TY - CHAP
T1 - ICDAR2019 robust reading challenge on multi-lingual scene text detection and recognition-RRC-MLT-2019
AU - Nayef, Nibal
AU - Liu, Cheng Lin
AU - Ogier, Jean Marc
AU - Patel, Yash
AU - Busta, Michal
AU - Chowdhury, Pinaki Nath
AU - Karatzas, Dimosthenis
AU - Khlif, Wafa
AU - Matas, Jiri
AU - Pal, Umapada
AU - Burie, Jean Christophe
N1 - Funding Information:
This work is partially funded by Agence Nationale de la Recherche (ANR) in France, National Natural Science Foundation of China (NSFC 61411136002) in China under the AUDINM project, and by the Visual Computing Competence Center TE01020415 of the Technology Agency of the Czech Republic.
Publisher Copyright:
© 2019 IEEE.
PY - 2019/9
Y1 - 2019/9
N2 - With the growing cosmopolitan culture of modern cities, the need of robust Multi-Lingual scene Text (MLT) detection and recognition systems has never been more immense. With the goal to systematically benchmark and push the state-of-the-art forward, the proposed competition builds on top of the RRC-MLT-2017 with an additional end-to-end task, an additional language in the real images dataset, a large scale multi-lingual synthetic dataset to assist the training, and a baseline End-to-End recognition method. The real dataset consists of 20,000 images containing text from 10 languages. The challenge has 4 tasks covering various aspects of multi-lingual scene text: (a) text detection, (b) cropped word script classification, (c) joint text detection and script classification and (d) end-to-end detection and recognition. In total, the competition received 60 submissions from the research and industrial communities. This paper presents the dataset, the tasks and the findings of the presented RRC-MLT-2019 challenge.
AB - With the growing cosmopolitan culture of modern cities, the need of robust Multi-Lingual scene Text (MLT) detection and recognition systems has never been more immense. With the goal to systematically benchmark and push the state-of-the-art forward, the proposed competition builds on top of the RRC-MLT-2017 with an additional end-to-end task, an additional language in the real images dataset, a large scale multi-lingual synthetic dataset to assist the training, and a baseline End-to-End recognition method. The real dataset consists of 20,000 images containing text from 10 languages. The challenge has 4 tasks covering various aspects of multi-lingual scene text: (a) text detection, (b) cropped word script classification, (c) joint text detection and script classification and (d) end-to-end detection and recognition. In total, the competition received 60 submissions from the research and industrial communities. This paper presents the dataset, the tasks and the findings of the presented RRC-MLT-2019 challenge.
KW - End-to-End Text Recognition
KW - Multi-lingual Text
KW - Scene Text Detection
KW - Script Identification
UR - http://www.scopus.com/inward/record.url?scp=85078291417&partnerID=8YFLogxK
U2 - 10.1109/ICDAR.2019.00254
DO - 10.1109/ICDAR.2019.00254
M3 - Chapter
AN - SCOPUS:85078291417
T3 - Proceedings of the International Conference on Document Analysis and Recognition, ICDAR
SP - 1582
EP - 1587
BT - Proceedings - 15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019
ER -