Opportunities for Personalization for Crowdsourcing in Handwritten Text Recognition

Alan J. Wecker, Uri Schor, Vered Raziel-Kretzmer, Dror Elovits, Moshe Lavee, Tsvi Kuflik, Daniel Stoekl Ben Ezra

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

Abstract

Transcribing historical handwritten documents is a difficult task. One facet is that it is a very tedious task normally performed by experts. Some newer techniques rely on crowdsourcing of manual transcription. Crowdsourcing helps speeding up the transcription process, but it is still limited and brings with it new challenges. Though crowdsourcing transcriptions can imply a repetitive task done by a large group of users, there is in fact room for personalization. This paper reports on insights gathered for future personalizations from the "Tikkoun Sofrim" project, that implements a framework for combining automatic handwritten text recognition with crowdsourcing for transcription of complete handwritten manuscripts. As a case study, the Hebrew "Midrash Tanhuma" manuscripts were selected.

Original languageEnglish
Title of host publicationUMAP 2020 Adjunct - Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization
PublisherAssociation for Computing Machinery, Inc
Pages373-375
Number of pages3
ISBN (Electronic)9781450367110
DOIs
StatePublished - 14 Jul 2020
Event28th ACM International Conference on User Modeling, Adaptation, and Personalization, UMAP 2020 - Genoa, Italy
Duration: 14 Jul 202017 Jul 2020

Publication series

NameUMAP 2020 Adjunct - Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization

Conference

Conference28th ACM International Conference on User Modeling, Adaptation, and Personalization, UMAP 2020
Country/TerritoryItaly
CityGenoa
Period14/07/2017/07/20

Bibliographical note

Funding Information:
The project is funded by PHC Maimonide 41146YC Thanks also to Avigail Ohali, Pawel Jablonski, Pauline Signoret and Moshe Schorr for their contributions to the project and discussions.

Publisher Copyright:
© 2020 ACM.

Keywords

  • computer assisted transcription for text images
  • crowdsourcing
  • handwritten text recognition

ASJC Scopus subject areas

  • Software

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