short-paper

Opportunities for Personalization for Crowdsourcing in Handwritten Text Recognition

Authors:

Alan J. Wecker,

Uri Schor,

Vered Raziel-Kretzmer,

Dror Elovits,

Moshe Lavee,

Tsvi Kuflik,

Daniel Stoekl Ben EzraAuthors Info & Claims

UMAP '20 Adjunct: Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization

Pages 373 - 375

https://doi.org/10.1145/3386392.3402436

Published: 13 July 2020 Publication History

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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

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Wecker ARaziel-Kretzmer VKiessling BEzra DLavee MKuflik TElovits DSchorr MSchor UJablonski P(2022)Tikkoun Sofrim: Making Ancient Manuscripts Digitally Accessible: The Case of Midrash TanhumaJournal on Computing and Cultural Heritage 10.1145/347677615:2(1-20)Online publication date: 7-Apr-2022
https://dl.acm.org/doi/10.1145/3476776
Kavvetsos GKatsini CRaptis G(2021)Introducing Privacy Preservation in Personalized Cultural Heritage ApplicationsAdjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3450614.3463388(194-198)Online publication date: 21-Jun-2021
https://dl.acm.org/doi/10.1145/3450614.3463388
Ardissono LMauro NRaptis GWecker A(2020)Workshop on Personalized Access to Cultural Heritage: PATCH'20Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3340631.3398670(396-397)Online publication date: 7-Jul-2020
https://dl.acm.org/doi/10.1145/3340631.3398670

Index Terms

Opportunities for Personalization for Crowdsourcing in Handwritten Text Recognition
1. Applied computing
  1. Arts and humanities

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Published In

UMAP '20 Adjunct: Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization

July 2020

395 pages

ISBN:9781450379502

DOI:10.1145/3386392

Editors:
Tsvi Kuflik
University of Haifa, Israel
,
Ilaria Torre
University of Genoa, Italy
,
Robin Burke
University of Colorado, Boulder, USA
,
Cristina Gena
University of Turin, Italy

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 July 2020

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PHC Maimonide

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UMAP '20

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UMAP '20: 28th ACM Conference on User Modeling, Adaptation and Personalization

July 14 - 17, 2020

Genoa, Italy

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Overall Acceptance Rate 162 of 633 submissions, 26%

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Wecker ARaziel-Kretzmer VKiessling BEzra DLavee MKuflik TElovits DSchorr MSchor UJablonski P(2022)Tikkoun Sofrim: Making Ancient Manuscripts Digitally Accessible: The Case of Midrash TanhumaJournal on Computing and Cultural Heritage 10.1145/347677615:2(1-20)Online publication date: 7-Apr-2022
https://dl.acm.org/doi/10.1145/3476776
Kavvetsos GKatsini CRaptis G(2021)Introducing Privacy Preservation in Personalized Cultural Heritage ApplicationsAdjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3450614.3463388(194-198)Online publication date: 21-Jun-2021
https://dl.acm.org/doi/10.1145/3450614.3463388
Ardissono LMauro NRaptis GWecker A(2020)Workshop on Personalized Access to Cultural Heritage: PATCH'20Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3340631.3398670(396-397)Online publication date: 7-Jul-2020
https://dl.acm.org/doi/10.1145/3340631.3398670

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An online overlaid handwritten Japanese text recognition system for small tablet

iiit-indic-hw-words: A Dataset for Indic Handwritten Text Recognition

Large Vocabulary Recognition of On-Line Handwritten Cursive Words