Word spotting using convolutional siamese network

Berat Kurar Barakat, Reem Alasam, Jihad El-Sana

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

22 Scopus citations

Abstract

We present a method for word spotting using convolutional siamese network. A convolutional siamese network employs two identical convolutional network to rank similarity between two input word images. Once the network is trained, it can then be used to spot not just words with varying writing styles and backgrounds but also to spot out of vocabulary words that are not in the training set. Experiments on the historical Arabic manuscript dataset VML, and on the George Washington dataset shows comparable results with the state of the art.

Original languageEnglish
Title of host publicationProceedings - 13th IAPR International Workshop on Document Analysis Systems, DAS 2018
PublisherInstitute of Electrical and Electronics Engineers
Pages229-234
Number of pages6
ISBN (Electronic)9781538633465
DOIs
StatePublished - 22 Jun 2018
Event13th IAPR International Workshop on Document Analysis Systems, DAS 2018 - Vienna, Austria
Duration: 24 Apr 201827 Apr 2018

Publication series

NameProceedings - 13th IAPR International Workshop on Document Analysis Systems, DAS 2018

Conference

Conference13th IAPR International Workshop on Document Analysis Systems, DAS 2018
Country/TerritoryAustria
CityVienna
Period24/04/1827/04/18

Keywords

  • Historical document image analysis
  • convolutional siamese network
  • deep learning
  • word spotting

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing

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