  In recent years, convolutional neural networks (CNNs) took over the field of
document analysis and they became the predominant model for word spotting.
Especially attribute CNNs, which learn the mapping between a word image and an
attribute representation, showed exceptional performances. The drawback of this
approach is the overconfidence of neural networks when used out of their
training distribution. In this paper, we explore different metrics for
quantifying the confidence of a CNN in its predictions, specifically on the
retrieval problem of word spotting. With these confidence measures, we limit
the inability of a retrieval list to reject certain candidates. We investigate
four different approaches that are either based on the network's attribute
estimations or make use of a surrogate model. Our approach also aims at
answering the question for which part of a dataset the retrieval system gives
reliable results. We further show that there exists a direct relation between
the proposed confidence measures and the quality of an estimated attribute
representation.
