Article

A Comprehensive Comparison of Open-Source Libraries for Handwritten Text Recognition in Norwegian

Authors:

Martin Maarand,

Knut T. Fosseide,

Christopher KermorvantAuthors Info & Claims

Document Analysis Systems: 15th IAPR International Workshop, DAS 2022, La Rochelle, France, May 22–25, 2022, Proceedings

Pages 399 - 413

https://doi.org/10.1007/978-3-031-06555-2_27

Published: 22 May 2022 Publication History

Abstract

In this paper, we introduce an open database of historical handwritten documents fully annotated in Norwegian, the first of its kind, allowing the development of handwritten text recognition models (HTR) in Norwegian. In order to evaluate the performance of state-of-the-art HTR models on this new base, we conducted a systematic survey of open-source HTR libraries published between 2019 and 2021, identified ten libraries and selected four of them to train HTR models. We trained twelve models in different configurations and compared their performance on both random and scripter-based data splitting. The best recognition results were obtained by the PyLaia and Kaldi libraries which have different and complementary characteristics, suggesting that they should be combined to further improve the results.

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Cited By

Tarride SKermorvant C(2024)Revisiting N-Gram Models: Their Impact in Modern Neural Networks for Handwritten Text RecognitionDocument Analysis and Recognition - ICDAR 202410.1007/978-3-031-70552-6_10(167-182)Online publication date: 30-Aug-2024
https://dl.acm.org/doi/10.1007/978-3-031-70552-6_10
Tarride SSchneider YGenerali-Lince MBoillet MAbadie BKermorvant C(2024)Improving Automatic Text Recognition with Language Models in the PyLaia Open-Source LibraryDocument Analysis and Recognition - ICDAR 202410.1007/978-3-031-70549-6_23(387-404)Online publication date: 30-Aug-2024
https://dl.acm.org/doi/10.1007/978-3-031-70549-6_23
Clérice TPinche AVlachou-Efstathiou MChagué ACamps JLevenson MBrisville-Fertin OBoschetti FFischer FGervers MBoutreux AManton AGabay SO’Connor PHaverals WKestemont MVandyck CKiessling B(2024)CATMuS Medieval: A Multilingual Large-Scale Cross-Century Dataset in Latin Script for Handwritten Text Recognition and BeyondDocument Analysis and Recognition - ICDAR 202410.1007/978-3-031-70543-4_11(174-194)Online publication date: 30-Aug-2024
https://dl.acm.org/doi/10.1007/978-3-031-70543-4_11
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Published In

cover image Guide Proceedings

Document Analysis Systems: 15th IAPR International Workshop, DAS 2022, La Rochelle, France, May 22–25, 2022, Proceedings

May 2022

794 pages

ISBN:978-3-031-06554-5

DOI:10.1007/978-3-031-06555-2

Editors:
Seiichi Uchida
Kyushu University, Fukuoka, Japan
,
Elisa Barney
Boise State University, BOISE, ID, USA
,
Véronique Eglin
LIRIS UMR CNRS, Villeurbanne, France

© Springer Nature Switzerland AG 2022.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 22 May 2022

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Cited By

Tarride SKermorvant C(2024)Revisiting N-Gram Models: Their Impact in Modern Neural Networks for Handwritten Text RecognitionDocument Analysis and Recognition - ICDAR 202410.1007/978-3-031-70552-6_10(167-182)Online publication date: 30-Aug-2024
https://dl.acm.org/doi/10.1007/978-3-031-70552-6_10
Tarride SSchneider YGenerali-Lince MBoillet MAbadie BKermorvant C(2024)Improving Automatic Text Recognition with Language Models in the PyLaia Open-Source LibraryDocument Analysis and Recognition - ICDAR 202410.1007/978-3-031-70549-6_23(387-404)Online publication date: 30-Aug-2024
https://dl.acm.org/doi/10.1007/978-3-031-70549-6_23
Clérice TPinche AVlachou-Efstathiou MChagué ACamps JLevenson MBrisville-Fertin OBoschetti FFischer FGervers MBoutreux AManton AGabay SO’Connor PHaverals WKestemont MVandyck CKiessling B(2024)CATMuS Medieval: A Multilingual Large-Scale Cross-Century Dataset in Latin Script for Handwritten Text Recognition and BeyondDocument Analysis and Recognition - ICDAR 202410.1007/978-3-031-70543-4_11(174-194)Online publication date: 30-Aug-2024
https://dl.acm.org/doi/10.1007/978-3-031-70543-4_11
Villanova-Aparisi DMartínez-Hinarejos CRomero VPastor-Gadea M(2023)Evaluation of Different Tagging Schemes for Named Entity Recognition in Handwritten DocumentsDocument Analysis and Recognition - ICDAR 202310.1007/978-3-031-41682-8_1(3-16)Online publication date: 21-Aug-2023
https://dl.acm.org/doi/10.1007/978-3-031-41682-8_1
Villanova-Aparisi DMartínez-Hinarejos CRomero VPastor-Gadea M(2023)Consistent Nested Named Entity Recognition in Handwritten Documents via Lattice RescoringDocument Analysis and Recognition - ICDAR 202310.1007/978-3-031-41676-7_15(255-268)Online publication date: 21-Aug-2023
https://dl.acm.org/doi/10.1007/978-3-031-41676-7_15
Nikolaidou KSeuret MMokayed HLiwicki M(2022)A survey of historical document image datasetsInternational Journal on Document Analysis and Recognition10.1007/s10032-022-00405-825:4(305-338)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1007/s10032-022-00405-8

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