HST-GAN: Historical Style Transfer GAN for Generating Historical Text Images

Boraq Madi, Reem Alaasam, Ahmad Droby, Jihad El-Sana

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

Abstract

This paper presents Historical Style Transfer Generative Adversarial Networks (HST-GAN) for generating historical text images. Our model consists of three blocks: Encoder, Generator, and Discriminator. The Encoder encodes the style to be generated, and the Generator applies an encoded style, S to an input text image, I, and generates a new image with the content of I and the style S. The Discriminator encourages the Generator to enhance the quality of the generated images. Multiple loss functions are applied to ensure the generation of quality images. We evaluated our model against three challenging historical handwritten datasets of two different languages. In addition, we compare the performance of HST-GAN with the state of art approaches and show that HST-GAN provides the best generated images for the three tested datasets. We demonstrate the capability of HST-GAN to transfer multiple styles across domains by taking the style from one dataset and the content from another dataset and generate the content according to the desired style. We test the quality of the style domains transferring using a designated classifier and a human evaluation and show that the generated images are very similar to the original style.

Original languageEnglish
Title of host publicationDocument Analysis Systems - 15th IAPR International Workshop, DAS 2022, Proceedings
EditorsSeiichi Uchida, Elisa Barney, Véronique Eglin
PublisherSpringer Science and Business Media Deutschland GmbH
Pages523-537
Number of pages15
ISBN (Print)9783031065545
DOIs
StatePublished - 1 Jan 2022
Event15th IAPR International Workshop on Document Analysis Systems, DAS 2022 - La Rochelle, France
Duration: 22 May 202225 May 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13237 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference15th IAPR International Workshop on Document Analysis Systems, DAS 2022
Country/TerritoryFrance
CityLa Rochelle
Period22/05/2225/05/22

Keywords

  • Generating document images
  • Generative adversarial networks
  • Historical handwritten styles transfer

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)

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