A Generic Image Retrieval Method for Date Estimation of Historical Document Collections

Adrià Molina*, Lluis Gomez, Oriol Ramos Terrades, Josep Lladós

*Corresponding author for this work

Research output: Book/ReportProceedingResearchpeer-review

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Date estimation of historical document images is a challenging problem, with several contributions in the literature that lack of the ability to generalize from one dataset to others.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

Original languageEnglish
Number of pages15
DOIs
Publication statusPublished - 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer-Verlag
ISSN (Print)0302-9743

Keywords

  • Date estimation
  • Document retrieval
  • Image retrieval
  • Ranking loss
  • Smooth-nDCG

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