ICDAR2019 robust reading challenge on arbitrary-shaped text-RRC-ArT

Chee Kheng Chng; Errui Ding; Jingtuo Liu; Dimosthenis Karatzas; Chee Seng Chan; Lianwen Jin; Yuliang Liu; Yipeng Sun; Chun Chet Ng; Canjie Luo; Zihan Ni; Chuan Ming Fang; Shuaitao Zhang; Junyu Han

doi:10.1109/ICDAR.2019.00252

ICDAR2019 robust reading challenge on arbitrary-shaped text-RRC-ArT

Chee Kheng Chng, Errui Ding, Jingtuo Liu, Dimosthenis Karatzas, Chee Seng Chan, Lianwen Jin, Yuliang Liu, Yipeng Sun, Chun Chet Ng, Canjie Luo, Zihan Ni, Chuan Ming Fang, Shuaitao Zhang, Junyu Han

Producció científica: Capítol de llibre › Capítol › Recerca › Avaluat per experts

253 Cites (Scopus)

Resum

This paper reports the ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text-RRC-ArT that consists of three major challenges: i) scene text detection, ii) scene text recognition, and iii) scene text spotting. A total of 78 submissions from 46 unique teams/individuals were received for this competition. The top performing score of each challenge is as follows: i) T1-82.65%, ii) T2.1-74.3%, iii) T2.2-85.32%, iv) T3.1-53.86%, and v) T3.2-54.91%. Apart from the results, this paper also details the ArT dataset, tasks description, evaluation metrics and participants' methods. The dataset, the evaluation kit as well as the results are publicly available at the challenge website.

Idioma original	Anglès
Títol de la publicació	Proceedings - 15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019
Pàgines	1571-1576
Nombre de pàgines	6
ISBN (electrònic)	9781728128610
DOIs	https://doi.org/10.1109/ICDAR.2019.00252
Estat de la publicació	Publicada - de set. 2019

Sèrie de publicacions

Nom	Proceedings of the International Conference on Document Analysis and Recognition, ICDAR
ISSN (imprès)	1520-5363

Accés al document

10.1109/ICDAR.2019.00252

Altres arxius i enllaços

Link to publication in Scopus

Com citar-ho

Chng, C. K., Ding, E., Liu, J., Karatzas, D., Chan, C. S., Jin, L., Liu, Y., Sun, Y., Ng, C. C., Luo, C., Ni, Z., Fang, C. M., Zhang, S., & Han, J. (2019). ICDAR2019 robust reading challenge on arbitrary-shaped text-RRC-ArT. In Proceedings - 15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019 (pàg. 1571-1576). Article 8978157 (Proceedings of the International Conference on Document Analysis and Recognition, ICDAR). https://doi.org/10.1109/ICDAR.2019.00252

@inbook{b6cf5546f04b44dd9b26af11ee910535,

title = "ICDAR2019 robust reading challenge on arbitrary-shaped text-RRC-ArT",

abstract = "This paper reports the ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text-RRC-ArT that consists of three major challenges: i) scene text detection, ii) scene text recognition, and iii) scene text spotting. A total of 78 submissions from 46 unique teams/individuals were received for this competition. The top performing score of each challenge is as follows: i) T1-82.65\%, ii) T2.1-74.3\%, iii) T2.2-85.32\%, iv) T3.1-53.86\%, and v) T3.2-54.91\%. Apart from the results, this paper also details the ArT dataset, tasks description, evaluation metrics and participants' methods. The dataset, the evaluation kit as well as the results are publicly available at the challenge website.",

keywords = "Arbitrary-Shaped Text, Chinese, End-to-End, English, Scene Text Detection, Scene Text Recognition",

author = "Chng, \{Chee Kheng\} and Errui Ding and Jingtuo Liu and Dimosthenis Karatzas and Chan, \{Chee Seng\} and Lianwen Jin and Yuliang Liu and Yipeng Sun and Ng, \{Chun Chet\} and Canjie Luo and Zihan Ni and Fang, \{Chuan Ming\} and Shuaitao Zhang and Junyu Han",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.",

year = "2019",

month = sep,

doi = "10.1109/ICDAR.2019.00252",

language = "English",

series = "Proceedings of the International Conference on Document Analysis and Recognition, ICDAR",

pages = "1571--1576",

booktitle = "Proceedings - 15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019",

}

Chng, CK, Ding, E, Liu, J, Karatzas, D, Chan, CS, Jin, L, Liu, Y, Sun, Y, Ng, CC, Luo, C, Ni, Z, Fang, CM, Zhang, S & Han, J 2019, ICDAR2019 robust reading challenge on arbitrary-shaped text-RRC-ArT. in Proceedings - 15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019., 8978157, Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, pàg. 1571-1576. https://doi.org/10.1109/ICDAR.2019.00252

ICDAR2019 robust reading challenge on arbitrary-shaped text-RRC-ArT. / Chng, Chee Kheng; Ding, Errui; Liu, Jingtuo et al.
Proceedings - 15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019. 2019. pàg. 1571-1576 8978157 (Proceedings of the International Conference on Document Analysis and Recognition, ICDAR).

Producció científica: Capítol de llibre › Capítol › Recerca › Avaluat per experts

TY - CHAP

T1 - ICDAR2019 robust reading challenge on arbitrary-shaped text-RRC-ArT

AU - Chng, Chee Kheng

AU - Ding, Errui

AU - Liu, Jingtuo

AU - Karatzas, Dimosthenis

AU - Chan, Chee Seng

AU - Jin, Lianwen

AU - Liu, Yuliang

AU - Sun, Yipeng

AU - Ng, Chun Chet

AU - Luo, Canjie

AU - Ni, Zihan

AU - Fang, Chuan Ming

AU - Zhang, Shuaitao

AU - Han, Junyu

PY - 2019/9

Y1 - 2019/9

N2 - This paper reports the ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text-RRC-ArT that consists of three major challenges: i) scene text detection, ii) scene text recognition, and iii) scene text spotting. A total of 78 submissions from 46 unique teams/individuals were received for this competition. The top performing score of each challenge is as follows: i) T1-82.65%, ii) T2.1-74.3%, iii) T2.2-85.32%, iv) T3.1-53.86%, and v) T3.2-54.91%. Apart from the results, this paper also details the ArT dataset, tasks description, evaluation metrics and participants' methods. The dataset, the evaluation kit as well as the results are publicly available at the challenge website.

AB - This paper reports the ICDAR2019 Robust Reading Challenge on Arbitrary-Shaped Text-RRC-ArT that consists of three major challenges: i) scene text detection, ii) scene text recognition, and iii) scene text spotting. A total of 78 submissions from 46 unique teams/individuals were received for this competition. The top performing score of each challenge is as follows: i) T1-82.65%, ii) T2.1-74.3%, iii) T2.2-85.32%, iv) T3.1-53.86%, and v) T3.2-54.91%. Apart from the results, this paper also details the ArT dataset, tasks description, evaluation metrics and participants' methods. The dataset, the evaluation kit as well as the results are publicly available at the challenge website.

KW - Arbitrary-Shaped Text

KW - Chinese

KW - End-to-End

KW - English

KW - Scene Text Detection

KW - Scene Text Recognition

UR - https://www.scopus.com/pages/publications/85079861917

U2 - 10.1109/ICDAR.2019.00252

DO - 10.1109/ICDAR.2019.00252

M3 - Chapter

AN - SCOPUS:85079861917

T3 - Proceedings of the International Conference on Document Analysis and Recognition, ICDAR

SP - 1571

EP - 1576

BT - Proceedings - 15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019

ER -

Chng CK, Ding E, Liu J, Karatzas D, Chan CS, Jin L et al. ICDAR2019 robust reading challenge on arbitrary-shaped text-RRC-ArT. In Proceedings - 15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019. 2019. pàg. 1571-1576. 8978157. (Proceedings of the International Conference on Document Analysis and Recognition, ICDAR). doi: 10.1109/ICDAR.2019.00252

ICDAR2019 robust reading challenge on arbitrary-shaped text-RRC-ArT

Resum

Sèrie de publicacions

Accés al document

Altres arxius i enllaços

Fingerprint

Com citar-ho