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Date estimation of historical document images is a challenging problem, withseveral contributions in the literature that lack of the ability to generalizefrom one dataset to others. This paper presents a robust date estimation systembased in a retrieval approach that generalizes well in front of heterogeneouscollections. we use a ranking loss function named smooth-nDCG to train aConvolutional Neural Network that learns an ordination of documents for eachproblem. One of the main usages of the presented approach is as a tool forhistorical contextual retrieval. It means that scholars could performcomparative analysis of historical images from big datasets in terms of theperiod where they were produced. We provide experimental evaluation ondifferent types of documents from real datasets of manuscript and newspaperimages.

A Generic Image Retrieval Method for Date Estimation of Historical Document Collections