Evaluation of Different Tagging Schemes for Named Entity Recognition in Handwritten Documents-Reference-Cited by-同舟云学术

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Evaluation of Different Tagging Schemes for Named Entity Recognition in Handwritten Documents

Published:2023 Issue: Volume: Page:3-16
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
language:
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Author:

Villanova-Aparisi David^ORCID,Martínez-Hinarejos Carlos-D.^ORCID,Romero Verónica^ORCID,Pastor-Gadea Moisés

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-41682-8_1

Reference31 articles.

1. Abadie, N., Carlinet, E., Chazalon, J., Duménieu, B.: A benchmark of named entity recognition approaches in historical documents application to 19th century French directories. In: Uchida, S., Barney, E., Eglin, V. (eds.) Document Analysis Systems, pp. 445–460. Springer International Publishing, Cham (2022). https://doi.org/10.1007/978-3-031-06555-2_30

2. Bluche, T.: Deep Neural Networks for Large Vocabulary Handwritten Text Recognition. Ph.D. thesis, Université Paris Sud-Paris XI (2015)

3. Boroş, E., et al.: A comparison of sequential and combined approaches for named entity recognition in a corpus of handwritten medieval charters. In: 2020 17th International Conference on Frontiers in Handwriting Recognition (ICFHR), pp. 79–84. IEEE (2020)

4. Carbonell, M., Villegas, M., Fornés, A., Lladós, J.: Joint recognition of handwritten text and named entities with a neural end-to-end model. In: 2018 13th IAPR International Workshop on Document Analysis Systems (DAS), pp. 399–404. IEEE (2018)

5. Catelli, R., Casola, V., De Pietro, G., Fujita, H., Esposito, M.: Combining contextualized word representation and sub-document level analysis through bi-LSTM+ CRF architecture for clinical de-identification. Knowl.-Based Syst. 213, 106649 (2021)

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1. Neural models for semantic analysis of handwritten document images;International Journal on Document Analysis and Recognition (IJDAR);2024-06-06

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