A Generic Image Retrieval Method for Date Estimation of Historical Document Collections

TL;DR

This paper introduces a robust image retrieval system using CNNs and a ranking loss function for accurate date estimation of diverse historical documents, aiding scholars in contextual analysis across large datasets.

Contribution

It proposes a generalized retrieval-based date estimation method employing smooth-nDCG loss and CNNs, effective across heterogeneous collections.

Findings

Effective across different document types

Outperforms existing methods in accuracy

Facilitates historical contextual retrieval

Abstract

Date estimation of historical document images is a challenging problem, with several contributions in the literature that lack of the ability to generalize from one dataset to others.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.