TY - CHAP
T1 - ICDAR2017 Robust Reading Challenge on Multi-Lingual Scene Text Detection and Script Identification - RRC-MLT
AU - Nayef, Nibal
AU - Yin, Fei
AU - Bizid, Imen
AU - Choi, Hyunsoo
AU - Feng, Yuan
AU - Karatzas, Dimosthenis
AU - Luo, Zhenbo
AU - Pal, Umapada
AU - Rigaud, Christophe
AU - Chazalon, Joseph
AU - Khlif, Wafa
AU - Luqman, Muhammad Muzzamil
AU - Burie, Jean Christophe
AU - Liu, Cheng Lin
AU - Ogier, Jean Marc
N1 - Funding Information:
This work is partially funded by Agence Nationale de la Recherche (ANR) in France and National Natural Science Foundation of China (NSFC 61411136002) in China under the AUDINM project.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - Text detection and recognition in a natural environment are key components of many applications, ranging from business card digitization to shop indexation in a street. This competition aims at assessing the ability of state-of-The-art methods to detect Multi-Lingual Text (MLT) in scene images, such as in contents gathered from the Internet media and in modern cities where multiple cultures live and communicate together. This competition is an extension of the Robust Reading Competition (RRC) which has been held since 2003 both in ICDAR and in an online context. The proposed competition is presented as a new challenge of the RRC. The dataset built for this challenge largely extends the previous RRC editions in many aspects: the multi-lingual text, the size of the dataset, the multi-oriented text, the wide variety of scenes. The dataset is comprised of 18,000 images which contain text belonging to 9 languages. The challenge is comprised of three tasks related to text detection and script classification. We have received a total of 16 participations from the research and industrial communities. This paper presents the dataset, the tasks and the findings of this RRC-MLT challenge.
AB - Text detection and recognition in a natural environment are key components of many applications, ranging from business card digitization to shop indexation in a street. This competition aims at assessing the ability of state-of-The-art methods to detect Multi-Lingual Text (MLT) in scene images, such as in contents gathered from the Internet media and in modern cities where multiple cultures live and communicate together. This competition is an extension of the Robust Reading Competition (RRC) which has been held since 2003 both in ICDAR and in an online context. The proposed competition is presented as a new challenge of the RRC. The dataset built for this challenge largely extends the previous RRC editions in many aspects: the multi-lingual text, the size of the dataset, the multi-oriented text, the wide variety of scenes. The dataset is comprised of 18,000 images which contain text belonging to 9 languages. The challenge is comprised of three tasks related to text detection and script classification. We have received a total of 16 participations from the research and industrial communities. This paper presents the dataset, the tasks and the findings of this RRC-MLT challenge.
KW - Multi-lingual Text
KW - Scene Text Detection
KW - Script Identification
UR - http://www.scopus.com/inward/record.url?scp=85045212801&partnerID=8YFLogxK
U2 - 10.1109/ICDAR.2017.237
DO - 10.1109/ICDAR.2017.237
M3 - Chapter
AN - SCOPUS:85045212801
T3 - Proceedings of the International Conference on Document Analysis and Recognition, ICDAR
SP - 1454
EP - 1459
BT - Proceedings - 14th IAPR International Conference on Document Analysis and Recognition, ICDAR 2017
ER -