Improving pattern spotting in historical documents using feature pyramid networks

Ignacio Úbeda, Jose M. Saavedra, Stéphane Nicolas, Caroline Petitjean, Laurent Heutte*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

Pattern spotting consists of locating different instances of a given object (i.e. an image query) in a collection of historical document images. These patterns may vary in shape, size, color, context and even style because they are hand-drawn, which makes pattern spotting a difficult task. To tackle this problem, we propose a Convolutional Neural Network (CNN) approach based on Feature Pyramid Networks (FPN) as the feature extractor of our system. Using FPN allows to extract descriptors of local regions of the documents to be indexed and queries, at multiple scales with just a single forward pass. Experiments conducted on DocExplore dataset show that the proposed system improves mAP by 73% (from 0.157 to 0.272) in pattern localization compared with state-of-the-art results, even when the feature extractor is not trained with domain-specific data. Memory requirement and computation time are also decreased since the descriptor dimension used for distance computation is reduced by a factor of 16.

Original languageEnglish
Pages (from-to)398-404
Number of pages7
JournalPattern Recognition Letters
Volume131
DOIs
StatePublished - Mar 2020
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2020

Keywords

  • Convolutional neural network
  • Historical documents
  • Image retrieval
  • Pattern spotting

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