is linked to the new sherd and stored within a database that can be shared online.


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