Virtual unwrapping is a software pipeline for the noninvasive recovery of texts inside damaged manuscripts via the analysis of three dimensional tomographic data, typically X-ray micro-CT. Recent advancements to the virtual unwrapping pipeline include the use of trained models to perform the “texturing” phase, where the content written upon a surface is extracted from the 3D volume and projected onto a surface mesh representing that page. Trained models are critical for their ability to discern subtle changes that indicate the presence or absence of writing at a given point on the surface.

The unique datasets and computational pipeline required to train and make use of these models make it a challenge to develop succinct, reliable, and reproducible research infrastructure. This paper presents our response to that challenge and outlines our framework designed to support the ongoing development of machine learning models to advance the capability of virtual unwrapping. Our approach is designed on the principles of visualization, automation, data access, metadata, and consistent benchmarks.