  Date estimation of historical document images is a challenging problem, with
several contributions in the literature that lack of the ability to generalize
from one dataset to others. This paper presents a robust date estimation system
based in a retrieval approach that generalizes well in front of heterogeneous
collections. we use a ranking loss function named smooth-nDCG to train a
Convolutional Neural Network that learns an ordination of documents for each
problem. One of the main usages of the presented approach is as a tool for
historical contextual retrieval. It means that scholars could perform
comparative analysis of historical images from big datasets in terms of the
period where they were produced. We provide experimental evaluation on
different types of documents from real datasets of manuscript and newspaper
images.
