A Generic Image Retrieval Method for Date Estimation of Historical Document Collections
This provides a tool for scholars to perform comparative analysis of historical images by estimating their production periods, though it is incremental as it builds on existing retrieval methods.
The paper tackles the problem of date estimation for historical document images by proposing a retrieval-based system that generalizes across heterogeneous collections, achieving robust performance on real datasets of manuscripts and newspapers.
Date estimation of historical document images is a challenging problem, with several contributions in the literature that lack of the ability to generalize from one dataset to others.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.