  Layout analysis is a task of uttermost importance in ancient handwritten
document analysis and represents a fundamental step toward the simplification
of subsequent tasks such as optical character recognition and automatic
transcription. However, many of the approaches adopted to solve this problem
rely on a fully supervised learning paradigm. While these systems achieve very
good performance on this task, the drawback is that pixel-precise text labeling
of the entire training set is a very time-consuming process, which makes this
type of information rarely available in a real-world scenario. In the present
paper, we address this problem by proposing an efficient few-shot learning
framework that achieves performances comparable to current state-of-the-art
fully supervised methods on the publicly available DIVA-HisDB dataset.
