PT Unknown
AU N.Nayef
   F.Yin
   I.Bizid
   H.Choi
   Y.Feng
   Dimosthenis Karatzas
   Z.Luo
   Umapada Pal
   Christophe Rigaud
   J. Chazalon
   W.Khlif
   Muhammad Muzzamil Luqman
   Jean-Christophe Burie
   C.L.Liu
   Jean-Marc Ogier
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
PY 2017
BP 1454
EP 1459
DI 10.1109/ICDAR.2017.237
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.
ER