FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents

Guillaume Jaume; Hazım Kemal Ekenel; Jean Philippe Thiran

doi:10.1109/ICDARW.2019.10029

FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents

Guillaume Jaume
, Hazım Kemal Ekenel
, Jean Philippe Thiran

Bilgisayar Mühendisliği Bölümü

Swiss Federal Institute of Technology Lausanne

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

370 Atıf (Scopus)

Özet

We present a new dataset for form understanding in noisy scanned documents (FUNSD) that aims at extracting and structuring the textual content of forms. The dataset comprises 199 real, fully annotated, scanned forms. The documents are noisy and vary widely in appearance, making form understanding (FoUn) a challenging task. The proposed dataset can be used for various tasks, including text detection, optical character recognition, spatial layout analysis, and entity labeling/linking. To the best of our knowledge, this is the first publicly available dataset with comprehensive annotations to address FoUn task. We also present a set of baselines and introduce metrics to evaluate performance on the FUNSD dataset, which can be downloaded at https://guillaumejaume.github.io/FUNSD/.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	2019 International Conference on Document Analysis and Recognition Workshops, ICDARW 2019
Yayınlayan	Institute of Electrical and Electronics Engineers Inc.
Sayfalar	1-6
Sayfa sayısı	6
ISBN (Elektronik)	9781728150543
DOI'lar	https://doi.org/10.1109/ICDARW.2019.10029
Yayın durumu	Yayınlandı - Eyl 2019
Etkinlik	2nd International Workshop on Open Services and Tools for Document Analysis, ICDAR-OST 2019 - ICDAR 2019 Workshop - Sydney, Australia Süre: 21 Eyl 2019 → …

Yayın serisi

Adı	2019 International Conference on Document Analysis and Recognition Workshops, ICDARW 2019
Hacim	2

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	2nd International Workshop on Open Services and Tools for Document Analysis, ICDAR-OST 2019 - ICDAR 2019 Workshop
Ülke/Bölge	Australia
Şehir	Sydney
Periyot	21/09/19 → …

Bibliyografik not

Publisher Copyright:
© 2019 IEEE.

Belgeye Erişim

10.1109/ICDARW.2019.10029

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Jaume, G., Ekenel, H. K., & Thiran, J. P. (2019). FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents. In 2019 International Conference on Document Analysis and Recognition Workshops, ICDARW 2019 (pp. 1-6). (2019 International Conference on Document Analysis and Recognition Workshops, ICDARW 2019; Hac. 2). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDARW.2019.10029

Jaume, Guillaume ; Ekenel, Hazım Kemal ; Thiran, Jean Philippe. / FUNSD : A Dataset for Form Understanding in Noisy Scanned Documents. 2019 International Conference on Document Analysis and Recognition Workshops, ICDARW 2019. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 1-6 (2019 International Conference on Document Analysis and Recognition Workshops, ICDARW 2019).

@inproceedings{5d526f5c333b43909b901443f7f38a1d,

title = "FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents",

abstract = "We present a new dataset for form understanding in noisy scanned documents (FUNSD) that aims at extracting and structuring the textual content of forms. The dataset comprises 199 real, fully annotated, scanned forms. The documents are noisy and vary widely in appearance, making form understanding (FoUn) a challenging task. The proposed dataset can be used for various tasks, including text detection, optical character recognition, spatial layout analysis, and entity labeling/linking. To the best of our knowledge, this is the first publicly available dataset with comprehensive annotations to address FoUn task. We also present a set of baselines and introduce metrics to evaluate performance on the FUNSD dataset, which can be downloaded at https://guillaumejaume.github.io/FUNSD/.",

keywords = "Form Understanding, Optical Character Recognition, Spatial Layout Analysis, Text detection",

author = "Guillaume Jaume and Ekenel, \{Hazım Kemal\} and Thiran, \{Jean Philippe\}",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2nd International Workshop on Open Services and Tools for Document Analysis, ICDAR-OST 2019 - ICDAR 2019 Workshop ; Conference date: 21-09-2019",

year = "2019",

month = sep,

doi = "10.1109/ICDARW.2019.10029",

language = "English",

series = "2019 International Conference on Document Analysis and Recognition Workshops, ICDARW 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1--6",

booktitle = "2019 International Conference on Document Analysis and Recognition Workshops, ICDARW 2019",

address = "United States",

}

Jaume, G, Ekenel, HK & Thiran, JP 2019, FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents. in 2019 International Conference on Document Analysis and Recognition Workshops, ICDARW 2019. 2019 International Conference on Document Analysis and Recognition Workshops, ICDARW 2019, hac. 2, Institute of Electrical and Electronics Engineers Inc., pp. 1-6, 2nd International Workshop on Open Services and Tools for Document Analysis, ICDAR-OST 2019 - ICDAR 2019 Workshop, Sydney, Australia, 21/09/19. https://doi.org/10.1109/ICDARW.2019.10029

FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents. / Jaume, Guillaume; Ekenel, Hazım Kemal; Thiran, Jean Philippe.
2019 International Conference on Document Analysis and Recognition Workshops, ICDARW 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1-6 (2019 International Conference on Document Analysis and Recognition Workshops, ICDARW 2019; Hac. 2).

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

TY - GEN

T1 - FUNSD

T2 - 2nd International Workshop on Open Services and Tools for Document Analysis, ICDAR-OST 2019 - ICDAR 2019 Workshop

AU - Jaume, Guillaume

AU - Ekenel, Hazım Kemal

AU - Thiran, Jean Philippe

PY - 2019/9

Y1 - 2019/9

N2 - We present a new dataset for form understanding in noisy scanned documents (FUNSD) that aims at extracting and structuring the textual content of forms. The dataset comprises 199 real, fully annotated, scanned forms. The documents are noisy and vary widely in appearance, making form understanding (FoUn) a challenging task. The proposed dataset can be used for various tasks, including text detection, optical character recognition, spatial layout analysis, and entity labeling/linking. To the best of our knowledge, this is the first publicly available dataset with comprehensive annotations to address FoUn task. We also present a set of baselines and introduce metrics to evaluate performance on the FUNSD dataset, which can be downloaded at https://guillaumejaume.github.io/FUNSD/.

AB - We present a new dataset for form understanding in noisy scanned documents (FUNSD) that aims at extracting and structuring the textual content of forms. The dataset comprises 199 real, fully annotated, scanned forms. The documents are noisy and vary widely in appearance, making form understanding (FoUn) a challenging task. The proposed dataset can be used for various tasks, including text detection, optical character recognition, spatial layout analysis, and entity labeling/linking. To the best of our knowledge, this is the first publicly available dataset with comprehensive annotations to address FoUn task. We also present a set of baselines and introduce metrics to evaluate performance on the FUNSD dataset, which can be downloaded at https://guillaumejaume.github.io/FUNSD/.

KW - Form Understanding

KW - Optical Character Recognition

KW - Spatial Layout Analysis

KW - Text detection

UR - https://www.scopus.com/pages/publications/105032365955

U2 - 10.1109/ICDARW.2019.10029

DO - 10.1109/ICDARW.2019.10029

M3 - Conference contribution

AN - SCOPUS:105032365955

T3 - 2019 International Conference on Document Analysis and Recognition Workshops, ICDARW 2019

SP - 1

EP - 6

BT - 2019 International Conference on Document Analysis and Recognition Workshops, ICDARW 2019

PB - Institute of Electrical and Electronics Engineers Inc.

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Jaume G, Ekenel HK, Thiran JP. FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents. In 2019 International Conference on Document Analysis and Recognition Workshops, ICDARW 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 1-6. (2019 International Conference on Document Analysis and Recognition Workshops, ICDARW 2019). doi: 10.1109/ICDARW.2019.10029

FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents

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