TY  - CONF
AU  - N.Nayef
AU  - F.Yin
AU  - I.Bizid
AU  - H.Choi
AU  - Y.Feng
AU  - Dimosthenis Karatzas
AU  - Z.Luo
AU  - Umapada Pal
AU  - Christophe Rigaud
AU  - J. Chazalon
AU  - W.Khlif
AU  - Muhammad Muzzamil Luqman
AU  - Jean-Christophe Burie
AU  - C.L.Liu
AU  - Jean-Marc Ogier
A2  - ICDAR
PY  - 2017//
TI  - ICDAR2017 Robust Reading Challenge on Multi-Lingual Scene Text Detection and Script Identification – RRC-MLT
BT  - 14th International Conference on Document Analysis and Recognition
SP  - 1454
EP  - 1459
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.
SN  - 978-1-5386-3586-5
UR  - http://dx.doi.org/10.1109/ICDAR.2017.237
N1  - DAG; 600.121
ID  - N.Nayef2017
ER  -