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

doi:https://doi.org/10.1145/3386392.3402436

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

University of Haifa

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-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 language	American English
Title of host publication	UMAP 2020 Adjunct - Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization
Pages	373-375
Number of pages	3
ISBN (Electronic)	9781450367110
DOIs	https://doi.org/10.1145/3386392.3402436
State	Published - 14 Jul 2020
Event	28th ACM International Conference on User Modeling, Adaptation, and Personalization, UMAP 2020 - Genoa, Italy Duration: 14 Jul 2020 → 17 Jul 2020

Publication series

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

Conference

Conference	28th ACM International Conference on User Modeling, Adaptation, and Personalization, UMAP 2020
Country/Territory	Italy
City	Genoa
Period	14/07/20 → 17/07/20

Keywords

computer assisted transcription for text images
crowdsourcing
handwritten text recognition

All Science Journal Classification (ASJC) codes

Software

Access to Document

https://doi.org/10.1145/3386392.3402436

Cite this

Wecker, A. J., Schor, U., Raziel-Kretzmer, V., Elovits, D., Lavee, M., Kuflik, T., & Stoekl Ben Ezra, D. (2020). Opportunities for Personalization for Crowdsourcing in Handwritten Text Recognition. In UMAP 2020 Adjunct - Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization (pp. 373-375). (UMAP 2020 Adjunct - Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization). https://doi.org/10.1145/3386392.3402436

Wecker, Alan J. ; Schor, Uri ; Raziel-Kretzmer, Vered et al. / Opportunities for Personalization for Crowdsourcing in Handwritten Text Recognition. UMAP 2020 Adjunct - Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization. 2020. pp. 373-375 (UMAP 2020 Adjunct - Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization).

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title = "Opportunities for Personalization for Crowdsourcing in Handwritten Text Recognition",

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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year = "2020",

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doi = "https://doi.org/10.1145/3386392.3402436",

language = "American English",

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Wecker, AJ, Schor, U, Raziel-Kretzmer, V, Elovits, D, Lavee, M , Kuflik, T & Stoekl Ben Ezra, D 2020, Opportunities for Personalization for Crowdsourcing in Handwritten Text Recognition. in UMAP 2020 Adjunct - Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization. UMAP 2020 Adjunct - Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization, pp. 373-375, 28th ACM International Conference on User Modeling, Adaptation, and Personalization, UMAP 2020, Genoa, Italy, 14/07/20. https://doi.org/10.1145/3386392.3402436

Opportunities for Personalization for Crowdsourcing in Handwritten Text Recognition. / Wecker, Alan J.; Schor, Uri; Raziel-Kretzmer, Vered et al.
UMAP 2020 Adjunct - Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization. 2020. p. 373-375 (UMAP 2020 Adjunct - Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Wecker, Alan J.

AU - Schor, Uri

AU - Raziel-Kretzmer, Vered

AU - Elovits, Dror

AU - Lavee, Moshe

AU - Kuflik, Tsvi

AU - Stoekl Ben Ezra, Daniel

PY - 2020/7/14

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N2 - 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.

AB - 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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SP - 373

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

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Wecker AJ, Schor U, Raziel-Kretzmer V, Elovits D, Lavee M , Kuflik T et al. Opportunities for Personalization for Crowdsourcing in Handwritten Text Recognition. In UMAP 2020 Adjunct - Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization. 2020. p. 373-375. (UMAP 2020 Adjunct - Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization). doi: https://doi.org/10.1145/3386392.3402436

Opportunities for Personalization for Crowdsourcing in Handwritten Text Recognition

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Keywords

All Science Journal Classification (ASJC) codes

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