Automated thread counting in archaeological textiles using pulse-based feature extraction

Abstract Archaeological textiles are often highly fragmented, posing significant challenges for reconstruction and analysis. Human experts traditionally rely on technical analyses, such as thread counting, to match fragments and determine their origins. However, this process is labour-intensive and time-consuming, especially for degraded artefacts such as the Oseberg tapestry from the Viking Age. Despite the potential for automation, existing computational methods have proven insufficiently robust, lacking the nuanced approach that human expertise offers. We propose a framework for automating thread counting in textile images using a feature extractor to capture thread counts in vertical and horizontal directions. Our method models thread counting as a pulse counting problem, inspired by how humans intuitively count threads. It involves analysing traces of pixel values from binary and gradient images to estimate thread density. Our method was compared to measurements by human experts, and while it tended to overestimate thread counts, it showed promising results in clustering fragments. Specifically, 14 out of 27 fragments were grouped in alignment with archaeologists’ hypotheses regarding their origins. Our method offers a valuable tool for digitally reconstructing archaeological textiles, improving efficiency in analysing and matching fragmented artefacts. Future work will focus on refining our approach with advanced thresholding techniques, modelling complex weaving techniques and utilizing hyperspectral images to improve accuracy in identifying complex weaving structures and mixed thread materials.