A comparison of sequential and combined approaches for named entity recognition in a corpus of handwritten medieval charters

Boroş, Emanuela; Romero, Verónica; Maarand, Martin; Zenklova, Katerina; Kreckova, Jitka; Vidal, Enrique; Stutzmann, Dominique; Kermorvant, Christopher

doi:10.1109/icfhr2020.2020.00025

Cited by 16 publications

(8 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wikidata is a KB created by the Wikimedia Foundation 11 . Its main purpose is to store user-generated data from the various projects supported by Wikimedia.…”

Section: Building Resourcesmentioning

confidence: 99%

“…Although these challenges also exist when dealing with contemporary text documents, digitized documents make them harder because of inherent problems associated with the source quality (natural degradation of the documents) and to the digitization process itself (e.g. digitization noise, image quality, and OCR bias) [5][6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

“…people, places, events) but also more abstract, varied, and subtler concepts, such as themes and topics. Furthermore, digitized historical documents have allowed the use of natural language processing (NLP) tools, such as named entity recognition (NER) [9][10][11] and entity linking (EL) [5,7] for enriching automatically the documents. This has attracted the attention of numerous digital humanities researchers since it allows quantitative analysis, e.g.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

MELHISSA: a multilingual entity linking architecture for historical press articles

Pontes¹,

Cabrera-Diego

Moreno

et al. 2021

Int J Digit Libr

Self Cite

View full text Add to dashboard Cite

Digital libraries have a key role in cultural heritage as they provide access to our culture and history by indexing books and historical documents (newspapers and letters). Digital libraries use natural language processing (NLP) tools to process these documents and enrich them with meta-information, such as named entities. Despite recent advances in these NLP models, most of them are built for specific languages and contemporary documents that are not optimized for handling historical material that may for instance contain language variations and optical character recognition (OCR) errors. In this work, we focused on the entity linking (EL) task that is fundamental to the indexation of documents in digital libraries. We developed a Multilingual Entity Linking architecture for HIstorical preSS Articles that is composed of multilingual analysis, OCR correction, and filter analysis to alleviate the impact of historical documents in the EL task. The source code is publicly available. Experimentation has been done over two historical documents covering five European languages (English, Finnish, French, German, and Swedish). Results have shown that our system improved the global performance for all languages and datasets by achieving an F-score@1 of up to 0.681 and an F-score@5 of up to 0.787.

show abstract

“…Wikidata is a KB created by the Wikimedia Foundation 11 . Its main purpose is to store user-generated data from the various projects supported by Wikimedia.…”

Section: Building Resourcesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

MELHISSA: a multilingual entity linking architecture for historical press articles

Pontes¹,

Cabrera-Diego

Moreno

et al. 2021

Int J Digit Libr

Self Cite

View full text Add to dashboard Cite

show abstract

“…HOME : The HOME dataset [41] consists in 420 annotated medieval charters selected amongst 43,000 digitized charters from the archives of the Bohemian Crown and the archives of monasteries. They have been written from 1145 and 1491 in Early Modern German, Latin and Czech.…”

Section: Datasetsmentioning

confidence: 99%

Robust Text Line Detection in Historical Documents: Learning and Evaluation Methods

Boillet,

Kermorvant,

Paquet

2022

Preprint

Self Cite

View full text Add to dashboard Cite

Text line segmentation is one of the key steps in historical document understanding. It is challenging due to the variety of fonts, contents, writing styles and the quality of documents that have degraded through the years.In this paper, we address the limitations that currently prevent people from building line segmentation models with a high generalization capacity. We present a study conducted using three state-of-the-art systems Doc-UFCN, dhSegment and ARU-Net and show that it is possible to build generic models trained on a wide variety of historical document datasets that can correctly segment diverse unseen pages. This paper also highlights the importance of the annotations used during training: each existing dataset is annotated differently. We present a unification of the annotations and show its positive impact on the final text recognition results. In this end, we present a complete evaluation strategy using standard pixel-level metrics, object-level ones and introducing goal-oriented metrics.

show abstract

“…Researchers are of the view that image features of documents are very useful because features are mixed representations of fonts, glyph, colors, etc. As the VIE task involves the document images, some researchers regard it as a pure computer vision task, such as EATEN , TreyNet [Carbonell et al, 2020] and [Boros ¸et al, 2020]. These methods solved the VIE task from the perspective of Optical Character Recognition (OCR ).…”

Section: Related Workmentioning

confidence: 99%

MatchVIE: Exploiting Match Relevancy between Entities for Visual Information Extraction

Tang

Xie²,

Jin

et al. 2021

Preprint

View full text Add to dashboard Cite

Visual Information Extraction (VIE) task aims to extract key information from multifarious document images (e.g., invoices and purchase receipts). Most previous methods treat the VIE task simply as a sequence labeling problem or classification problem, which requires models to carefully identify each kind of semantics by introducing multimodal features, such as font, color, layout. But simply introducing multimodal features couldn't work well when faced with numeric semantic categories or some ambiguous texts. To address this issue, in this paper we propose a novel key-value matching model based on a graph neural network for VIE (MatchVIE). Through key-value matching based on relevancy evaluation, the proposed MatchVIE can bypass the recognitions to various semantics, and simply focuses on the strong relevancy between entities. Besides, we introduce a simple but effective operation, Num2Vec, to tackle the instability of encoded values, which helps model converge more smoothly. Comprehensive experiments demonstrate that the proposed MatchVIE can significantly outperform previous methods. Notably, to the best of our knowledge, MatchVIE may be the first attempt to tackle the VIE task by modeling the relevancy between keys and values and it is a good complement to the existing methods.

show abstract

A comparison of sequential and combined approaches for named entity recognition in a corpus of handwritten medieval charters

Cited by 16 publications

References 16 publications

MELHISSA: a multilingual entity linking architecture for historical press articles

MELHISSA: a multilingual entity linking architecture for historical press articles

Robust Text Line Detection in Historical Documents: Learning and Evaluation Methods

MatchVIE: Exploiting Match Relevancy between Entities for Visual Information Extraction

Contact Info

Product

Resources

About